R Under development (unstable) (2024-12-01 r87412 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > if (requireNamespace("tinytest", quietly = TRUE)) { + tinytest::test_package("douconca") + } test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 0 tests test_anova.dcca.R............. 1 tests OK test_anova.dcca.R............. 1 tests OK test_anova.dcca.R............. 2 tests OK test_anova.dcca.R............. 3 tests OK test_anova.dcca.R............. 3 tests OK test_anova.dcca.R............. 3 tests OK test_anova.dcca.R............. 3 tests OK test_anova.dcca.R............. 4 tests OK test_anova.dcca.R............. 4 tests OK test_anova.dcca.R............. 5 tests OK test_anova.dcca.R............. 5 tests OK test_anova.dcca.R............. 5 tests OK test_anova.dcca.R............. 6 tests OK Error in solve.default(qr.R(msqr$qrX)) : system is computationally singular: reciprocal condition number = 6.85932e-17 test_anova.dcca.R............. 7 tests OK test_anova.dcca.R............. 7 tests OK test_anova.dcca.R............. 7 tests OK test_anova.dcca.R............. 7 tests OK Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'A11' test_anova.dcca.R............. 7 tests OK test_anova.dcca.R............. 8 tests OK test_anova.dcca.R............. 8 tests OK Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'A11' test_anova.dcca.R............. 9 tests OK The model is overfitted with no unconstrained (residual) component test_anova.dcca.R............. 9 tests OK test_anova.dcca.R............. 9 tests OK Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'A11' test_anova.dcca.R............. 9 tests OK test_anova.dcca.R............. 10 tests OK test_anova.dcca.R............. 10 tests OK test_anova.dcca.R............. 10 tests OK test_anova.dcca.R............. 10 tests OK test_anova.dcca.R............. 10 tests OK test_anova.dcca.R............. 11 tests OK 3.0s test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 0 tests test_anova.dccav.R............ 1 tests OK test_anova.dccav.R............ 1 tests OK test_anova.dccav.R............ 2 tests OK test_anova.dccav.R............ 3 tests OK test_anova.dccav.R............ 3 tests OK test_anova.dccav.R............ 3 tests OK test_anova.dccav.R............ 3 tests OK test_anova.dccav.R............ 4 tests OK test_anova.dccav.R............ 4 tests OK test_anova.dccav.R............ 5 tests OK test_anova.dccav.R............ 5 tests OK test_anova.dccav.R............ 5 tests OK test_anova.dccav.R............ 6 tests OK Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'A11' test_anova.dccav.R............ 7 tests OK test_anova.dccav.R............ 7 tests OK test_anova.dccav.R............ 7 tests OK test_anova.dccav.R............ 7 tests OK Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'A11' test_anova.dccav.R............ 7 tests OK test_anova.dccav.R............ 8 tests OK test_anova.dccav.R............ 8 tests OK Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'A11' test_anova.dccav.R............ 9 tests OK The model is overfitted with no unconstrained (residual) component test_anova.dccav.R............ 9 tests OK test_anova.dccav.R............ 9 tests OK Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'A11' test_anova.dccav.R............ 9 tests OK test_anova.dccav.R............ 10 tests OK test_anova.dccav.R............ 10 tests OK test_anova.dccav.R............ 10 tests OK test_anova.dccav.R............ 10 tests OK test_anova.dccav.R............ 10 tests OK test_anova.dccav.R............ 11 tests OK 1.9s test_dcca.R................... 0 tests test_dcca.R................... 0 tests test_dcca.R................... 0 tests test_dcca.R................... 0 tests test_dcca.R................... 0 tests test_dcca.R................... 0 tests test_dcca.R................... 0 tests test_dcca.R................... 0 tests test_dcca.R................... 1 tests OK test_dcca.R................... 1 tests OK test_dcca.R................... 2 tests OK test_dcca.R................... 3 tests OK test_dcca.R................... 3 tests OK test_dcca.R................... 3 tests OK test_dcca.R................... 3 tests OK test_dcca.R................... 4 tests OK test_dcca.R................... 4 tests OK test_dcca.R................... 5 tests OK test_dcca.R................... 5 tests OK