* using log directory 'd:/Rcompile/CRANpkg/local/3.3/psycho.Rcheck' * using R version 3.3.3 (2017-03-06) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * checking for file 'psycho/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'psycho' version '0.1.4' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'psycho' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... NOTE Authors@R field gives persons with no valid roles: Sasha Epskamp [rev] (Sasha reviewed the package for JOSS, see https://github.com/openjournals/joss-reviews/issues/470) * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking use of S3 registration ... OK * checking dependencies in R code ... NOTE Missing or unexported object: 'lmerTest::summary' * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... ERROR Running examples in 'psycho-Ex.R' failed The error most likely occurred in: > ### Name: analyze.glmerMod > ### Title: Analyze glmerMod objects. > ### Aliases: analyze.glmerMod > > ### ** Examples > > library(psycho) > require(lme4) Loading required package: lme4 Loading required package: Matrix > fit <- lme4::glmer(vs ~ mpg + (1|cyl), data=mtcars, family="binomial") > > results <- analyze(fit) The result is correct only if all data used by the model has not changed since model was fitted. The result is correct only if all data used by the model has not changed since model was fitted. Error: 'summary' is not an exported object from 'namespace:lmerTest' Execution halted * checking for unstated dependencies in 'tests' ... OK * checking tests ... [66s] ERROR Running 'testthat.R' [66s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(psycho) > > test_check("psycho") -- 1. Error: If it works. (@test-analyze.glmerMod.R#9) ------------------------ 'summary' is not an exported object from 'namespace:lmerTest' 1: psycho::analyze(fit) at testthat/test-analyze.glmerMod.R:9 2: analyze.glmerMod(fit) 3: data.frame(lmerTest::summary(fit)$coefficients) 4: lmerTest::summary 5: getExportedValue(pkg, name) 6: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), call. = FALSE, domain = NA) -- 2. Error: If it works. (@test-analyze.merMod.R#9) -------------------------- object of type 'closure' is not subsettable 1: psycho::analyze(x) at testthat/test-analyze.merMod.R:9 2: analyze.merMod(x) 3: analyze(fit) 4: analyze.merMod(fit) 5: lmerTest::lmer(formula, data) 6: eval.parent(mc) 7: eval(expr, p) 8: eval(expr, envir, enclos) 9: lme4::lmer(formula = formula, data = data) 10: eval(mc, parent.frame(1L)) 11: eval(expr, envir, enclos) 12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa", restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE, checkControl = structure(list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop", check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop", check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"), .Names = c("check.nobs.vs.rankZ", "check.nobs.vs.nlev", "check.nlev.gtreq.5", "check.nlev.gtr.1", "check.nobs.vs.nRE", "check.rankX", "check.scaleX", "check.formula.LHS")), checkConv = structure(list( check.conv.grad = structure(list(action = "warning", tol = 0.002, relTol = NULL), .Names = c("action", "tol", "relTol")), check.conv.singular = structure(list(action = "ignore", tol = 1e-04), .Names = c("action", "tol")), check.conv.hess = structure(list( action = "warning", tol = 1e-06), .Names = c("action", "tol"))), .Names = c("check.conv.grad", "check.conv.singular", "check.conv.hess")), optCtrl = list()), .Names = c("optimizer", "restart_edge", "boundary.tol", "calc.derivs", "use.last.params", "checkControl", "checkConv", "optCtrl"), class = c("lmerControl", "merControl"))) 13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop") 14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3) 15: as.formula(formula, env = denv) 16: formula(object, env = baseenv()) 17: formula.character(object, env = baseenv()) 18: formula(eval(parse(text = x, keep.source = FALSE)[[1L]])) 19: formula.default(eval(parse(text = x, keep.source = FALSE)[[1L]])) 20: notnull(x$formula) -- 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) ------------------ object of type 'closure' is not subsettable 1: psycho::analyze(fit) at testthat/test-analyze.merModLmerTest.R:9 2: analyze.merMod(fit) 3: analyze(fit) 4: analyze.merMod(fit) 5: lmerTest::lmer(formula, data) 6: eval.parent(mc) 7: eval(expr, p) 8: eval(expr, envir, enclos) 9: lme4::lmer(formula = formula, data = data) 10: eval(mc, parent.frame(1L)) 11: eval(expr, envir, enclos) 12: lme4::lFormula(formula = formula, data = data, control = structure(list(optimizer = "bobyqa", restart_edge = TRUE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE, checkControl = structure(list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop", check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop", check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"), .Names = c("check.nobs.vs.rankZ", "check.nobs.vs.nlev", "check.nlev.gtreq.5", "check.nlev.gtr.1", "check.nobs.vs.nRE", "check.rankX", "check.scaleX", "check.formula.LHS")), checkConv = structure(list( check.conv.grad = structure(list(action = "warning", tol = 0.002, relTol = NULL), .Names = c("action", "tol", "relTol")), check.conv.singular = structure(list(action = "ignore", tol = 1e-04), .Names = c("action", "tol")), check.conv.hess = structure(list( action = "warning", tol = 1e-06), .Names = c("action", "tol"))), .Names = c("check.conv.grad", "check.conv.singular", "check.conv.hess")), optCtrl = list()), .Names = c("optimizer", "restart_edge", "boundary.tol", "calc.derivs", "use.last.params", "checkControl", "checkConv", "optCtrl"), class = c("lmerControl", "merControl"))) 13: checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop") 14: stopifnot(!checkLHS || length(as.formula(formula, env = denv)) == 3) 15: as.formula(formula, env = denv) 16: formula(object, env = baseenv()) 17: formula.character(object, env = baseenv()) 18: formula(eval(parse(text = x, keep.source = FALSE)[[1L]])) 19: formula.default(eval(parse(text = x, keep.source = FALSE)[[1L]])) 20: notnull(x$formula) SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1). Rejecting initial value: Log probability evaluates to log(0), i.e. negative infinity. Stan can't start sampling from this initial value. Rejecting initial value: Log probability evaluates to log(0), i.e. negative infinity. Stan can't start sampling from this initial value. Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.908 seconds (Warm-up) 0.673 seconds (Sampling) 1.581 seconds (Total) SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2). Rejecting initial value: Log probability evaluates to log(0), i.e. negative infinity. Stan can't start sampling from this initial value. Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 1.061 seconds (Warm-up) 0.749 seconds (Sampling) 1.81 seconds (Total) SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.983 seconds (Warm-up) 0.796 seconds (Sampling) 1.779 seconds (Total) SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.999 seconds (Warm-up) 0.686 seconds (Sampling) 1.685 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 3.716 seconds (Warm-up) 3.341 seconds (Sampling) 7.057 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 3.918 seconds (Warm-up) 4.334 seconds (Sampling) 8.252 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3). Gradient evaluation took 0.016 seconds 1000 transitions using 10 leapfrog steps per transition would take 160 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 3.968 seconds (Warm-up) 4.823 seconds (Sampling) 8.791 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 4.125 seconds (Warm-up) 3.964 seconds (Sampling) 8.089 seconds (Total) The Crawford-Howell (1998) t-test suggests that the case partipant is significantly distinct from the control group (t(7) = 3.05, p < .05*). SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.187 seconds (Warm-up) 0.206 seconds (Sampling) 0.393 seconds (Total) SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.203 seconds (Warm-up) 0.205 seconds (Sampling) 0.408 seconds (Total) SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.203 seconds (Warm-up) 0.171 seconds (Sampling) 0.374 seconds (Total) SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.187 seconds (Warm-up) 0.172 seconds (Sampling) 0.359 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.14 seconds (Warm-up) 0.125 seconds (Sampling) 0.265 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.125 seconds (Warm-up) 0.109 seconds (Sampling) 0.234 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.113 seconds (Warm-up) 0.124 seconds (Sampling) 0.237 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.156 seconds (Warm-up) 0.125 seconds (Sampling) 0.281 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.409 seconds (Warm-up) 0.328 seconds (Sampling) 0.737 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.374 seconds (Warm-up) 0.312 seconds (Sampling) 0.686 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.499 seconds (Warm-up) 0.328 seconds (Sampling) 0.827 seconds (Total) SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4). Gradient evaluation took 0 seconds 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.437 seconds (Warm-up) 0.296 seconds (Sampling) 0.733 seconds (Total) The Mellenbergh & van den Brink (1998) test suggests that the change is not significant (d = 8, 90% CI [1.06, 14.94], z = 1.90, p = 0.06°). The Mellenbergh & van den Brink (1998) test suggests that the change is significant (d = 8, 90% CI [2.07, 13.93], z = 2.23, p < .05*). 1 == testthat results =========================================================== OK: 56 SKIPPED: 0 FAILED: 3 1. Error: If it works. (@test-analyze.glmerMod.R#9) 2. Error: If it works. (@test-analyze.merMod.R#9) 3. Error: If it works. (@test-analyze.merModLmerTest.R#9) Error: testthat unit tests failed Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... OK * checking re-building of vignette outputs ... [21s] WARNING Error in re-building vignettes: ... * checking PDF version of manual ... OK * DONE Status: 2 ERRORs, 1 WARNING, 2 NOTEs