test_that("Gaussian response Bugs model construction works", { if(.Platform$OS.type == "unix") { capture.output( # No parent nodes / no predictors expect_no_error(gauss_bugs(nodename = "a", parentnames = NULL, nodesintercept = c(0.318077), parentcoefs = NULL, std = c(0.05773503))), # One parent nodes / predictor expect_no_error(gauss_bugs(nodename = "a", parentnames = "b", nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395)), std = c(0.05773503))), # Multiple parent nodes / predictors expect_no_error(gauss_bugs(nodename = "a", parentnames = c("b", "c"), nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395), "c"=c(c=0.5555)), std = c(0.05773503))), # Multinomial parent nodes / predictors expect_no_error(gauss_bugs(nodename = "a", parentnames = c("b", "c"), nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395, b=0.66666, b=0.77777), # multinomial with three factor levels "c"=c(c=0.5555)), std = c(0.05773503))), file = "/dev/null" ) } outstring <- c( "a ~ dnorm(mu.a, precision.a) # Gaussian response mu.a <- 0.318077 + 0.3059395*b + 0.5555*c # Linear regression precision.a <- inverse(0.05773503) # precision tau = 1/standard_dev") expect_output(gauss_bugs(nodename = "a", parentnames = c("b", "c"), nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395), "c"=c(c=0.5555)), std = c(0.05773503)), regexp = outstring, fixed = TRUE) }) test_that("Gaussian response Bugs model construction with mixed-effects works", { muY <- 3 sigmaY <- 1 sigma_alphaY <- 0.5 betaX <- 4 betaN <- 5 if(.Platform$OS.type == "unix") { capture.output( # No parent nodes / no predictors expect_no_error(gauss_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = NULL, parentcoefs = NULL, sigma = sigmaY, sigma_alpha = sigma_alphaY)), # One parent nodes / predictor expect_no_error(gauss_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X"), parentcoefs = list("X"=c(X=betaX)), sigma = sigmaY, sigma_alpha = sigma_alphaY)), # Multiple parent nodes / predictors expect_no_error(gauss_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X", "N"), parentcoefs = list("X"=c(X=betaX), "N"=c(N=betaN)), sigma = sigmaY, sigma_alpha = sigma_alphaY)), # Multinomial parent nodes / predictors # expect_no_error(), file = "/dev/null" ) } outstring <- c( "Y <- mu_Y + 4*X + 5*N + alpha_Y + e_Y mu_Y <- 3 e_Y ~ dnorm(mu_e_Y, tau_Y) mu_e_Y <- 0 tau_Y <- inverse(sigma_Y) sigma_Y <- 1 alpha_Y ~ dnorm(mu_alpha_Y, tau_alpha_Y) mu_alpha_Y <- 0 tau_alpha_Y <- inverse(sigma_alpha_Y) sigma_alpha_Y <- 0.5") expect_output(gauss_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X", "N"), parentcoefs = list("X"=c(X=betaX), "N"=c(N=betaN)), sigma = sigmaY, sigma_alpha = sigma_alphaY), regexp = outstring, fixed = TRUE) }) test_that("Binomial response Bugs model construction works", { if(.Platform$OS.type == "unix") { capture.output( # No parent nodes / no predictors expect_no_error(bern_bugs(nodename = "a", parentnames = NULL, nodesintercept = c(0.318077), parentcoefs = NULL)), # One parent nodes / predictor expect_no_error(bern_bugs(nodename = "a", parentnames = "b", nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395)))), # Multiple parent nodes / predictors expect_no_error(bern_bugs(nodename = "a", parentnames = c("b", "c"), nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395), "c"=c(c=0.5555)))), file = "/dev/null" ) } outstring <- c( "a ~ dbern(p.a) # Bernoulli response logit(p.a) <- 0.318077 + 0.3059395*b + 0.5555*c # logistic regression") expect_output(bern_bugs(nodename = "a", parentnames = c("b", "c"), nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395), "c"=c(c=0.5555))), regexp = outstring, fixed = TRUE) }) test_that("Binomial response Bugs model construction with mixed-effects works", { muY <- 3 sigma_alphaY <- 0.5 betaX <- 4 betaN <- 5 if(.Platform$OS.type == "unix") { capture.output( # No parent nodes / no predictors expect_no_error(bern_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = NULL, parentcoefs = NULL, sigma_alpha = sigma_alphaY)), # One parent nodes / predictor expect_no_error(bern_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X"), parentcoefs = list("X"=c(X=betaX)), sigma_alpha = sigma_alphaY)), # Multiple parent nodes / predictors expect_no_error(bern_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X", "N"), parentcoefs = list("X"=c(X=betaX), "N"=c(N=betaN)), sigma_alpha = sigma_alphaY)), # Multinomial parent nodes / predictors # expect_no_error(), file = "/dev/null" ) } outstring <- c( "Y ~ dbern(p_Y) logit(p_Y) <- mu_Y + 4*X + 5*N + alpha_Y mu_Y <- 3 alpha_Y ~ dnorm(mu_alpha_Y, tau_alpha_Y) mu_alpha_Y <- 0 tau_alpha_Y <- inverse(sigma_alpha_Y) sigma_alpha_Y <- 0.5") expect_output(bern_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X", "N"), parentcoefs = list("X"=c(X=betaX), "N"=c(N=betaN)), sigma_alpha = sigma_alphaY), regexp = outstring, fixed = TRUE) }) test_that("Categorical response Bugs model construction works", { if(.Platform$OS.type == "unix") { capture.output( # No parent nodes / no predictors expect_no_error(categorical_bugs(nodename = "b", nodesCatIdx = c(2, 3, 4), parentnames = NULL, nodesintercepts = c(2.188650, 3.133928, 3.138531), parentcoefs = NULL)), # One parent nodes / predictor expect_no_error(categorical_bugs(nodename = "b", nodesCatIdx = c(2, 3, 4), parentnames = "a", nodesintercepts = c(2.188650, 3.133928, 3.138531), parentcoefs = list("a"=c(a=1.686432, a=3.134161, a=5.052104)))), # Multiple parent nodes / predictors expect_no_error(categorical_bugs(nodename = "b", nodesCatIdx = c(2, 3, 4), parentnames = c("a", "c"), nodesintercepts = c(2.188650, 3.133928, 3.138531), parentcoefs = list("a"=c(a=1.686432, a=3.134161, a=5.052104), "c"=c(c=0.5555, c=0.6666, c=0.7777)))), file = "/dev/null" ) } outstring <- c( "b ~ dcat(p.b) # Categorical response p.b[1] <- phi.b[1]/sum(phi.b) # soft-max log(phi.b[1]) <- 0 # Reference category p.b[2] <- phi.b[2]/sum(phi.b) # soft-max log(phi.b[2]) <- 2.18865 + 1.686432*a p.b[3] <- phi.b[3]/sum(phi.b) # soft-max log(phi.b[3]) <- 3.133928 + 3.134161*a p.b[4] <- phi.b[4]/sum(phi.b) # soft-max log(phi.b[4]) <- 3.138531 + 5.052104*a") expect_output(categorical_bugs(nodename = "b", nodesCatIdx = c(2, 