test_dcca.R................... 5 tests OK test_dcca.R................... 5 tests OK test_dcca.R................... 5 tests OK test_dcca.R................... 6 tests OK test_dcca.R................... 6 tests OK test_dcca.R................... 6 tests OK test_dcca.R................... 7 tests OK test_dcca.R................... 8 tests OK test_dcca.R................... 9 tests OK test_dcca.R................... 9 tests OK test_dcca.R................... 9 tests OK test_dcca.R................... 10 tests OK test_dcca.R................... 10 tests OK test_dcca.R................... 10 tests OK test_dcca.R................... 10 tests OK Error in solve.default(qr.R(msqr$qrX)) : system is computationally singular: reciprocal condition number = 4.89592e-17 In addition: Warning message: In f_wmean(object$formulaEnv, tY = t(object$data$Y)/sum(object$data$Y), : singular environment data. Env_explain not available. test_dcca.R................... 12 tests OK test_dcca.R................... 13 tests OK test_dcca.R................... 14 tests OK test_dcca.R................... 15 tests OK test_dcca.R................... 16 tests OK test_dcca.R................... 17 tests OK test_dcca.R................... 18 tests OK test_dcca.R................... 19 tests OK test_dcca.R................... 19 tests OK test_dcca.R................... 19 tests OK test_dcca.R................... 20 tests OK test_dcca.R................... 21 tests OK test_dcca.R................... 22 tests OK test_dcca.R................... 23 tests OK test_dcca.R................... 24 tests OK test_dcca.R................... 25 tests OK test_dcca.R................... 26 tests OK test_dcca.R................... 27 tests OK test_dcca.R................... 28 tests OK test_dcca.R................... 29 tests OK The model is overfitted with no unconstrained (residual) component test_dcca.R................... 30 tests OK test_dcca.R................... 31 tests OK test_dcca.R................... 32 tests OK Error in solve.default(qr.R(msqr$qrX)) : system is computationally singular: reciprocal condition number = 5.67437e-19 test_dcca.R................... 34 tests OK test_dcca.R................... 35 tests OK test_dcca.R................... 37 tests OK test_dcca.R................... 38 tests OK test_dcca.R................... 39 tests OK test_dcca.R................... 40 tests OK test_dcca.R................... 40 tests OK test_dcca.R................... 41 tests OK test_dcca.R................... 43 tests OK test_dcca.R................... 44 tests OK test_dcca.R................... 44 tests OK test_dcca.R................... 44 tests OK test_dcca.R................... 44 tests OK test_dcca.R................... 44 tests OK test_dcca.R................... 44 tests OK test_dcca.R................... 44 tests OK The model is overfitted with no unconstrained (residual) component test_dcca.R................... 44 tests OK test_dcca.R................... 45 tests OK test_dcca.R................... 45 tests OK test_dcca.R................... 45 tests OK test_dcca.R................... 45 tests OK test_dcca.R................... 45 tests OK test_dcca.R................... 45 tests OK test_dcca.R................... 46 tests OK test_dcca.R................... 47 tests OK 3.7s test_dccav.R.................. 0 tests test_dccav.R.................. 0 tests test_dccav.R.................. 0 tests test_dccav.R.................. 0 tests test_dccav.R.................. 0 tests test_dccav.R.................. 0 tests test_dccav.R.................. 0 tests test_dccav.R.................. 0 tests test_dccav.R.................. 1 tests OK test_dccav.R.................. 1 tests OK test_dccav.R.................. 2 tests OK test_dccav.R.................. 3 tests OK test_dccav.R.................. 3 tests OK test_dccav.R.................. 3 tests OK test_dccav.R.................. 3 tests OK test_dccav.R.................. 4 tests OK test_dccav.R.................. 4 tests OK test_dccav.R.................. 5 tests OK test_dccav.R.................. 5 tests OK test_dccav.R.................. 5 tests OK test_dccav.R.................. 5 tests OK test_dccav.R.................. 5 tests OK test_dccav.R.................. 6 tests OK test_dccav.R.................. 6 tests OK test_dccav.R.................. 6 tests OK test_dccav.R.................. 7 tests OK test_dccav.R.................. 