Running 'effect-tests-1.R' [2s]
  Running 'effect-tests-2.R' [4s]
  Running 'regression-tests.R' [154s]
Running the tests in 'tests/regression-tests.R' failed.
Complete output:
  > regression.tests <- FALSE
  > 
  > if (regression.tests && require("carData") && require("effects")){
  + 
  +   load("regression-tests.RData")
  +   
  +   regressionTest <- function(old, new){
  +     test.name <- deparse(substitute(new))
  +     if (!isTRUE(all.equal(old, as.data.frame(new), tolerance=1e-5))) {
  +       stop("failed regression test ", test.name)
  +     } else {
  +       cat("\n", test.name, ": OK", sep="")
  +     }
  +   }
  +   
  +   m.lm <- lm(prestige~(income + education)*type, data=Duncan)
  +   eff.lm <- Effect(c("income", "type"), m.lm)
  +   regressionTest(df.lm, eff.lm)
  +   
  +   m.glm <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial)
  +   eff.glm <- Effect(c("neuroticism", "extraversion"), m.glm)
  +   regressionTest(df.glm, eff.glm)
  +   
  +   m.mlm <- lm(cbind(post.test.1, post.test.2, post.test.3) ~ 
  +                 group*(pretest.1 + pretest.2), data = Baumann)
  +   eff.mlm <- Effect(c("group", "pretest.1"), m.mlm)
  +   regressionTest(df.mlm, eff.mlm)
  +   
  +   if (require(nnet)){
  +     m.multinom <- multinom(vote ~ age + gender + economic.cond.national +
  +                            economic.cond.household + Blair + Hague + Kennedy +
  +                            Europe*political.knowledge, data=BEPS)
  +     
  +     eff.multinom <- Effect(c("Europe", "political.knowledge"), m.multinom,
  +                 xlevels=list(political.knowledge=0:3))
  +     regressionTest(df.multinom, eff.multinom)
  +   }
  +   
  +   if (require(MASS)){
  +     m.polr <- polr(poverty ~ gender + religion + degree + country*poly(age,3),
  +                     data=WVS)
  +     
  +     eff.polr <- Effect(c("country", "age"), m.polr)
  +     regressionTest(df.polr, eff.polr)
  +   }
  +   
  +   if (require(nlme)){
  +     m.lme <- lme(distance ~ age*Sex, data = Orthodont, random = ~ 1)
  +     eff.lme <- Effect(c("age", "Sex"), m.lme)
  +     m.gls <- gls(Employed ~ GNP*Population,
  +         correlation=corAR1(form= ~ Year), data=longley)
  +     eff.gls <- Effect(c("GNP", "Population"), m.gls)
  +     regressionTest(df.gls, eff.gls)
  +   }
  +   
  +   if (require(lme4) && require(nlme)){
  +      data("Orthodont", package="nlme")
  +      m.lmer <- lmer(distance ~ age*Sex + (1 |Subject), data = Orthodont)
  +      eff.lmer <- Effect(c("age", "Sex"), m.lmer)
  +      regressionTest(df.lmer, eff.lmer)
  +   }
  +   
  +   if (require(lme4)){
  +     m.glmer <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
  +                 data = cbpp, family = binomial)
  +     eff.glmer <- Effect("period", m.glmer)
  +     regressionTest(df.glmer, eff.glmer)
  +   }
  +   
  +   if (require(lme4) && require(robustlmm)){
  +       m.rlmer <- rlmer(distance ~ age*Sex + (1 |Subject), data = Orthodont)
  +       eff.rlmer <- Effect(c("age", "Sex"), m.rlmer)
  +       regressionTest(df.rlmer, eff.rlmer)
  +   }
  +   
  +   if (require(betareg) && require(lme4)){
