Running ‘testthat.R’ [309s/416s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > if (require("testthat")) {
  +   library(ggeffects)
  + 
  +   if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
  +     Sys.setenv("RunAllggeffectsTests" = "yes")
  +   } else {
  +     Sys.setenv("RunAllggeffectsTests" = "no")
  +   }
  + 
  +   test_check("ggeffects")
  + }
  Loading required package: testthat
  Loading required package: gam
  Loading required package: splines
  Loading required package: foreach
  Loaded gam 1.20
  
  Loading required namespace: emmeans
  Loading required package: emmeans
  Loading required package: effects
  Loading required package: carData
  lattice theme set by effectsTheme()
  See ?effectsTheme for details.
  Loading required package: betareg
  Loading required package: lme4
  Loading required package: Matrix
  Loading required package: sjmisc
  Learn more about sjmisc with 'browseVignettes("sjmisc")'.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Loading required package: sjlabelled
  Loading required package: survival
  Loading required package: gee
  (Intercept)    tensionM    tensionH 
     36.38889   -10.00000   -14.72222 
  Loading required package: geepack
  Loading required package: haven
  
  Attaching package: 'haven'
  
  The following objects are masked from 'package:sjlabelled':
  
      as_factor, read_sas, read_spss, read_stata, write_sas, zap_labels
  
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Loading required package: robustbase
  
  Attaching package: 'robustbase'
  
  The following object is masked from 'package:survival':
  
      heart
  
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Can't compute estimated marginal means, 'emmeans::emmeans()' returned an error.
  
  Reason: Can't handle an object of class  "glmrob" 
   Use help("models", package = "emmeans") for information on supported models.
  You may try 'ggpredict()' or 'ggeffect()'.
  
  Loading required package: nlme
  
  Attaching package: 'nlme'
  
  The following object is masked from 'package:lme4':
  
      lmList
  
  Can't compute estimated marginal means, 'emmeans::emmeans()' returned an error.
  
  Reason: Can't handle an object of class  "lmrob" 
   Use help("models", package = "emmeans") for information on supported models.
  You may try 'ggpredict()' or 'ggeffect()'.
  
  Loading required package: logistf
  Can't compute estimated marginal means, 'emmeans::emmeans()' returned an error.
  
  Reason: Can't handle an object of class  "logistf" 
   Use help("models", package = "emmeans") for information on supported models.
  You may try 'ggpredict()' or 'ggeffect()'.
  
  Loading required package: rms
  Loading required package: Hmisc
  Loading required package: lattice
  Loading required package: Formula
  Loading required package: ggplot2
  
  Attaching package: 'ggplot2'
  
  The following object is masked from 'package:sjlabelled':
  
      as_label
  
  
  Attaching package: 'Hmisc'
  
  The following object is masked from 'package:sjmisc':
  
      %nin%
  
  The following object is masked from 'package:testthat':
  
      describe
  
  The following objects are masked from 'package:base':
  
      format.pval, units
  
  Loading required package: SparseM
  
  Attaching package: 'SparseM'
  
  The following object is masked from 'package:base':
  
      backsolve
  
  
  Attaching package: 'rms'
  
  The following object is masked from 'package:emmeans':
  
      contrast
  
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Can't compute marginal effects, 'effects::Effect()' returned an error.
  
  Reason: object 'neg_c_7d' not found
  You may try 'ggpredict()' or 'ggemmeans()'.
  
  Data were 'prettified'. Consider using `terms="c12hour [all]"` to get smooth plots.
  Loading required package: MASS
  Loading required package: pscl
  Classes and Methods for R developed in the
  Political Science Computational Laboratory
  Department of Political Science
  Stanford University
  Simon Jackman
  hurdle and zeroinfl functions by Achim Zeileis
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  
  Re-fitting to get Hessian
  
  Loading required package: glmmTMB
  
   *** caught segfault ***
  address 4, cause 'memory not mapped'
  
  Traceback:
   1: retape(set.defaults = TRUE)
   2: MakeADFun(data.tmb, parameters, map = mapArg, random = randomArg,     profile = NULL, silent = !verbose, DLL = "glmmTMB")
   3: eval(substitute(expr), data, enclos = parent.frame())
   4: eval(substitute(expr), data, enclos = parent.frame())
   5: with.default(TMBStruc, MakeADFun(data.tmb, parameters, map = mapArg,     random = randomArg, profile = NULL, silent = !verbose, DLL = "glmmTMB"))
   6: with(TMBStruc, MakeADFun(data.tmb, parameters, map = mapArg,     random = randomArg, profile = NULL, silent = !verbose, DLL = "glmmTMB"))
   7: fitTMB(TMBStruc)
   8: glmmTMB(count ~ spp + poly(cover, 3) + mined + (1 | site), ziformula = ~DOY,     dispformula = ~spp, data = Salamanders, family = nbinom2)
   9: eval(code, test_env)
  10: eval(code, test_env)
  11: withCallingHandlers({    eval(code, test_env)    if (!handled && !is.null(test)) {        skip_empty()    }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,     message = handle_message, error = handle_error)
  12: doTryCatch(return(expr), name, parentenv, handler)
  13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  14: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
  15: doTryCatch(return(expr), name, parentenv, handler)
  16: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),     names[nh], parentenv, handlers[[nh]])
  17: tryCatchList(expr, classes, parentenv, handlers)
  18: tryCatch(withCallingHandlers({    eval(code, test_env)    if (!handled && !is.null(test)) {        skip_empty()    }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,     message = handle_message, error = handle_error), error = handle_fatal,     skip = function(e) {    })
  19: test_code(NULL, exprs, env)
  20: source_file(path, child_env(env), wrap = wrap)
  21: FUN(X[[i]], ...)
  22: lapply(test_paths, test_one_file, env = env, wrap = wrap)
  23: force(code)
  24: doWithOneRestart(return(expr), restart)
  25: withOneRestart(expr, restarts[[1L]])
  26: withRestarts(testthat_abort_reporter = function() NULL, force(code))
  27: with_reporter(reporters$multi, lapply(test_paths, test_one_file,     env = env, wrap = wrap))
  28: test_files(test_dir = test_dir, test_package = test_package,     test_paths = test_paths, load_helpers = load_helpers, reporter = reporter,     env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,     wrap = wrap, load_package = load_package)
  29: test_files(test_dir = path, test_paths = test_paths, test_package = package,     reporter = reporter, load_helpers = load_helpers, env = env,     stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,     wrap = wrap, load_package = load_package, parallel = parallel)
  30: test_dir("testthat", package = package, reporter = reporter,     ..., load_package = "installed")
  31: test_check("ggeffects")
  An irrecoverable exception occurred. R is aborting now ...