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Type 'q()' to quit R. > library(virtualspecies) Loading required package: terra terra 1.8.70 > a <- matrix(rep(dnorm(1:100, 50, sd = 25)), + nrow = 100, ncol = 100, byrow = TRUE) > env <- c(rast(a * dnorm(1:100, 50, sd = 25)), + rast(a * 1:100)) > names(env) <- c("variable1", "variable2") > plot(env) # Illustration of the variables > > # Easy creation of the parameter list: > # see in real time the shape of the response functions > parameters <- formatFunctions(variable1 = c(fun = 'dnorm', mean = 1e-04, + sd = 1e-04), + variable2 = c(fun = 'linearFun', a = 1, b = 0)) > > plotResponse(x = env, parameters = parameters, + approach = "response") No default value was defined for rescale.each.response, setting rescale.each.response = TRUE No default value was defined for rescale.each.response, setting rescale.each.response = TRUE > > sp1 <- generateSpFromFun(env, parameters, plot = TRUE) Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE > > # If you provide env, then you can see the shape of response functions: > parameters <- formatFunctions(x = env, + variable1 = c(fun = 'dnorm', mean = 1e-04, + sd = 1e-04), + variable2 = c(fun = 'linearFun', a = 1, b = 0)) No default value was defined for rescale.each.response, setting rescale.each.response = TRUE No default value was defined for rescale.each.response, setting rescale.each.response = TRUE > > sp1 <- generateSpFromFun(env, parameters, plot = TRUE) Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE > sp1 <- generateSpFromFun(env, parameters, plot = TRUE, + formula = NULL, + species.type = "additive") Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE > sp1 <- generateSpFromFun(env, parameters, plot = TRUE, + formula = NULL, + species.type = "multiplicative") Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE > sp1 <- generateSpFromFun(env, parameters, plot = TRUE, + formula = NULL, + species.type = "multiplicative", + rescale = FALSE) Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE > sp1 <- generateSpFromFun(env, parameters, plot = TRUE, + formula = NULL, + species.type = "multiplicative", + rescale = FALSE, + rescale.each.response = FALSE) Generating virtual species environmental suitability... > sp1 <- generateSpFromFun(env, parameters, plot = TRUE, + formula = "variable1 + variable2", + species.type = "multiplicative", + rescale = FALSE, + rescale.each.response = TRUE) Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE [1] "variable1 + variable2" > sp1 <- generateSpFromFun(env, parameters, plot = TRUE, + formula = "sqrt(variable1) + variable2 + 2 * variable2^2 + variable2^3", + species.type = "multiplicative", + rescale = FALSE, + rescale.each.response = TRUE) Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE [1] "sqrt(variable1) + variable2 + 2 * variable2^2 + variable2^3" > sp1 <- generateSpFromFun(env, parameters, plot = TRUE, + formula = "sqrt(variable1) + variable2 + 2 * variable2^2 + variable2^3", + species.type = "multiplicative", + rescale = TRUE, + rescale.each.response = TRUE) Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE [1] "sqrt(variable1) + variable2 + 2 * variable2^2 + variable2^3" > > > sp2 <- convertToPA(sp1, + PA.method = "threshold") --- Generating a random value of beta for the threshold conversion Threshold conversion finished: - cutoff = 0.379379379379379 - species prevalence =0.1526 > sp2 <- convertToPA(sp1, + PA.method = "threshold", + beta = 0.5) Threshold conversion finished: - cutoff = 0.5 - species prevalence =0.1023 > sp2 <- convertToPA(sp1, + PA.method = "threshold", + beta = 0) Threshold conversion finished: - cutoff = 0 - species prevalence =0.9999 > sp2 <- convertToPA(sp1, + PA.method = "threshold", + beta = 1) Threshold conversion finished: - cutoff = 1 - species prevalence =0 > sp2 <- convertToPA(sp1, + PA.method = "threshold", + beta = "random") --- Generating a random value of beta for the threshold conversion Threshold conversion finished: - cutoff = 0.861861861861862 - species prevalence =0.0128 > sp2 <- convertToPA(sp1, + PA.method = "threshold", + species.prevalence = .2) Threshold conversion finished: - cutoff = 0.298037629032326 - species prevalence =0.2 > sp2 <- convertToPA(sp1, + PA.method = "threshold", + species.prevalence = .02) Threshold conversion finished: - cutoff = 0.8172017320348 - species prevalence =0.0199 > sp2 <- convertToPA(sp1, + PA.method = "threshold", + species.prevalence = .9) Threshold conversion finished: - cutoff = 0.0470198966430044 - species prevalence =0.9 > sp2 <- convertToPA(sp1, + PA.method = "threshold", + species.prevalence = .99) Threshold conversion finished: - cutoff = 0.00959072698507723 - species prevalence =0.9899 > sp2 <- convertToPA(sp1, + PA.method = "probability", + prob.method = "logistic", + beta = "random", + a = NULL, + b = NULL, + species.prevalence = .99) --- Determing beta automatically according to alpha and species.prevalence Logistic conversion finished: - beta = -0.1328125 - alpha = -0.05 - species prevalence =0.99 > sp2 <- convertToPA(sp1, + PA.method = "probability", + prob.method = "logistic", + beta = "random", + a = NULL, + b = NULL, + species.prevalence = .5) --- Determing beta automatically according to alpha and species.prevalence Logistic conversion finished: - beta = 0.148818969726562 - alpha = -0.05 - species