3, 4), parentnames = "a", nodesintercepts = c(2.188650, 3.133928, 3.138531), parentcoefs = list("a"=c(a=1.686432, a=3.134161, a=5.052104))), regexp = outstring, fixed = TRUE) }) test_that("Categorical response Bugs model construction with mixed-effects works", { nodename <- "Y" nodesCatIdx <- c(2,3,4) nodesintercepts <- c(5,6,7) parentnames <- c("X") parentcoefs <- data.frame(X = c(4,5,6)) sigma <- 1 # within variance sigma_alpha <- matrix(0, ncol = 4, nrow = 4) # var-covariance for one predictor diag(sigma_alpha) <- 0.5 sigma_alpha2 <- matrix(0, ncol = 8, nrow = 8) # var-covariance for two predictors diag(sigma_alpha2) <- 0.5 if(.Platform$OS.type == "unix") { capture.output( # No parent nodes / no predictors expect_no_error(categorical_bugsGroup(nodename = nodename, nodesCatIdx = nodesCatIdx, nodesintercepts = nodesintercepts, parentnames = NULL, parentcoefs = NULL, sigma = sigma, sigma_alpha = sigma_alpha)), # One parent nodes / predictor expect_no_error(categorical_bugsGroup(nodename = nodename, nodesCatIdx = nodesCatIdx, nodesintercepts = nodesintercepts, parentnames = parentnames, parentcoefs = parentcoefs, sigma = sigma, sigma_alpha = sigma_alpha)), # Multiple parent nodes / predictors expect_no_error(categorical_bugsGroup(nodename = nodename, nodesCatIdx = nodesCatIdx, nodesintercepts = nodesintercepts, parentnames = c("X1", "X2"), parentcoefs = data.frame(X1 = c(4,5,6), X2 = c(4,5,6)), sigma = sigma, sigma_alpha = sigma_alpha2)), # Multinomial parent nodes / predictors # expect_no_error(), file = "/dev/null" ) } outstring <- c( "Y ~ dcat(p_Y) p_Y[1] <- phi_Y[1]/sum(phi_Y) log(phi_Y[1]) <- 0 + alpha_Y[1] p_Y[2] <- phi_Y[2]/sum(phi_Y) log(phi_Y[2]) <- 5 + 4*X + alpha_Y[1] p_Y[3] <- phi_Y[3]/sum(phi_Y) log(phi_Y[3]) <- 6 + 5*X + alpha_Y[2] p_Y[4] <- phi_Y[4]/sum(phi_Y) log(phi_Y[4]) <- 7 + 6*X + alpha_Y[3] alpha_Y ~ dmnorm.vcov(mu_alpha_Y, sigma_alpha_Y) mu_alpha_Y[1] <- 0 sigma_alpha_Y[1, 1] <- 0.5 sigma_alpha_Y[1, 2] <- 0 sigma_alpha_Y[1, 3] <- 0 sigma_alpha_Y[1, 4] <- 0 mu_alpha_Y[2] <- 0 sigma_alpha_Y[2, 1] <- 0 sigma_alpha_Y[2, 2] <- 0.5 sigma_alpha_Y[2, 3] <- 0 sigma_alpha_Y[2, 4] <- 0 mu_alpha_Y[3] <- 0 sigma_alpha_Y[3, 1] <- 0 sigma_alpha_Y[3, 2] <- 0 sigma_alpha_Y[3, 3] <- 0.5 sigma_alpha_Y[3, 4] <- 0 mu_alpha_Y[4] <- 0 sigma_alpha_Y[4, 1] <- 0 sigma_alpha_Y[4, 2] <- 0 sigma_alpha_Y[4, 3] <- 0 sigma_alpha_Y[4, 4] <- 0.5") expect_output(categorical_bugsGroup(nodename = nodename, nodesCatIdx = nodesCatIdx, nodesintercepts = nodesintercepts, parentnames = parentnames, parentcoefs = parentcoefs, sigma = sigma, sigma_alpha = sigma_alpha), regexp = outstring, fixed = TRUE) }) test_that("Poisson response Bugs model construction works", { if(.Platform$OS.type == "unix") { capture.output( # No parent nodes / no predictors expect_no_error(pois_bugs(nodename = "a", parentnames = NULL, nodesintercept = c(0.318077), parentcoefs = NULL)), # One parent nodes / predictor expect_no_error(pois_bugs(nodename = "a", parentnames = "b", nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395)))), # Multiple parent nodes / predictors