8 tests OK test_dccav.R.................. 9 tests OK test_dccav.R.................. 9 tests OK test_dccav.R.................. 9 tests OK test_dccav.R.................. 10 tests OK test_dccav.R.................. 10 tests OK test_dccav.R.................. 10 tests OK test_dccav.R.................. 10 tests OK Error in solve.default(qr.R(msqr$qrX)) : system is computationally singular: reciprocal condition number = 1.87328e-16 test_dccav.R.................. 12 tests OK test_dccav.R.................. 13 tests OK test_dccav.R.................. 14 tests OK test_dccav.R.................. 15 tests OK test_dccav.R.................. 16 tests OK test_dccav.R.................. 17 tests OK test_dccav.R.................. 18 tests OK test_dccav.R.................. 19 tests OK test_dccav.R.................. 19 tests OK test_dccav.R.................. 19 tests OK test_dccav.R.................. 20 tests OK test_dccav.R.................. 21 tests OK test_dccav.R.................. 22 tests OK test_dccav.R.................. 23 tests OK test_dccav.R.................. 24 tests OK test_dccav.R.................. 25 tests OK test_dccav.R.................. 26 tests OK test_dccav.R.................. 27 tests OK The model is overfitted with no unconstrained (residual) component test_dccav.R.................. 28 tests OK test_dccav.R.................. 29 tests OK The model is overfitted with no unconstrained (residual) component test_dccav.R.................. 30 tests OK test_dccav.R.................. 31 tests OK test_dccav.R.................. 32 tests OK Error in solve.default(qr.R(msqr$qrX)) : system is computationally singular: reciprocal condition number = 4.31227e-18 test_dccav.R.................. 34 tests OK test_dccav.R.................. 35 tests OK test_dccav.R.................. 37 tests OK test_dccav.R.................. 38 tests OK test_dccav.R.................. 39 tests OK test_dccav.R.................. 40 tests OK test_dccav.R.................. 40 tests OK test_dccav.R.................. 41 tests OK test_dccav.R.................. 43 tests OK test_dccav.R.................. 44 tests OK test_dccav.R.................. 44 tests OK test_dccav.R.................. 44 tests OK test_dccav.R.................. 44 tests OK test_dccav.R.................. 44 tests OK test_dccav.R.................. 44 tests OK test_dccav.R.................. 44 tests OK test_dccav.R.................. 44 tests OK The model is overfitted with no unconstrained (residual) component test_dccav.R.................. 44 tests OK test_dccav.R.................. 45 tests OK test_dccav.R.................. 45 tests OK test_dccav.R.................. 45 tests OK test_dccav.R.................. 45 tests OK test_dccav.R.................. 45 tests OK test_dccav.R.................. 45 tests OK test_dccav.R.................. 46 tests OK test_dccav.R.................. 47 tests OK 2.7s test_fCWMSNC.R................ 0 tests test_fCWMSNC.R................ 0 tests test_fCWMSNC.R................ 0 tests test_fCWMSNC.R................ 0 tests test_fCWMSNC.R................ 0 tests test_fCWMSNC.R................ 0 tests test_fCWMSNC.R................ 0 tests test_fCWMSNC.R................ 0 tests test_fCWMSNC.R................ 1 tests OK test_fCWMSNC.R................ 2 tests OK test_fCWMSNC.R................ 2 tests OK test_fCWMSNC.R................ 2 tests OK test_fCWMSNC.R................ 3 tests OK test_fCWMSNC.R................ 3 tests OK test_fCWMSNC.R................ 4 tests OK test_fCWMSNC.R................ 5 tests OK test_fCWMSNC.R................ 5 tests OK test_fCWMSNC.R................ 6 tests OK test_fCWMSNC.R................ 7 tests OK test_fCWMSNC.R................ 7 tests OK test_fCWMSNC.R................ 7 tests OK test_fCWMSNC.R................ 7 tests OK test_fCWMSNC.R................ 8 tests OK test_fCWMSNC.R................ 9 tests OK test_fCWMSNC.R................ 10 tests OK test_fCWMSNC.R................ 10 tests OK test_fCWMSNC.R................ 10 tests OK test_fCWMSNC.R................ 11 tests OK test_fCWMSNC.R................ 12 tests OK test_fCWMSNC.R................ 13 tests OK test_fCWMSNC.R................ 13 tests OK test_fCWMSNC.R................ 13 tests OK test_fCWMSNC.R................ 14 tests OK test_fCWMSNC.R................ 15 tests OK test_fCWMSNC.R................ 16 tests OK