Running ‘testthat.R’ [66s/86s]
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: 0.640323 seconds (Warm-up)
                 0.58164 seconds (Sampling)
                 1.22196 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 3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.682738 seconds (Warm-up)
                 0.488199 seconds (Sampling)
                 1.17094 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
  
  Gradient evaluation took 4.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.67476 seconds (Warm-up)
                 0.481502 seconds (Sampling)
                 1.15626 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: 0.680148 seconds (Warm-up)
                 0.516977 seconds (Sampling)
                 1.19712 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0.000108 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.08 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 3.61393 seconds (Warm-up)
                 3.85033 seconds (Sampling)
                 7.46426 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 3.67831 seconds (Warm-up)
                 3.29427 seconds (Sampling)
                 6.97257 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  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: 3.60486 seconds (Warm-up)
                 3.37697 seconds (Sampling)
                 6.98183 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  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: 3.86941 seconds (Warm-up)
                 2.68249 seconds (Sampling)
                 6.5519 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 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: 0.141327 seconds (Warm-up)
                 0.149431 seconds (Sampling)
                 0.290758 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
  
  Gradient evaluation took 3.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.139136 seconds (Warm-up)
                 0.162443 seconds (Sampling)
                 0.301579 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
  
  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: 0.135303 seconds (Warm-up)
                 0.132018 seconds (Sampling)
                 0.267321 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
  
  Gradient evaluation took 3.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.32 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.130213 seconds (Warm-up)
                 0.127226 seconds (Sampling)
                 0.257439 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  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: 0.107032 seconds (Warm-up)
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                 0.204656 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  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: 0.10874 seconds (Warm-up)
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                 0.205159 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.096968 seconds (Warm-up)
                 0.100563 seconds (Sampling)
                 0.197531 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' 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: 0.12511 seconds (Warm-up)
                 0.106256 seconds (Sampling)
                 0.231366 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 7e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.7 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.287474 seconds (Warm-up)
                 0.282799 seconds (Sampling)
                 0.570273 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 3.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.282404 seconds (Warm-up)
                 0.288945 seconds (Sampling)
                 0.571349 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 6.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.69 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.295631 seconds (Warm-up)
                 0.271055 seconds (Sampling)
                 0.566686 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 6.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.2642 seconds (Warm-up)
                 0.268605 seconds (Sampling)
                 0.532805 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