expect_no_error(pois_bugs(nodename = "a", parentnames = c("b", "c"), nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395), "c"=c(c=0.5555)))), file = "/dev/null" ) } outstring <- c( "a ~ dpois(lambda.a) # Poisson response log(lambda.a) <- 0.318077 + 0.3059395*b + 0.5555*c # logistic regression") expect_output(pois_bugs(nodename = "a", parentnames = c("b", "c"), nodesintercept = c(0.318077), parentcoefs = list("b"=c(b=0.3059395), "c"=c(c=0.5555))), regexp = outstring, fixed = TRUE) }) test_that("Poisson response Bugs model construction with mixed-effects works", { muY <- 3 sigma_alphaY <- 0.5 betaX <- 4 betaN <- 5 if(.Platform$OS.type == "unix") { capture.output( # No parent nodes / no predictors expect_no_error(pois_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = NULL, parentcoefs = NULL, sigma_alpha = sigma_alphaY)), # One parent nodes / predictor expect_no_error(pois_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X"), parentcoefs = list("X"=c(X=betaX)), sigma_alpha = sigma_alphaY)), # Multiple parent nodes / predictors expect_no_error(pois_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X", "N"), parentcoefs = list("X"=c(X=betaX), "N"=c(N=betaN)), sigma_alpha = sigma_alphaY)), # Multinomial parent nodes / predictors # expect_no_error(), file = "/dev/null" ) } outstring <- c( "Y ~ dpois(lambda_Y) log(lambda_Y) <- mu_Y + 4*X + 5*N + alpha_Y mu_Y <- 3 alpha_Y ~ dnorm(mu_alpha_Y, tau_alpha_Y) mu_alpha_Y <- 0 tau_alpha_Y <- inverse(sigma_alpha_Y) sigma_alpha_Y <- 0.5") expect_output(pois_bugsGroup(nodename = "Y", nodesintercept = muY, parentnames = c("X", "N"), parentcoefs = list("X"=c(X=betaX), "N"=c(N=betaN)), sigma_alpha = sigma_alphaY), regexp = outstring, fixed = TRUE) }) test_that("makebugs() works", { mydists <- list(a="gaussian", b="multinomial", c="binomial", d="poisson") mydag <- matrix(0, 4, 4, byrow = TRUE, dimnames = list(c("a", "b", "c", "d"), c("a", "b", "c", "d"))) mydag[2,1] <- mydag[3,2] <- mydag[4,3] <- 1 # plotAbn(mydag, data.dists = mydists) mycoefs <- list("a"=matrix(-6.883383e-17, byrow = T, dimnames = list(NULL, "a|intercept")), "b"=matrix(c(2.18865, 3.133928, 3.138531, 1.686432, 3.134161, 5.052104), nrow= 1, byrow = T, dimnames = list(c(NULL), c("b|intercept.2", "b|intercept.3", "b|intercept.4", "a.2", "a.3", "a.4"))), "c"=matrix(c(1.11, 2.22, 3.33, 4.44, 5.55), nrow= 1, byrow = T, dimnames = list(c(NULL), c("c|intercept", "b1", "b2", "b3", "b4"))), "d"=matrix(c(3.33, 4.44), nrow= 1, byrow = T, dimnames = list(c(NULL), c("d|intercept", "c")))) mymse <- c("a"=0,"b"=1,"c"=2,"d"=3) if(.Platform$OS.type == "unix") { capture.output( expect_no_error({ makebugs(dag = mydag, data.dists = mydists, coefs = mycoefs, std = mymse)}), file = "/dev/null") } outstring <- c( "model{ a ~ dnorm(mu.a, precision.a) # Gaussian response mu.a <- -6.883383e-17 # Linear regression precision.a <- inverse(0) # precision tau = 1/standard_dev b ~ dcat(p.b) # Categorical response p.b[1] <- phi.b[1]/sum(phi.b) # soft-max log(phi.b[1]) <- 0 # Reference category p.b[2] <- phi.b[2]/sum(phi.b) # soft-max log(phi.b[2]) <- 2.18865 + 