test_fCWMSNC.R................ 16 tests OK test_fCWMSNC.R................ 16 tests OK test_fCWMSNC.R................ 16 tests OK test_fCWMSNC.R................ 16 tests OK test_fCWMSNC.R................ 16 tests OK test_fCWMSNC.R................ 16 tests OK test_fCWMSNC.R................ 17 tests OK test_fCWMSNC.R................ 18 tests OK test_fCWMSNC.R................ 18 tests OK test_fCWMSNC.R................ 19 tests OK test_fCWMSNC.R................ 19 tests OK test_fCWMSNC.R................ 19 tests OK Error in if (is.na(env_explain)) env_explain <- NULL : argument is of length zero [1] "Error in if (is.na(env_explain)) env_explain <- NULL : \n argument is of length zero\n" attr(,"class") [1] "try-error" attr(,"condition") test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 20 tests OK test_fCWMSNC.R................ 21 tests OK test_fCWMSNC.R................ 22 tests OK test_fCWMSNC.R................ 23 tests OK test_fCWMSNC.R................ 23 tests OK test_fCWMSNC.R................ 23 tests OK test_fCWMSNC.R................ 23 tests OK test_fCWMSNC.R................ 24 tests OK test_fCWMSNC.R................ 24 tests OK test_fCWMSNC.R................ 24 tests OK test_fCWMSNC.R................ 25 tests OK test_fCWMSNC.R................ 25 tests OK test_fCWMSNC.R................ 25 tests OK test_fCWMSNC.R................ 26 tests OK 0.5s test_fCWMSNCv.R............... 0 tests test_fCWMSNCv.R............... 0 tests test_fCWMSNCv.R............... 0 tests test_fCWMSNCv.R............... 0 tests test_fCWMSNCv.R............... 0 tests test_fCWMSNCv.R............... 0 tests test_fCWMSNCv.R............... 0 tests test_fCWMSNCv.R............... 0 tests test_fCWMSNCv.R............... 1 tests OK test_fCWMSNCv.R............... 2 tests OK test_fCWMSNCv.R............... 2 tests OK test_fCWMSNCv.R............... 2 tests OK test_fCWMSNCv.R............... 3 tests OK test_fCWMSNCv.R............... 3 tests OK test_fCWMSNCv.R............... 4 tests OK test_fCWMSNCv.R............... 5 tests OK test_fCWMSNCv.R............... 5 tests OK test_fCWMSNCv.R............... 6 tests OK test_fCWMSNCv.R............... 7 tests OK test_fCWMSNCv.R............... 7 tests OK test_fCWMSNCv.R............... 7 tests OK test_fCWMSNCv.R............... 7 tests OK test_fCWMSNCv.R............... 8 tests OK test_fCWMSNCv.R............... 9 tests OK test_fCWMSNCv.R............... 10 tests OK test_fCWMSNCv.R............... 10 tests OK test_fCWMSNCv.R............... 10 tests OK test_fCWMSNCv.R............... 11 tests OK test_fCWMSNCv.R............... 12 tests OK test_fCWMSNCv.R............... 12 tests OK test_fCWMSNCv.R............... 12 tests OK test_fCWMSNCv.R............... 13 tests OK test_fCWMSNCv.R............... 14 tests OK test_fCWMSNCv.R............... 15 tests OK test_fCWMSNCv.R............... 15 tests OK test_fCWMSNCv.R............... 15 tests OK test_fCWMSNCv.R............... 15 tests OK test_fCWMSNCv.R............... 15 tests OK test_fCWMSNCv.R............... 15 tests OK test_fCWMSNCv.R............... 15 tests OK test_fCWMSNCv.R............... 16 tests OK test_fCWMSNCv.R............... 17 tests OK test_fCWMSNCv.R............... 17 tests OK test_fCWMSNCv.R............... 18 tests OK test_fCWMSNCv.R............... 18 tests OK test_fCWMSNCv.R............... 18 tests OK Error in if (is.na(env_explain)) env_explain <- NULL : argument is of length zero In addition: Warning messages: 1: In set_newdata(object, newdata1, type = "traitsFromEnv", means_mis = attr(reg, : newdata does not contain the predictor variables Moist,Use These are set at their mean values and, for factors, at the reference level The current formula is ~ Mag + A1 + Moist + Manure + Use 2: In set_newdata(object, newdata1, type = "envFromTraits", means_mis = attr(reg, : newdata does not contain the predictor variables Height,Lifespan,Seedmass These are set at their mean values and, for factors, at the reference level The current formula is ~ Seedmass + SLA + Height + LDMC + Lifespan 3: In set_newdata(object, newdata1[[1]], type = "envFromTraits", means_mis = sc[["regression_traits"]][, : newdata does not contain the predictor variables Height,Lifespan,Seedmass These are set at their mean values and, for factors, at the reference level The current formula is ~ Seedmass + SLA + Height + LDMC + Lifespan 4: In set_newdata(object, newdata1[[2]], type = "traitsFromEnv", means_mis = sc[["regression"]][, : newdata does not contain the predictor variables Moist,Use These are set at their mean values and, for factors, at the reference level The current formula is ~ Mag + A1 + Moist + Manure + Use 5: In set_newdata(object, newdata1, type = "traitsFromEnv", means_mis = attr(reg, : newdata does not contain the predictor variables Moist,Use These are set at their mean values and, for factors, at the reference level The current formula is ~ Mag + A1 + Moist + Manure + Use 6: In set_newdata(object, newdata1, type = "envFromTraits", means_mis = attr(reg, : newdata does not contain the predictor variables Height,Lifespan,Seedmass These are set at their mean values and, for factors, at the reference level The current formula is ~ Seedmass + SLA + Height + LDMC + Lifespan 7: In set_newdata(object, newdata1[[1]], type = "envFromTraits", means_mis = sc[["regression_traits"]][, : newdata does not contain the predictor variables Height,Lifespan,Seedmass These are set at their mean values and, for factors, at the reference level The current formula is ~ Seedmass + SLA + Height + LDMC + Lifespan 8: In set_newdata(object, newdata1[[2]], type = "traitsFromEnv", means_mis = sc[["regression"]][, : newdata does not contain the predictor variables Moist,Use These are set at their mean values and, for factors, at the reference level The current formula is ~ Mag + A1 + Moist + Manure + Use [1] "Error in if (is.na(env_explain)) env_explain <- NULL : \n argument is of length zero\n" attr(,"class") [1] "try-error" attr(,"condition") test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 19 tests OK test_fCWMSNCv.R............... 20 tests OK test_fCWMSNCv.R............... 21 tests OK test_fCWMSNCv.R............... 22 tests OK test_fCWMSNCv.R............... 22 tests OK test_fCWMSNCv.R............... 22 tests OK test_fCWMSNCv.R............... 22 tests OK test_fCWMSNCv.R............... 23 tests OK test_fCWMSNCv.R............... 23 tests OK test_fCWMSNCv.R............... 23 tests OK test_fCWMSNCv.R............... 24 tests OK test_fCWMSNCv.R............... 24 tests OK test_fCWMSNCv.R............... 24 tests OK test_fCWMSNCv.R............... 25 tests OK 0.6s test_plot.dcca.R.............. 0 tests test_plot.dcca.R.............. 0 tests test_plot.dcca.R.............. 0 tests test_plot.dcca.R.............. 0 tests test_plot.dcca.R.............. 0 tests test_plot.dcca.R.............. 0 tests test_plot.dcca.R.............. 0 tests test_plot.dcca.R.............. 1 tests OK test_plot.dcca.R.............. 1 tests OK test_plot.dcca.R.............. 2 tests OK test_plot.dcca.R.............. 3 tests OK test_plot.dcca.R.............. 4 tests OK test_plot.dcca.R.............. 5 tests OK test_plot.dcca.R.............. 6 tests OK test_plot.dcca.R.............. 7 tests OK test_plot.dcca.R.............. 8 tests OK test_plot.dcca.R.............. 9 tests OK test_plot.dcca.R.............. 9 tests OK test_plot.dcca.R.............. 10 tests OK test_plot.dcca.R.............. 11 tests OK test_plot.dcca.R.............. 11 tests OK test_plot.dcca.R.............. 11 tests OK test_plot.dcca.R.............. 12 tests OK test_plot.dcca.R.............. 13 tests OK test_plot.dcca.R.............. 14 tests OK test_plot.dcca.R.............. 15 tests OK test_plot.dcca.R.............. 15 tests OK test_plot.dcca.R.............. 15 tests OK test_plot.dcca.R.............. 15 tests OK test_plot.dcca.R.............. 15 tests OK test_plot.dcca.R.............. 15 tests OK 5.5s test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 0 tests test_predict.dcca.R........... 1 tests OK test_predict.dcca.R........... 1 tests OK test_predict.dcca.R........... 1 tests OK test_predict.dcca.R........... 2 tests OK test_predict.dcca.R........... 2 tests OK test_predict.dcca.R........... 2 tests OK test_predict.dcca.R........... 3 tests OK test_predict.dcca.R........... 3 tests OK test_predict.dcca.R........... 3 tests OK test_predict.dcca.R........... 3 tests OK test_predict.dcca.R........... 4 tests OK test_predict.dcca.R........... 4 tests OK test_predict.dcca.R........... 4 tests OK test_predict.dcca.R........... 4 tests OK test_predict.dcca.R........... 4 tests OK test_predict.dcca.R........... 4 tests OK test_predict.dcca.R........... 