Running ‘testthat.R’ [99s/385s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(insight)
  > 
  > if (length(strsplit(packageDescription("insight")$Version, "\\.")[[1]]) > 3) {
  +   Sys.setenv("RunAllinsightTests" = "yes")
  + } else {
  +   Sys.setenv("RunAllinsightTests" = "no")
  + }
  > 
  > test_check("insight")
  ── 1. Failure: get_response (@test-GLMMadaptive.R#98)  ─────────────────────────
  get_response(m) not identical to fish$count.
  target is NULL, current is numeric
  
  Warning: Data frame is empty, probably you have an intercept-only model?
  ── 2. Failure: get_predictors (@test-GLMMadaptive.R#102)  ──────────────────────
  colnames(get_predictors(m)) not identical to c("child", "camper", "livebait").
  target is NULL, current is character
  
  ── 3. Failure: get_random (@test-GLMMadaptive.R#106)  ──────────────────────────
  colnames(get_random(m)) not identical to "persons".
  target is NULL, current is character
  
  ── 4. Failure: get_data (@test-GLMMadaptive.R#110)  ────────────────────────────
  colnames(get_data(m)) not identical to c("count", "child", "camper", "livebait", "persons").
  target is NULL, current is character
  
  ── 5. Failure: get_data (@test-GLMMadaptive.R#111)  ────────────────────────────
  colnames(get_data(m, effects = "fixed")) not identical to c("count", "child", "camper", "livebait").
  target is NULL, current is character
  
  ── 6. Failure: get_data (@test-GLMMadaptive.R#112)  ────────────────────────────
  colnames(get_data(m, effects = "random")) not identical to "persons".
  target is NULL, current is character
  
  ── 7. Failure: get_data (@test-GLMMadaptive.R#113)  ────────────────────────────
  colnames(get_data(m, component = "zi")) not identical to c("count", "child", "livebait", "persons").
  target is NULL, current is character
  
  ── 8. Failure: get_data (@test-GLMMadaptive.R#114)  ────────────────────────────
  colnames(get_data(m, component = "zi", effects = "fixed")) not identical to c("count", "child", "livebait").
  target is NULL, current is character
  
  ── 9. Failure: get_data (@test-GLMMadaptive.R#115)  ────────────────────────────
  colnames(get_data(m, component = "zi", effects = "random")) not identical to "persons".
  target is NULL, current is character
  
  ── 10. Failure: get_data (@test-GLMMadaptive.R#116)  ───────────────────────────
  colnames(get_data(m, component = "cond")) not identical to c("count", "child", "camper", "persons").
  target is NULL, current is character
  
  ── 11. Failure: get_data (@test-GLMMadaptive.R#117)  ───────────────────────────
  colnames(get_data(m, component = "cond", effects = "fixed")) not identical to c("count", "child", "camper").
  target is NULL, current is character
  
  ── 12. Failure: get_data (@test-GLMMadaptive.R#118)  ───────────────────────────
  colnames(get_data(m, component = "cond", effects = "random")) not identical to "persons".
  target is NULL, current is character
  
  ── 13. Failure: get_data (@test-GLMMadaptive.R#119)  ───────────────────────────
  colnames(get_data(m, component = "disp")) not identical to "count".
  target is NULL, current is character
  
  ── 14. Failure: get_data (@test-GLMMadaptive.R#121)  ───────────────────────────
  colnames(get_data(m3)) not identical to c("incidence", "size", "period", "herd").
  target is NULL, current is character
  
  GAM s.wam loop 1: deviance = 66.42095 
  GAM s.wam loop 2: deviance = 63.77252 
  GAM s.wam loop 3: deviance = 63.25199 
  GAM s.wam loop 4: deviance = 63.13399 
  GAM s.wam loop 5: deviance = 63.11016 
  GAM s.wam loop 6: deviance = 63.10748 
  GAM s.wam loop 7: deviance = 63.10727 
  GAM s.wam loop 8: deviance = 63.10725 
  GAM s.wam loop 9: deviance = 63.10725 
  Model was fitted with uninformative (flat) priors!
  Gu & Wahba 4 term additive model
  GAMLSS-RS iteration 1: Global Deviance = 4771.925 
  GAMLSS-CG iteration 1: Global Deviance = 4771.013 
  GAMLSS-CG iteration 2: Global Deviance = 4770.994 
  GAMLSS-CG iteration 3: Global Deviance = 4770.994 
  4 term additive + random effectGu & Wahba 4 term additive model
  
   Maximum number of PQL iterations:  20 
  Gu & Wahba 4 term additive model
  (Intercept)    tensionM    tensionH 
     36.38889   -10.00000   -14.72222 
  Warning: Data frame is empty, probably component 'all' does not exist in the random-part of the model?
  Warning: Data frame is empty, probably you have an intercept-only model?
  Warning: Data frame is empty, probably component 'dispersion' does not exist in the random-part of the model?
  Estimating SMNL model 
  Fitting one lmer() model. [DONE]
  Calculating p-values. [DONE]
  Fitting one lmer() model. [DONE]
  Calculating p-values. [DONE]
  # weights:  6 (5 variable)
  initial  value 131.004817 
  iter  10 value 108.535568
  final  value 108.535541 
  converged
  VGLM    linear loop  1 :  deviance = 5.181115
  VGLM    linear loop  2 :  deviance = 5.129147
  VGLM    linear loop  3 :  deviance = 5.129141
  VGLM    linear loop  4 :  deviance = 5.129141
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 1774 SKIPPED: 7 WARNINGS: 69 FAILED: 14
  1. Failure: get_response (@test-GLMMadaptive.R#98) 
  2. Failure: get_predictors (@test-GLMMadaptive.R#102) 
  3. Failure: get_random (@test-GLMMadaptive.R#106) 
  4. Failure: get_data (@test-GLMMadaptive.R#110) 
  5. Failure: get_data (@test-GLMMadaptive.R#111) 
  6. Failure: get_data (@test-GLMMadaptive.R#112) 
  7. Failure: get_data (@test-GLMMadaptive.R#113) 
  8. Failure: get_data (@test-GLMMadaptive.R#114) 
  9. Failure: get_data (@test-GLMMadaptive.R#115) 
  1. ...
  
  Error: testthat unit tests failed
  Execution halted