1.686432*a p.b[3] <- phi.b[3]/sum(phi.b) # soft-max log(phi.b[3]) <- 3.133928 + 3.134161*a p.b[4] <- phi.b[4]/sum(phi.b) # soft-max log(phi.b[4]) <- 3.138531 + 5.052104*a c ~ dbern(p.c) # Bernoulli response logit(p.c) <- 1.11 + 0.0242836145724376*b + 0.0736851897251164*b + 0.223587273987992*b + 0.678443921714454*b # logistic regression d ~ dpois(lambda.d) # Poisson response log(lambda.d) <- 3.33 + 4.44*c # logistic regression }" ) expect_output({ makebugs(dag = mydag, data.dists = mydists, coefs = mycoefs, std = mymse) }, regexp = outstring, fixed = TRUE, width = 150) }) test_that("makebugsGroup() works",{ # load("tests/testthat/testdata/makebugsGauss_data.Rdata") load("testdata/makebugsGauss_data.Rdata") expectedOut <- c( "model{ Outdoor ~ dbern(p_Outdoor) logit(p_Outdoor) <- mu_Outdoor + alpha_Outdoor mu_Outdoor <- -0.1231088 alpha_Outdoor ~ dnorm(mu_alpha_Outdoor, tau_alpha_Outdoor) mu_alpha_Outdoor <- 0 tau_alpha_Outdoor <- inverse(sigma_alpha_Outdoor) sigma_alpha_Outdoor <- 1.045681 Sex ~ dcat(p_Sex) p_Sex[1] <- phi_Sex[1]/sum(phi_Sex) log(phi_Sex[1]) <- 0 + alpha_Sex[1] p_Sex[2] <- phi_Sex[2]/sum(phi_Sex) log(phi_Sex[2]) <- 0.9706316 + 1.28692758032913*Age + alpha_Sex[1] p_Sex[3] <- phi_Sex[3]/sum(phi_Sex) log(phi_Sex[3]) <- -0.05845993 + 0.28240627414296*Age + alpha_Sex[2] p_Sex[4] <- phi_Sex[4]/sum(phi_Sex) log(phi_Sex[4]) <- 0.3357361 + 1.57473104113221*Age + alpha_Sex[3] alpha_Sex ~ dmnorm.vcov(mu_alpha_Sex, sigma_alpha_Sex) mu_alpha_Sex[1] <- 0 sigma_alpha_Sex[1, 1] <- 5.933619e-05 sigma_alpha_Sex[1, 2] <- 1e-50 sigma_alpha_Sex[1, 3] <- 4.32787e-08 mu_alpha_Sex[2] <- 0 sigma_alpha_Sex[2, 1] <- 1e-50 sigma_alpha_Sex[2, 2] <- 5.834134e-05 sigma_alpha_Sex[2, 3] <- 1e-50 mu_alpha_Sex[3] <- 0 sigma_alpha_Sex[3, 1] <- 4.32787e-08 sigma_alpha_Sex[3, 2] <- 1e-50 sigma_alpha_Sex[3, 3] <- 6.37651e-05 GroupSize ~ dpois(lambda_GroupSize) log(lambda_GroupSize) <- mu_GroupSize + -0.282436260957026*Outdoor + alpha_GroupSize mu_GroupSize <- 1.251637 alpha_GroupSize ~ dnorm(mu_alpha_GroupSize, tau_alpha_GroupSize) mu_alpha_GroupSize <- 0 tau_alpha_GroupSize <- inverse(sigma_alpha_GroupSize) sigma_alpha_GroupSize <- 1e-50 Age <- mu_Age + -0.00454841753065188*GroupSize + alpha_Age + e_Age mu_Age <- -0.04832546 e_Age ~ dnorm(mu_e_Age, tau_Age) mu_e_Age <- 0 tau_Age <- inverse(sigma_Age) sigma_Age <- 0.9912502 alpha_Age ~ dnorm(mu_alpha_Age, tau_alpha_Age) mu_alpha_Age <- 0 tau_alpha_Age <- inverse(sigma_alpha_Age) sigma_alpha_Age <- 0.22154 }") expect_output({ makebugsGroup(dag = dag, data.dists = data.dists, stderrors = mse, group.var = group.var, mu = mu, betas = betas, sigma = sigm, sigma_alpha = sigm_alpha)}, regexp = expectedOut, fixed = TRUE, width = 150) }) test_that("simulateAbn() catches wrong arguments", { # Make a proper abnFit object ## without group.var if(.Platform$OS.type == "unix") { capture.output({ df <- FCV[, c(12:15)] mydists <- list(Outdoor="binomial", Sex="multinomial", GroupSize="poisson", Age="gaussian") ## buildScoreCache -> mostProbable() -> fitAbn() suppressWarnings({ mycache.mle <- buildScoreCache(data.df = df, data.dists = mydists, method = "mle", adj.vars = NULL, cor.vars = NULL, dag.banned = NULL, dag.retained = NULL, max.parents = 1, which.nodes = NULL, defn.res = NULL) }) # ignore non-convergence warnings expect_no_error({ mp.dag.mle <- mostProbable(score.cache = mycache.mle, verbose = FALSE) }) expect_no_error({ myres.mle <- fitAbn(object = mp.dag.mle, method = "mle") }) }, file = "/dev/null" ) # The actual tests capture.output( expect_no_error({ simulateAbn(object = myres.mle, run.simulation = TRUE, bugsfile = NULL, verbose = FALSE) }), file = "/dev/null") expect_error({ simulateAbn(object = myres.mle, run.simulation = "TRUE", bugsfile = NULL, verbose = FALSE) }) expect_error({ simulateAbn(object = myres.mle, run.simulation = TRUE, bugsfile = 1, verbose = FALSE) }) expect_error({ simulateAbn(object = myres.mle, run.simulation = TRUE, bugsfile = NULL, verbose = "FALSE") }) expect_error({ simulateAbn(object = unclass(myres.mle), run.simulation = TRUE, bugsfile = NULL, verbose = FALSE) }) expect_error({ simulateAbn(object = NULL, run.simulation = TRUE, bugsfile = NULL, verbose = FALSE) }) } }) test_that("simulateAbn() simulation works with method 'mle'", { # Make a proper abnFit object ## without group.var if(.Platform$OS.type == "unix") { capture.output({ df <- FCV[, c(12:15)] mydists <- list(Outdoor="binomial", Sex="multinomial", GroupSize="poisson", Age="gaussian") ## buildScoreCache -> mostProbable() -> fitAbn() suppressWarnings({ mycache.mle <- buildScoreCache(data.df = df, data.dists = mydists, method = "mle", adj.vars = NULL, cor.vars = NULL, dag.banned = NULL, dag.retained = NULL, max.parents = 1, which.nodes = NULL, defn.res = NULL) }) # ignore non-convergence warnings expect_no_error({ mp.dag.mle <- mostProbable(score.cache = mycache.mle, verbose = FALSE) }) expect_no_error({ myres.mle <- fitAbn(object = mp.dag.mle, method = "mle", centre = FALSE) }) }, file = "/dev/null" ) expect_no_error({ myres.sim <- simulateAbn(object = myres.mle, run.simulation = TRUE, bugsfile = NULL, n.chains = 10L, n.adapt = 1000L, n.thin = 100L, n.iter = 10000L, seed = 42L, verbose = FALSE) }) act <- as.numeric(round(prop.table(table(myres.sim$Outdoor)), 2)) expected <- as.numeric(round(prop.table(table(df$Outdoor)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.05) act <- as.numeric(round(prop.table(table(myres.sim$Sex)), 2)) expected <- as.numeric(round(prop.table(table(df$Sex)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.05) ## with group.var suppressWarnings({ suppressMessages({ capture.output({ df <- FCV[, c(11:15)] mydists <- list(Pedigree="binomial", Outdoor="binomial", Sex="multinomial", GroupSize="poisson", Age="gaussian") mydists <- mydists[-1] # remove grouping variable from distribution list retaindag <- matrix(0, nrow = length(mydists), ncol = length(mydists), dimnames = list(names(mydists), names(mydists))) retaindag[3, 1] <- retaindag[4,3] <- retaindag[2,4] <- 1 mycache.mle.grp <- buildScoreCache(data.df = df, data.dists = mydists, method = "mle", group.var = "Pedigree", adj.vars = NULL, cor.vars = NULL, dag.banned = NULL, dag.retained = retaindag, max.parents = 3, which.nodes = NULL, defn.res = NULL) mp.dag.mle.grp <- mostProbable(score.cache = mycache.mle.grp, verbose = FALSE) expect_no_error({ myres.mle.grp <- fitAbn(object = mp.dag.mle.grp, method = "mle", group.var = "Pedigree") }) expect_no_error({ mysim.grp <- simulateAbn(object = myres.mle.grp, run.simulation = TRUE, bugsfile = NULL, verbose = FALSE, debug = FALSE) }) }, file = "/dev/null" ) }) }) act <- as.numeric(round(prop.table(table(mysim.grp$Outdoor)), 2)) expected <- as.numeric(round(prop.table(table(df$Outdoor)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.1) # quite high tolerance because the actual data has no grouping... # Correct number of categories simulated for categorical/multinomial variables? act <- as.numeric(round(prop.table(table(mysim.grp$Sex)), 2)) expected <- as.numeric(round(prop.table(table(df$Sex)), 2)) expect_equal(length(act), length(expected)) } }) test_that("simulateAbn() simulation works with method 'bayes'", { # Make a proper abnFit object ## without group.var if(.Platform$OS.type == "unix") { capture.output({ df <- FCV[, c(12, 14:15)] mydists <- list(Outdoor="binomial", # Sex="multinomial", GroupSize="poisson", Age="gaussian") ## buildScoreCache -> mostProbable() -> fitAbn() suppressWarnings({ mycache.bayes <- buildScoreCache(data.df = df, data.dists = mydists, method = "bayes", adj.vars = NULL, cor.vars = NULL, dag.banned = NULL, dag.retained = NULL, max.parents = 1, which.nodes = NULL, defn.res = NULL) }) # ignore non-convergence warnings expect_no_error({ mp.dag.bayes <- mostProbable(score.cache = mycache.bayes, verbose = FALSE) }) expect_no_error({ myres.bayes <- fitAbn(object = mp.dag.bayes, method = "bayes", centre = FALSE) }) }, file = "/dev/null" ) expect_no_error({ myres.sim <- simulateAbn(object = myres.bayes, run.simulation = TRUE, bugsfile = NULL, n.chains = 10L, n.adapt = 1000L, n.thin = 100L, n.iter = 10000L, seed = 42L, verbose = FALSE) }) act <- as.numeric(round(prop.table(table(myres.sim$Outdoor)), 2)) expected <- as.numeric(round(prop.table(table(df$Outdoor)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.05) act <- as.numeric(round(prop.table(table(myres.sim$Sex)), 2)) expected <- as.numeric(round(prop.table(table(df$Sex)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.05) skip("simulateAbn() with method 'bayes' is not tested with 'group.var'.") ## with group.var suppressWarnings({ suppressMessages({ capture.output({ df <- FCV[, c(11:15)] mydists <- list(Pedigree="binomial", Outdoor="binomial", Sex="multinomial", GroupSize="poisson", Age="gaussian") mydists <- mydists[-1] # remove grouping variable from distribution list retaindag <- matrix(0, nrow = length(mydists), ncol = length(mydists), dimnames = list(names(mydists), names(mydists))) retaindag[3, 1] <- retaindag[4,3] <- retaindag[2,4] <- 1 mycache.mle.grp <- buildScoreCache(data.df = df, data.dists = mydists, method = "bayes", group.var = "Pedigree", adj.vars = NULL, cor.vars = NULL, dag.banned = NULL, dag.retained = retaindag, max.parents = 3, which.nodes = NULL, defn.res = NULL) mp.dag.mle.grp <- mostProbable(score.cache = mycache.mle.grp, verbose = FALSE) expect_no_error({ myres.mle.grp <- fitAbn(object = mp.dag.mle.grp, method = "bayes", group.var = "Pedigree") }) expect_no_error({ mysim.grp <- simulateAbn(object = myres.mle.grp, run.simulation = TRUE, bugsfile = NULL, verbose = FALSE, debug = FALSE) }) }, file = "/dev/null" ) }) }) act <- as.numeric(round(prop.table(table(mysim.grp$Outdoor)), 2)) expected <- as.numeric(round(prop.table(table(df$Outdoor)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.1) # quite high tolerance because the actual data has no grouping... # Correct number of categories simulated for categorical/multinomial variables? act <- as.numeric(round(prop.table(table(mysim.grp$Sex)), 2)) expected <- as.numeric(round(prop.table(table(df$Sex)), 2)) expect_equal(length(act), length(expected)) } }) test_that("simulateAbn() works with grouping in real data.",{ suppressMessages({ suppressWarnings({ if(.Platform$OS.type == "unix") { capture.output({ df <- adg df[,1:5] <- lapply(df[,1:5], factor) df[,9] <- factor(df[,9]) mydists <- list(AR = "binomial", pneumS = "binomial", female = "binomial", livdam = "binomial", eggs = "binomial", wormCount = "poisson", age = "gaussian", adg = "gaussian") # farm = "multinomial") #ban/retain matrices myretain <- matrix(0, length(mydists), length(mydists)) colnames(myretain) <- rownames(myretain) <- names(mydists) mybanned <- matrix(0, length(mydists), length(mydists)) colnames(mybanned) <- rownames(mybanned) <- names(mydists) mybanned[3,-3] <- 1 ### # max parent search ### max.par <- length(mydists)-1 # maximal possible parent values all.fits <- list() for (i in 1:max.par) { mycache <- buildScoreCache(data.df = df, data.dists = mydists, dag.banned = mybanned, dag.retained = myretain, max.parents = i, group.var = "farm", method = "mle") mydag <- mostProbable(score.cache = mycache) fabn <- fitAbn(object = mydag, method = "mle") cat(paste("network score for", i, "parents =", fabn$mlik, "\n\n")) all.fits[i] <- list(fabn) } allmlik <- lapply(X = all.fits, FUN = function(x){sum(x$mlik)}) # plot(unlist(allmlik), xlab = "number of max. parents", ylab = "mlik") first.maxpar <- min(which(unlist(allmlik) == max(unlist(allmlik)))) ### # extract best fit ### best.fit <- all.fits[[first.maxpar]] # plotAbn(best.fit) ### # simulate new data based on estimated parameters from GLMM ### mysim <- simulateAbn(best.fit, verbose = FALSE) act <- as.numeric(round(prop.table(table(mysim$livdam)), 2)) expected <- as.numeric(round(prop.table(table(df$livdam)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.001) act <- as.numeric(round(prop.table(table(mysim$eggs)), 2)) expected <- as.numeric(round(prop.table(table(df$eggs)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.05) act <- as.numeric(round(prop.table(table(mysim$pneumS)), 2)) expected <- as.numeric(round(prop.table(table(df$pneumS)), 2)) expect_equal(act[order(act)], expected[order(expected)], tolerance = 0.07) # quite high tolerance because the actual data has no grouping... }, file = "/dev/null" ) } }) }) })