4 tests OK test_predict.dcca.R........... 5 tests OK test_predict.dcca.R........... 6 tests OK test_predict.dcca.R........... 7 tests OK test_predict.dcca.R........... 8 tests OK test_predict.dcca.R........... 9 tests OK test_predict.dcca.R........... 10 tests OK test_predict.dcca.R........... 11 tests OK test_predict.dcca.R........... 11 tests OK test_predict.dcca.R........... 11 tests OK test_predict.dcca.R........... 11 tests OK test_predict.dcca.R........... 11 tests OK test_predict.dcca.R........... 12 tests OK test_predict.dcca.R........... 12 tests OK test_predict.dcca.R........... 12 tests OK test_predict.dcca.R........... 12 tests OK test_predict.dcca.R........... 13 tests OK test_predict.dcca.R........... 14 tests OK test_predict.dcca.R........... 15 tests OK test_predict.dcca.R........... 16 tests OK test_predict.dcca.R........... 17 tests OK test_predict.dcca.R........... 17 tests OK test_predict.dcca.R........... 17 tests OK test_predict.dcca.R........... 17 tests OK test_predict.dcca.R........... 17 tests OK test_predict.dcca.R........... 17 tests OK test_predict.dcca.R........... 18 tests OK test_predict.dcca.R........... 19 tests OK test_predict.dcca.R........... 20 tests OK test_predict.dcca.R........... 21 tests OK test_predict.dcca.R........... 22 tests OK test_predict.dcca.R........... 23 tests OK test_predict.dcca.R........... 24 tests OK test_predict.dcca.R........... 25 tests OK test_predict.dcca.R........... 25 tests OK test_predict.dcca.R........... 25 tests OK test_predict.dcca.R........... 25 tests OK test_predict.dcca.R........... 26 tests OK test_predict.dcca.R........... 27 tests OK test_predict.dcca.R........... 27 tests OK test_predict.dcca.R........... 27 tests OK test_predict.dcca.R........... 28 tests OK test_predict.dcca.R........... 29 tests OK test_predict.dcca.R........... 30 tests OK test_predict.dcca.R........... 30 tests OK test_predict.dcca.R........... 30 tests OK test_predict.dcca.R........... 31 tests OK test_predict.dcca.R........... 31 tests OK test_predict.dcca.R........... 32 tests OK test_predict.dcca.R........... 32 tests OK The model is overfitted with no unconstrained (residual) component test_predict.dcca.R........... 32 tests OK test_predict.dcca.R........... 32 tests OK test_predict.dcca.R........... 33 tests OK test_predict.dcca.R........... 33 tests OK test_predict.dcca.R........... 33 tests OK test_predict.dcca.R........... 34 tests OK test_predict.dcca.R........... 34 tests OK test_predict.dcca.R........... 34 tests OK test_predict.dcca.R........... 35 tests OK test_predict.dcca.R........... 35 tests OK test_predict.dcca.R........... 35 tests OK test_predict.dcca.R........... 36 tests OK test_predict.dcca.R........... 37 tests OK test_predict.dcca.R........... 37 tests OK test_predict.dcca.R........... 37 tests OK test_predict.dcca.R........... 38 tests OK The model is overfitted with no unconstrained (residual) component test_predict.dcca.R........... 38 tests OK test_predict.dcca.R........... 39 tests OK test_predict.dcca.R........... 40 tests OK test_predict.dcca.R........... 40 tests OK test_predict.dcca.R........... 40 tests OK test_predict.dcca.R........... 41 tests OK test_predict.dcca.R........... 41 tests OK test_predict.dcca.R........... 41 tests OK test_predict.dcca.R........... 41 tests OK Error in solve.default(qr.R(msqr$qrX)) : system is computationally singular: reciprocal condition number = 4.89592e-17 In addition: Warning messages: 1: In set_newdata(object, newdata1, type = "traitsFromEnv", means_mis = attr(reg, : newdata does not contain the predictor variables Moist,Use These are set at their mean values and, for factors, at the reference level The current formula is ~ Mag + A1 + Moist + Manure + Use 2: In set_newdata(object, newdata1, type = "envFromTraits", means_mis = attr(reg, : newdata does not contain the predictor variables Height,Lifespan,Seedmass These are set at their mean values and, for factors, at the reference level The current formula is ~ Seedmass + SLA + Height + LDMC + Lifespan 3: In set_newdata(object, newdata1[[1]], type = "envFromTraits", means_mis = sc[["regression_traits"]][, : newdata does not contain the predictor variables Height,Lifespan,Seedmass These are set at their mean values and, for factors, at the reference level The