  +      data("GasolineYield", package = "betareg")
  +      m.betareg <- betareg(yield ~ batch*temp, data = GasolineYield)
  +      eff.betareg <- Effect(c("batch", "temp"), m.betareg)
  +      regressionTest(df.betareg, eff.betareg)
  +   }
  +   
  +   if (require(ordinal) && require(MASS)){
  +     m.clm <- clm(poverty ~ gender + religion + degree + country*poly(age,3),
  +         data=WVS)
  +     eff.clm <- Effect(c("country", "age"), m.clm)
  +     regressionTest(df.clm, eff.clm)
  +     
  +     m.clm2 <- clm2(poverty ~ gender + religion + degree + country*poly(age,3),
  +                  data=WVS)
  +     eff.clm2 <- Effect(c("country", "age"), m.clm2)
  +     regressionTest(df.clm2, eff.clm2)
  +     
  +     m.clmm <- clmm(SURENESS ~ PROD + (1|RESP) + (1|RESP:PROD), 
  +                 data = soup, link = "logit", threshold = "flexible")
  +     eff.clmm <- Effect("PROD", m.clmm)
  +     regressionTest(df.clmm, eff.clmm)
  +   }
  +   
  +   if (require(poLCA)){
  +     set.seed(123)
  +     data(election, package="poLCA")
  +     f <- cbind(MORALG,CARESG,KNOWG,LEADG,DISHONG,INTELG,
  +                MORALB,CARESB,KNOWB,LEADB,DISHONB,INTELB)~PARTY
  +     m.poLCA <- poLCA(f, election, nclass=3, nrep=5)
  +     eff.poLCA <- Effect("PARTY", m.poLCA)
  +     regressionTest(df.poLCA, eff.poLCA)
  +   }
  +   
  +   if (require(survey)){
  +     data(api, package="survey")
  +     dstrat <-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
  +     m.svyglm <- svyglm(api00~ell*meals+mobility, design=dstrat)
  +     eff.svyglm <- Effect(c("ell", "meals"), m.svyglm)
  +     regressionTest(df.svyglm, eff.svyglm)
  +   }
  +   
  +   if(require(nlme) && require(MASS)){
  +     m.glmmPQL <- glmmPQL(y ~ trt*I(week > 2), random = ~ 1 | ID,
  +                     family = binomial, data = bacteria)
  +     eff.glmmPQL <- Effect(c("trt", "week"), m.glmmPQL)
  +     regressionTest(df.glmmPQL, eff.glmmPQL)
  +   }
  + 
  + }
  > 
  > # the following commented-out code creates the comparison objects:
  > #
  > # df.betareg <- as.data.frame(eff.betareg )
  > # df.clm <- as.data.frame(eff.clm) 
  > # df.clm2 <- as.data.frame(eff.clm2)
  > # df.clmm <- as.data.frame(eff.clmm) 
  > # df.glm <- as.data.frame(eff.glm) 
  > # df.glmer <- as.data.frame(eff.glmer) 
  > # df.gls <- as.data.frame(eff.gls) 
  > # df.lm <- as.data.frame(eff.lm) 
  > # df.lme <- as.data.frame(eff.lme) 
  > # df.lmer <- as.data.frame(eff.lmer) 
  > # df.mlm <- as.data.frame(eff.mlm) 
  > # df.multinom <- as.data.frame(eff.multinom) 
  > # df.poLCA <- as.data.frame(eff.poLCA) 
  > # df.polr <- as.data.frame(eff.polr) 
  > # df.rlmer <- as.data.frame(eff.rlmer) 
  > # df.svyglm <- as.data.frame(eff.svyglm)
  > # df.glmmPQL <- as.data.frame(eff.glmmPQL)
  > # 
  > # 
  > # save(df.betareg, df.clm, df.clm2, df.clmm, df.glm,
  > #      df.glmer, df.gls, df.lm, df.lme, df.lmer, df.mlm,
  > #      df.multinom, df.poLCA, df.polr, df.rlmer, df.svyglm,
  > #      df.glmmPQL,
  > #      file="regression-tests.RData")
  > 
  > # To add to regression tests first
  > # load("regression-tests.RData")
  > 
  > proc.time()
     user  system elapsed 
     0.25    0.07    0.26