prevalence =0.5034 > sp2 <- convertToPA(sp1, + PA.method = "probability", + prob.method = "logistic", + beta = .5, + a = NULL, + b = NULL) --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.5 - alpha = -0.05 - species prevalence =0.1091 > > > sp3 <- generateRandomSp(env) - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.342342342342342 - alpha = -0.1 - species prevalence =0.3816 > > a <- matrix(rep(dnorm(1:100, 50, sd = 25)), + nrow = 100, ncol = 100, byrow = TRUE) > > env1 <- c(rast(a * dnorm(1:100, 50, sd = 25)), + rast(a * 1:100), + rast(a), + rast(t(a))) > names(env1) <- c("var1", "var2", "var3", "var4") > b <- matrix(rep(dnorm(1:100, 25, sd = 50)), + nrow = 100, ncol = 100, byrow = TRUE) > > env2 <- c(rast(b * dnorm(1:100, 50, sd = 25)), + rast(b * 1:100), + rast(b), + rast(t(b))) > > names(env2) <- c("var1", "var2", "var3", "var4") > > # Generating a species with the BCA > sp4 <- generateSpFromBCA(raster.stack.current = env1, raster.stack.future = env2) Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the between component analysis - Defining the response of the species along the axis - Calculating current suitability values - Calculating future suitability values The final environmental suitability was rescaled between 0 and1. To disable, set argument rescale = FALSE - Ploting response and suitability > plotResponse(sp4) > > sp4 <- convertToPA(sp4) --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.507368771834419 - alpha = -0.05 - species prevalence =0.136 > plotSuitabilityToProba(sp4) > > sp4 <- convertToPA(sp4, + PA.method = "threshold") --- Generating a random value of beta for the threshold conversion Threshold conversion finished: - cutoff = 0.358260395102015 - species prevalence =0.183 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "threshold", + beta = 0.5) Threshold conversion finished: - cutoff = 0.5 - species prevalence =0.1385 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "threshold", + beta = 0) Threshold conversion finished: - cutoff = 0 - species prevalence =0.9999 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "threshold", + beta = 1) Threshold conversion finished: - cutoff = 1 - species prevalence =0 Warning message: In convertToPA(sp4, PA.method = "threshold", beta = 1) : beta is higher than all values in your suitability raster. The species will most likely be absent everywhere > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "threshold", + beta = "random") --- Generating a random value of beta for the threshold conversion Threshold conversion finished: - cutoff = 0.00700509152434108 - species prevalence =0.39 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "threshold", + species.prevalence = .2) Threshold conversion finished: - cutoff = 0.302307904831028 - species prevalence =0.1999 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "threshold", + species.prevalence = .02) Threshold conversion finished: - cutoff = 0.931631852841673 - species prevalence =0.0199 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "threshold", + species.prevalence = .9) Threshold conversion finished: - cutoff = 2.33781262301761e-24 - species prevalence =0.9 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "threshold", + species.prevalence = .99) Threshold conversion finished: - cutoff = 5.81207372978603e-41 - species prevalence =0.99 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "probability", + prob.method = "logistic", + beta = "random", + a = NULL, + b = NULL, + species.prevalence = .99) --- Determing beta automatically according to alpha and species.prevalence Logistic conversion finished: - beta = -0.210879836703183 - alpha = -0.05 - species prevalence =0.9888 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "probability", + prob.method = "logistic", + beta = "random", + a = NULL, + b = NULL, + species.prevalence = .5) --- Determing beta automatically according to alpha and species.prevalence Logistic conversion finished: - beta = 0.0449095948534556 - alpha = -0.05 - species prevalence =0.4964 > plotSuitabilityToProba(sp4) > sp4 <- convertToPA(sp4, + PA.method = "probability", + prob.method = "logistic", + beta = .5, + a = NULL, + b = NULL) --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.5 - alpha = -0.05 - species prevalence =0.1393 > plotSuitabilityToProba(sp4) > > > for(i in 1:100){ + sp5 <- generateRandomSp(env) + } - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.994994994994995 - alpha = -0.1 - species prevalence =0.0194 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.247247247247247 - alpha = -0.1 - species prevalence =0.576 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.129129129129129 - alpha = -0.1 - species prevalence =0.9119 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.498498498498498 - alpha = -0.1 - species prevalence =0.2112 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.651651651651652 - alpha = -0.1 - species prevalence =0.2441 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.75975975975976 - alpha = -0.1 - species prevalence =0.3864 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.773773773773774 - alpha = -0.1 - species prevalence =0.1631 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0910910910910911 - alpha = -0.1 - species prevalence =0.3224 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.473473473473473 - alpha = -0.1 - species prevalence =0.212 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.944944944944945 - alpha = -0.1 - species prevalence =0.0442 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.767767767767768 - alpha = -0.1 - species prevalence =0.0866 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.797797797797798 - alpha = -0.1 - species prevalence =0.0404 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.241241241241241 - alpha = -0.1 - species prevalence =0.2183 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.872872872872873 - alpha = -0.1 - species prevalence =0.0451 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.48948948948949 - alpha = -0.1 - species prevalence =0.2146 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.874874874874875 - alpha = -0.1 - species prevalence =0.0667 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.900900900900901 - alpha = -0.1 - species prevalence =0.0647 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0820820820820821 - alpha = -0.1 - species prevalence =0.8375 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.75975975975976 - alpha = -0.1 - species prevalence =0.6392 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.766766766766767 - alpha = -0.1 - species prevalence =0.1977 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.66966966966967 - alpha = -0.1 - species prevalence =0.4219 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0620620620620621 - alpha = -0.1 - species prevalence =0.8058 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.27027027027027 - alpha = -0.1 - species prevalence =0.4254 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.681681681681682 - alpha = -0.1 - species prevalence =0.4289 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.147147147147147 - alpha = -0.1 - species prevalence =0.6955 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.685685685685686 - alpha = -0.1 - species prevalence =0.1426 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.001001001001001 - alpha = -0.1 - species prevalence =0.6106 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.640640640640641 - alpha = -0.1 - species prevalence =0.176 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.413413413413413 - alpha = -0.1 - species prevalence =0.3053 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.95995995995996 - alpha = -0.1 - species prevalence =0.0938 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.96996996996997 - alpha = -0.1 - species prevalence =0.0279 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.254254254254254 - alpha = -0.1 - species prevalence =0.3974 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.038038038038038 - alpha = -0.1 - species prevalence =0.932 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.476476476476476 - alpha = -0.1 - species prevalence =0.1398 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.242242242242242 - alpha = -0.1 - species prevalence =0.5551 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.684684684684685 - alpha = -0.1 - species prevalence =0.0351 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.76976976976977 - alpha = -0.1 - species prevalence =0.0234 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.794794794794795 - alpha = -0.1 - species prevalence =0.0092 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.628628628628629 - alpha = -0.1 - species prevalence =0.5888 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.964964964964965 - alpha = -0.1 - species prevalence =0.0218 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.834834834834835 - alpha = -0.1 - species prevalence =0.0504 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.327327327327327 - alpha = -0.1 - species prevalence =0.5361 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.491491491491491 - alpha = -0.1 - species prevalence =0.7765 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.542542542542543 - alpha = -0.1 - species prevalence =0.1864 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.046046046046046 - alpha = -0.1 - species prevalence =0.8608 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.287287287287287 - alpha = -0.1 - species prevalence =0.5269 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.522522522522523 - alpha = -0.1 - species prevalence =0.3378 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.318318318318318 - alpha = -0.1 - species prevalence =0.487 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.636636636636637 - alpha = -0.1 - species prevalence =0.125 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.294294294294294 - alpha = -0.1 - species prevalence =0.4779 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.358358358358358 - alpha = -0.1 - species prevalence =0.6201 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.019019019019019 - alpha = -0.1 - species prevalence =0.7895 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.365365365365365 - alpha = -0.1 - species prevalence =0.2161 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.367367367367367 - alpha = -0.1 - species prevalence =0.6904 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.826826826826827 - alpha = -0.1 - species prevalence =0.1165 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.152152152152152 - alpha = -0.1 - species prevalence =0.6063 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.368368368368368 - alpha = -0.1 - species prevalence =0.0591 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.662662662662663 - alpha = -0.1 - species prevalence =0.2328 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.911911911911912 - alpha = -0.1 - species prevalence =0.1822 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.433433433433433 - alpha = -0.1 - species prevalence =0.4315 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.522522522522523 - alpha = -0.1 - species prevalence =0.0784 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.841841841841842 - alpha = -0.1 - species prevalence =0.1565 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.878878878878879 - alpha = -0.1 - species prevalence =0.0542 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.607607607607608 - alpha = -0.1 - species prevalence =0.1543 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.562562562562563 - alpha = -0.1 - species prevalence =0.5556 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.29029029029029 - alpha = -0.1 - species prevalence =0.2977 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.515515515515516 - alpha = -0.1 - species prevalence =0.1222 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.55955955955956 - alpha = -0.1 - species prevalence =0.16 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.641641641641642 - alpha = -0.1 - species prevalence =0.4402 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.911911911911912 - alpha = -0.1 - species prevalence =0.2144 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.418418418418418 - alpha = -0.1 - species prevalence =0.2 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.166166166166166 - alpha = -0.1 - species prevalence =0.5196 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.593593593593594 - alpha = -0.1 - species prevalence =0.276 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.026026026026026 - alpha = -0.1 - species prevalence =0.5284 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.817817817817818 - alpha = -0.1 - species prevalence =0.0248 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.461461461461461 - alpha = -0.1 - species prevalence =0.8706 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.00600600600600601 - alpha = -0.1 - species prevalence =0.7713 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.265265265265265 - alpha = -0.1 - species prevalence =0.0888 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.151151151151151 - alpha = -0.1 - species prevalence =0.7662 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.581581581581582 - alpha = -0.1 - species prevalence =0.4804 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.756756756756757 - alpha = -0.1 - species prevalence =0.038 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.016016016016016 - alpha = -0.1 - species prevalence =0.829 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.359359359359359 - alpha = -0.1 - species prevalence =0.727 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.988988988988989 - alpha = -0.1 - species prevalence =0.0726 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.48048048048048 - alpha = -0.1 - species prevalence =0.0596 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.882882882882883 - alpha = -0.1 - species prevalence =0.048 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.196196196196196 - alpha = -0.1 - species prevalence =0.8549 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.476476476476476 - alpha = -0.1 - species prevalence =0.3155 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.578578578578579 - alpha = -0.1 - species prevalence =0.3276 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.617617617617618 - alpha = -0.1 - species prevalence =0.1579 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.461461461461461 - alpha = -0.1 - species prevalence =0.2052 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.616616616616617 - alpha = -0.1 - species prevalence =0.1867 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.183183183183183 - alpha = -0.1 - species prevalence =0.5043 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.583583583583584 - alpha = -0.1 - species prevalence =0.4562 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.967967967967968 - alpha = -0.1 - species prevalence =0.0131 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.262262262262262 - alpha = -0.1 - species prevalence =0.8623 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.219219219219219 - alpha = -0.1 - species prevalence =0.9155 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.484484484484485 - alpha = -0.1 - species prevalence =0.2044 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0770770770770771 - alpha = -0.1 - species prevalence =0.8912 - Determining species' response to predictor variables - Calculating species suitability Generating virtual species environmental suitability... - The response to each variable was rescaled between 0 and 1. To disable, set argument rescale.each.response = FALSE - The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.428428428428428 - alpha = -0.1 - species prevalence =0.1579 > > env <- c(rast(a * dnorm(1:100, 50, sd = 25)), + rast(a * 1:100), + rast(a * logisticFun(1:100, alpha = 10, beta = 70)), + rast(t(a)), + rast(exp(a)), + rast(log(a))) > names(env) <- paste("Var", 1:6, sep = "") > > for(i in 1:100){ + sp5 <- generateRandomSp(env) + } Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.531531531531532 - alpha = -0.1 - species prevalence =0.3574 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.509509509509509 - alpha = -0.1 - species prevalence =0.0795 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.617617617617618 - alpha = -0.1 - species prevalence =0.4153 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.682682682682683 - alpha = -0.1 - species prevalence =0.2156 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.683683683683684 - alpha = -0.1 - species prevalence =0.1015 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.519519519519519 - alpha = -0.1 - species prevalence =0.0416 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.476476476476476 - alpha = -0.1 - species prevalence =0.0159 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.442442442442442 - alpha = -0.1 - species prevalence =0.1227 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.784784784784785 - alpha = -0.1 - species prevalence =0.0099 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.863863863863864 - alpha = -0.1 - species prevalence =0.0903 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.495495495495495 - alpha = -0.1 - species prevalence =0.1165 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.041041041041041 - alpha = -0.1 - species prevalence =0.8008 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.883883883883884 - alpha = -0.1 - species prevalence =0.186 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.765765765765766 - alpha = -0.1 - species prevalence =0.0118 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.252252252252252 - alpha = -0.1 - species prevalence =0.1144 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.025025025025025 - alpha = -0.1 - species prevalence =0.4983 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.645645645645646 - alpha = -0.1 - species prevalence =0.039 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.295295295295295 - alpha = -0.1 - species prevalence =0.4599 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.795795795795796 - alpha = -0.1 - species prevalence =0.0231 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.941941941941942 - alpha = -0.1 - species prevalence =0.009 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.263263263263263 - alpha = -0.1 - species prevalence =0.1242 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.366366366366366 - alpha = -0.1 - species prevalence =0.7259 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.867867867867868 - alpha = -0.1 - species prevalence =0.2536 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.71971971971972 - alpha = -0.1 - species prevalence =0.0877 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.033033033033033 - alpha = -0.1 - species prevalence =0.8858 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.974974974974975 - alpha = -0.1 - species prevalence =0.0125 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0570570570570571 - alpha = -0.1 - species prevalence =0.7544 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.500500500500501 - alpha = -0.1 - species prevalence =0.4912 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0990990990990991 - alpha = -0.1 - species prevalence =0.9484 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.113113113113113 - alpha = -0.1 - species prevalence =0.4904 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.623623623623624 - alpha = -0.1 - species prevalence =0.2011 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.381381381381381 - alpha = -0.1 - species prevalence =0.1299 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.882882882882883 - alpha = -0.1 - species prevalence =0.1963 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.556556556556557 - alpha = -0.1 - species prevalence =0.3444 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.258258258258258 - alpha = -0.1 - species prevalence =0.8753 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.540540540540541 - alpha = -0.1 - species prevalence =0.3333 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.888888888888889 - alpha = -0.1 - species prevalence =0.0235 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.504504504504504 - alpha = -0.1 - species prevalence =0.4821 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.371371371371371 - alpha = -0.1 - species prevalence =0.0376 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.917917917917918 - alpha = -0.1 - species prevalence =0.0792 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.343343343343343 - alpha = -0.1 - species prevalence =0.2127 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.45045045045045 - alpha = -0.1 - species prevalence =0.6218 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.788788788788789 - alpha = -0.1 - species prevalence =0.2371 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.286286286286286 - alpha = -0.1 - species prevalence =0.7565 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.864864864864865 - alpha = -0.1 - species prevalence =0.0087 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.690690690690691 - alpha = -0.1 - species prevalence =0.095 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0980980980980981 - alpha = -0.1 - species prevalence =0.7295 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.98998998998999 - alpha = -0.1 - species prevalence =0.0177 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.828828828828829 - alpha = -0.1 - species prevalence =0.0171 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.193193193193193 - alpha = -0.1 - species prevalence =0.8604 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.866866866866867 - alpha = -0.1 - species prevalence =0.0426 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.168168168168168 - alpha = -0.1 - species prevalence =0.3243 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.777777777777778 - alpha = -0.1 - species prevalence =0.1576 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.810810810810811 - alpha = -0.1 - species prevalence =0.104 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.168168168168168 - alpha = -0.1 - species prevalence =0.4477 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.643643643643644 - alpha = -0.1 - species prevalence =0.1818 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.024024024024024 - alpha = -0.1 - species prevalence =0.4932 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.594594594594595 - alpha = -0.1 - species prevalence =0.1696 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.133133133133133 - alpha = -0.1 - species prevalence =0.2229 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.996996996996997 - alpha = -0.1 - species prevalence =0.0442 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.976976976976977 - alpha = -0.1 - species prevalence =0.0368 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.303303303303303 - alpha = -0.1 - species prevalence =0.1487 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.168168168168168 - alpha = -0.1 - species prevalence =0.4166 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.654654654654655 - alpha = -0.1 - species prevalence =0.1367 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0860860860860861 - alpha = -0.1 - species prevalence =0.6492 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.022022022022022 - alpha = -0.1 - species prevalence =0.5414 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0770770770770771 - alpha = -0.1 - species prevalence =0.3618 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.895895895895896 - alpha = -0.1 - species prevalence =0.1395 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.131131131131131 - alpha = -0.1 - species prevalence =0.5372 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.329329329329329 - alpha = -0.1 - species prevalence =0.5593 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.352352352352352 - alpha = -0.1 - species prevalence =0.104 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.409409409409409 - alpha = -0.1 - species prevalence =0.4253 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.791791791791792 - alpha = -0.1 - species prevalence =0.0028 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.55955955955956 - alpha = -0.1 - species prevalence =0.1302 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.198198198198198 - alpha = -0.1 - species prevalence =0.4019 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.346346346346346 - alpha = -0.1 - species prevalence =0.5109 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.047047047047047 - alpha = -0.1 - species prevalence =0.9018 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.0960960960960961 - alpha = -0.1 - species prevalence =0.3731 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.931931931931932 - alpha = -0.1 - species prevalence =0.0412 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.923923923923924 - alpha = -0.1 - species prevalence =0.1588 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.204204204204204 - alpha = -0.1 - species prevalence =0.2265 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.594594594594595 - alpha = -0.1 - species prevalence =0.3453 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.918918918918919 - alpha = -0.1 - species prevalence =0.0667 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.394394394394394 - alpha = -0.1 - species prevalence =0.1314 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.511511511511512 - alpha = -0.1 - species prevalence =0.0252 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.615615615615616 - alpha = -0.1 - species prevalence =0.3042 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.673673673673674 - alpha = -0.1 - species prevalence =0.0109 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.95995995995996 - alpha = -0.1 - species prevalence =0.0031 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.867867867867868 - alpha = -0.1 - species prevalence =0.0236 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.498498498498498 - alpha = -0.1 - species prevalence =0.4663 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.978978978978979 - alpha = -0.1 - species prevalence =0.0925 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.227227227227227 - alpha = -0.1 - species prevalence =0.5471 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.88988988988989 - alpha = -0.1 - species prevalence =0.0013 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.625625625625626 - alpha = -0.1 - species prevalence =0.0982 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.36036036036036 - alpha = -0.1 - species prevalence =0.2955 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.134134134134134 - alpha = -0.1 - species prevalence =0.5575 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.890890890890891 - alpha = -0.1 - species prevalence =0.0476 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.818818818818819 - alpha = -0.1 - species prevalence =0.1105 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.394394394394394 - alpha = -0.1 - species prevalence =0.3534 Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.119119119119119 - alpha = -0.1 - species prevalence =0.89 > > env <- c(rast(a * dnorm(1:100, 50, sd = 25)), + rast(a * 1:100), + rast(a * logisticFun(1:100, alpha = 10, beta = 70)), + rast(t(a)), + rast(exp(a)), + rast(log(a))) > names(env) <- paste("Var", 1:6, sep = "") > > sp5 <- generateRandomSp(env) Reading raster values. If it fails for very large rasters, use arguments 'sample.points = TRUE' and define a number of points to sample with 'nb.point'. - Perfoming the pca - Defining the response of the species along PCA axes - Calculating suitability values The final environmental suitability was rescaled between 0 and 1. To disable, set argument rescale = FALSE - Converting into Presence - Absence --- Generating a random value of beta for the logistic conversion --- Determing species.prevalence automatically according to alpha and beta Logistic conversion finished: - beta = 0.132132132132132 - alpha = -0.1 - species prevalence =0.4889 > > samp1 <- sampleOccurrences(sp5, + n = 50) > samp1 <- sampleOccurrences(sp5, + n = 50, + type = "presence-absence") > samp1 <- sampleOccurrences(sp5, + n = 50, + type = "presence-absence", + extract.probability = TRUE) > samp1 <- sampleOccurrences(sp5, + n = 50, + type = "presence-absence", + sample.prevalence = .9, + extract.probability = TRUE) > samp1 <- sampleOccurrences(sp5, + n = 50, + type = "presence-absence", + sample.prevalence = .1, + extract.probability = TRUE) > > # > # > # worldclim <- geodata::worldclim_global(var = "bio", res = 10, path = tempdir()) > # names(worldclim) <- paste0("bio", 1:19) > # > # my.stack <- worldclim[[c("bio2", "bio5", "bio6", "bio12", "bio13", "bio14")]] > # random.sp <- generateSpFromPCA(my.stack, > # axes = 1:3, > # niche.breadth = "narrow") > # > # random.sp <- convertToPA(random.sp) > # > # worldmap <- rnaturalearth::ne_countries(returnclass = "sf") > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50) > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = "Morocco", > # error.probability = 0.1, > # detection.probability = .9) > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = worldmap[worldmap$sovereignt == > # "France", ], > # error.probability = 0.1, > # detection.probability = .9) > # > # > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # error.probability = 0.1, > # detection.probability = .9) > # > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # bias = "extent", > # bias.strength = 50, > # error.probability = 0.1, > # detection.probability = .9) > # > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # bias = "country", > # bias.area = "Egypt", > # bias.strength = 50, > # error.probability = 0.1, > # detection.probability = .9) > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # bias = "region", > # bias.area = "Africa", > # bias.strength = 50, > # error.probability = 0.1, > # detection.probability = .9) > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # bias = "continent", > # bias.area = "Africa", > # bias.strength = 50, > # error.probability = 0.1, > # detection.probability = .9) > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # bias = "polygon", > # bias.area = worldmap[worldmap$sovereignt == > # "Egypt", ], > # bias.strength = 200, > # error.probability = 0.1, > # detection.probability = .9) > # > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # bias = "polygon", > # bias.area = NULL, > # bias.strength = 200, > # error.probability = 0.1, > # detection.probability = .9) > # > # > # samp1 <- sampleOccurrences(random.sp, > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # bias = "extent", > # bias.area = NULL, > # bias.strength = 200, > # error.probability = 0.1, > # detection.probability = .9) > # > # > # samp1 <- sampleOccurrences(random.sp, > # type = "presence-absence", > # n = 50, > # sampling.area = ext(0, 180, 0, 90), > # bias = "extent", > # bias.area = NULL, > # bias.strength = 200, > # error.probability = 0.1, > # detection.probability = .9) > # > # samp1 <- sampleOccurrences(random.sp, > # type = "presence-absence", > # n = 50, > # error.probability = 0.1, > # detection.probability = .9, > # correct.by.suitability = TRUE) > # > # > # samp1 <- sampleOccurrences(random.sp, > # type = "presence-absence", > # n = 50, > # error.probability = 0.1, > # detection.probability = .9, > # correct.by.suitability = TRUE, > # bias = "manual", > # weights = exp(random.sp$suitab.raster)) > > proc.time() user system elapsed 124.34 2.34 126.67