current formula is ~ Seedmass + SLA + Height + LDMC + Lifespan 4: In set_newdata(object, newdata1[[2]], type = "traitsFromEnv", means_mis = sc[["regression"]][, : newdata does not contain the predictor variables Moist,Use These are set at their mean values and, for factors, at the reference level The current formula is ~ Mag + A1 + Moist + Manure + Use 5: In set_newdata(object, newdata1, type = "traitsFromEnv", means_mis = attr(reg, : newdata does not contain the predictor variables Moist,Use These are set at their mean values and, for factors, at the reference level The current formula is ~ Mag + A1 + Moist + Manure + Use 6: In set_newdata(object, newdata1, type = "envFromTraits", means_mis = attr(reg, : newdata does not contain the predictor variables Height,Lifespan,Seedmass These are set at their mean values and, for factors, at the reference level The current formula is ~ Seedmass + SLA + Height + LDMC + Lifespan 7: In set_newdata(object, newdata1[[1]], type = "envFromTraits", means_mis = sc[["regression_traits"]][, : newdata does not contain the predictor variables Height,Lifespan,Seedmass These are set at their mean values and, for factors, at the reference level The current formula is ~ Seedmass + SLA + Height + LDMC + Lifespan 8: In set_newdata(object, newdata1[[2]], type = "traitsFromEnv", means_mis = sc[["regression"]][, : newdata does not contain the predictor variables Moist,Use These are set at their mean values and, for factors, at the reference level The current formula is ~ Mag + A1 + Moist + Manure + Use 9: In qt((1 - level)/2, df) : NaNs produced 10: In max(ids, na.rm = TRUE) : no non-missing arguments to max; returning -Inf test_predict.dcca.R........... 42 tests OK test_predict.dcca.R........... 43 tests OK test_predict.dcca.R........... 43 tests OK test_predict.dcca.R........... 43 tests OK test_predict.dcca.R........... 44 tests OK test_predict.dcca.R........... 45 tests OK 0.9s test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 0 tests test_predict.dccav.R.......... 1 tests OK test_predict.dccav.R.......... 1 tests OK test_predict.dccav.R.......... 1 tests OK test_predict.dccav.R.......... 2 tests OK test_predict.dccav.R.......... 2 tests OK test_predict.dccav.R.......... 2 tests OK test_predict.dccav.R.......... 3 tests OK test_predict.dccav.R.......... 3 tests OK test_predict.dccav.R.......... 3 tests OK test_predict.dccav.R.......... 3 tests OK test_predict.dccav.R.......... 4 tests OK test_predict.dccav.R.......... 4 tests OK test_predict.dccav.R.......... 4 tests OK test_predict.dccav.R.......... 4 tests OK test_predict.dccav.R.......... 4 tests OK test_predict.dccav.R.......... 4 tests OK test_predict.dccav.R.......... 4 tests OK test_predict.dccav.R.......... 5 tests OK test_predict.dccav.R.......... 6 tests OK test_predict.dccav.R.......... 7 tests OK test_predict.dccav.R.......... 8 tests OK test_predict.dccav.R.......... 9 tests OK test_predict.dccav.R.......... 10 tests OK test_predict.dccav.R.......... 11 tests OK test_predict.dccav.R.......... 11 tests OK test_predict.dccav.R.......... 11 tests OK test_predict.dccav.R.......... 11 tests OK test_predict.dccav.R.......... 11 tests OK test_predict.dccav.R.......... 12 tests OK test_predict.dccav.R.......... 12 tests OK test_predict.dccav.R.......... 12 tests OK test_predict.dccav.R.......... 12 tests OK test_predict.dccav.R.......... 13 tests OK test_predict.dccav.R.......... 14 tests OK test_predict.dccav.R.......... 15 tests OK test_predict.dccav.R.......... 16 tests OK test_predict.dccav.R.......... 17 tests OK test_predict.dccav.R.......... 17 tests OK test_predict.dccav.R.......... 17 tests OK test_predict.dccav.R.......... 17 tests OK test_predict.dccav.R.......... 17 tests OK test_predict.dccav.R.......... 17 tests OK test_predict.dccav.R.......... 18 tests OK test_predict.dccav.R.......... 19 tests OK test_predict.dccav.R.......... 20 tests OK test_predict.dccav.R.......... 21 tests OK test_predict.dccav.R.......... 22 tests OK test_predict.dccav.R.......... 23 tests OK test_predict.dccav.R.......... 24 tests OK test_predict.dccav.R.......... 25 tests OK test_predict.dccav.R.......... 25 tests OK test_predict.dccav.R.......... 25 tests OK test_predict.dccav.R.......... 25 tests OK test_predict.dccav.R.......... 26 tests OK test_predict.dccav.R.......... 