Running ‘testthat.R’ [66s/90s]
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, p)
  9: lme4::lmer(formula = formula, data = data)
  10: eval(mc, parent.frame(1L))
  11: eval(mc, parent.frame(1L))
  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, p)
  9: lme4::lmer(formula = formula, data = data)
  10: eval(mc, parent.frame(1L))
  11: eval(mc, parent.frame(1L))
  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 5.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.53 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.627617 seconds (Warm-up)
                 0.56702 seconds (Sampling)
                 1.19464 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 2.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.700062 seconds (Warm-up)
                 0.50935 seconds (Sampling)
                 1.20941 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
  
  Gradient evaluation took 2.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.693962 seconds (Warm-up)
                 0.469144 seconds (Sampling)
                 1.16311 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
  
  Gradient evaluation took 2.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.6733 seconds (Warm-up)
                 0.516753 seconds (Sampling)
                 1.19005 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 8.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.82 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 3.45248 seconds (Warm-up)
                 3.7153 seconds (Sampling)
                 7.16777 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 5.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 3.79119 seconds (Warm-up)
                 3.46261 seconds (Sampling)
                 7.2538 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 5.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 3.62815 seconds (Warm-up)
                 3.513 seconds (Sampling)
                 7.14115 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 6.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 3.96855 seconds (Warm-up)
                 2.79268 seconds (Sampling)
                 6.76122 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 4.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.133387 seconds (Warm-up)
                 0.142201 seconds (Sampling)
                 0.275588 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
  
  Gradient evaluation took 2.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.13427 seconds (Warm-up)
                 0.151891 seconds (Sampling)
                 0.286161 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
  
  Gradient evaluation took 2.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.128906 seconds (Warm-up)
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                 0.251453 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
  
  Gradient evaluation took 2.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.128119 seconds (Warm-up)
                 0.123537 seconds (Sampling)
                 0.251656 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.094467 seconds (Warm-up)
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                 0.18722 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 2.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.102029 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 2.7e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.101807 seconds (Warm-up)
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                 0.196072 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 2.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.117473 seconds (Warm-up)
                 0.101454 seconds (Sampling)
                 0.218927 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 5.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.55 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.291993 seconds (Warm-up)
                 0.280642 seconds (Sampling)
                 0.572635 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 4.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.30072 seconds (Warm-up)
                 0.283072 seconds (Sampling)
                 0.583792 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 5.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.56 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.290997 seconds (Warm-up)
                 0.271715 seconds (Sampling)
                 0.562712 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 6.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.277736 seconds (Warm-up)
                 0.269958 seconds (Sampling)
                 0.547694 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<b0>).
  
  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