27 tests OK test_predict.dccav.R.......... 27 tests OK test_predict.dccav.R.......... 27 tests OK test_predict.dccav.R.......... 28 tests OK test_predict.dccav.R.......... 29 tests OK test_predict.dccav.R.......... 30 tests OK test_predict.dccav.R.......... 30 tests OK test_predict.dccav.R.......... 30 tests OK test_predict.dccav.R.......... 31 tests OK test_predict.dccav.R.......... 31 tests OK test_predict.dccav.R.......... 32 tests OK test_predict.dccav.R.......... 32 tests OK The model is overfitted with no unconstrained (residual) component The model is overfitted with no unconstrained (residual) component test_predict.dccav.R.......... 32 tests OK test_predict.dccav.R.......... 32 tests OK test_predict.dccav.R.......... 33 tests OK test_predict.dccav.R.......... 33 tests OK test_predict.dccav.R.......... 34 tests OK test_predict.dccav.R.......... 34 tests OK test_predict.dccav.R.......... 34 tests OK test_predict.dccav.R.......... 35 tests OK test_predict.dccav.R.......... 35 tests OK test_predict.dccav.R.......... 35 tests OK test_predict.dccav.R.......... 36 tests OK test_predict.dccav.R.......... 36 tests OK The model is overfitted with no unconstrained (residual) component test_predict.dccav.R.......... 36 tests OK test_predict.dccav.R.......... 37 tests OK test_predict.dccav.R.......... 38 tests OK test_predict.dccav.R.......... 38 tests OK test_predict.dccav.R.......... 38 tests OK test_predict.dccav.R.......... 39 tests OK The model is overfitted with no unconstrained (residual) component test_predict.dccav.R.......... 39 tests OK test_predict.dccav.R.......... 40 tests OK test_predict.dccav.R.......... 41 tests OK test_predict.dccav.R.......... 41 tests OK test_predict.dccav.R.......... 41 tests OK test_predict.dccav.R.......... 42 tests OK test_predict.dccav.R.......... 42 tests OK test_predict.dccav.R.......... 42 tests OK test_predict.dccav.R.......... 42 tests OK Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'A11' Error in solve.default(qr.R(msqr$qrX)) : system is computationally singular: reciprocal condition number = 1.87328e-16 In addition: Warning messages: 1: In f_wmean(object$formulaEnv, tY = t(object$data$Y)/sum(object$data$Y), : singular environment data. Env_explain not available. 2: Collinearity detected in CWM-model. VIF and t-ratio's not available (NA). 3: Collinearity detected in SNC-model. VIF and t-ratio's not available (NA). test_predict.dccav.R.......... 43 tests OK test_predict.dccav.R.......... 44 tests OK test_predict.dccav.R.......... 44 tests OK test_predict.dccav.R.......... 44 tests OK test_predict.dccav.R.......... 45 tests OK test_predict.dccav.R.......... 46 tests OK 0.8s test_wrda.R................... 0 tests test_wrda.R................... 0 tests test_wrda.R................... 0 tests test_wrda.R................... 0 tests test_wrda.R................... 0 tests test_wrda.R................... 0 tests test_wrda.R................... 0 tests test_wrda.R................... 1 tests OK test_wrda.R................... 2 tests OK test_wrda.R................... 3 tests OK test_wrda.R................... 3 tests OK test_wrda.R................... 3 tests OK test_wrda.R................... 4 tests OK test_wrda.R................... 4 tests OK test_wrda.R................... 5 tests OK test_wrda.R................... 5 tests OK test_wrda.R................... 5 tests OK test_wrda.R................... 5 tests OK test_wrda.R................... 6 tests OK test_wrda.R................... 7 tests OK test_wrda.R................... 7 tests OK test_wrda.R................... 7 tests OK test_wrda.R................... 8 tests OK test_wrda.R................... 9 tests OK test_wrda.R................... 10 tests OK test_wrda.R................... 11 tests OK test_wrda.R................... 11 tests OK test_wrda.R................... 12 tests OK test_wrda.R................... 13 tests OK test_wrda.R................... 14 tests OK 3.4s All ok, 287 results (23.0s) Warning messages: 1: In f_wmean(object$formulaEnv, tY = t(object$data$Y)/sum(object$data$Y), : singular environment data. Env_explain not available. 2: Collinearity detected in CWM-model. VIF and t-ratio's not available (NA). 3: Collinearity detected in SNC-model. VIF and t-ratio's not available (NA). > > > proc.time() user system elapsed 22.59 1.45 24.62