R Under development (unstable) (2026-01-07 r89288 ucrt) -- "Unsuffered Consequences"
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> # Additional tests for interop-car.R which don't work well with testthat::test_that
> 
> library(mmrm)
> library(testthat)
> 
> # Type 3 tests are compatible with nlme::gls even if only interaction term exists
> 
> if (require("car", quietly = TRUE)) {
+   fev_data_complete <- na.omit(fev_data)
+ 
+   fev_data_complete_gls <- fev_data_complete
+   contrasts(fev_data_complete_gls$AVISIT) <- contr.sum(length(levels(
+     fev_data_complete_gls$AVISIT
+   )))
+   contrasts(fev_data_complete_gls$RACE) <- contr.sum(length(levels(
+     fev_data_complete_gls$RACE
+   )))
+   contrasts(fev_data_complete_gls$ARMCD) <- contr.sum(length(levels(
+     fev_data_complete_gls$ARMCD
+   )))
+ 
+   mod1 <- mmrm(
+     formula = FEV1 ~ FEV1_BL:AVISIT - 1 + ar1(AVISIT | USUBJID),
+     data = fev_data_complete
+   )
+   result1 <- car::Anova(mod1, type = "3", test.statistic = "Chisq")
+ 
+   mod1_gls <- nlme::gls(
+     FEV1 ~ FEV1_BL:AVISIT - 1,
+     correlation = nlme::corAR1(form = ~ as.numeric(AVISIT) | USUBJID),
+     data = fev_data_complete_gls,
+     method = "REML"
+   )
+   result1_gls <- car::Anova(mod1_gls, type = "3")
+ 
+   expect_equal(result1$Chisq, result1_gls$Chisq, tolerance = 1e-6)
+   expect_equal(result1$Df, result1_gls$Df)
+ 
+   mod2 <- mmrm(
+     formula = FEV1 ~ AVISIT + AVISIT:RACE + FEV1_BL + us(AVISIT | USUBJID),
+     data = fev_data
+   )
+   result2 <- car::Anova(mod2, type = "3", test.statistic = "Chisq")
+ 
+   mod2_gls <- nlme::gls(
+     FEV1 ~ AVISIT + AVISIT:RACE + FEV1_BL,
+     correlation = nlme::corSymm(form = ~ as.numeric(AVISIT) | USUBJID),
+     weights = nlme::varIdent(form = ~ 1 | as.numeric(AVISIT)),
+     data = fev_data_complete_gls,
+     method = "REML"
+   )
+   result2_gls <- car::Anova(mod2_gls, type = "3")
+ 
+   # We don't get the same fit here therefore results are a little bit different.
+   expect_equal(
+     as.numeric(logLik(mod2)),
+     as.numeric(logLik(mod2_gls)),
+     tolerance = 1e-2
+   )
+   expect_equal(result2$Chisq, result2_gls$Chisq[-1], tolerance = 1e-3)
+ 
+   # Check simplest example in more detail and compare with nlme and lme4 result.
+   mod3 <- mmrm(
+     FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
+     data = fev_data
+   )
+   result3 <- car::Anova(mod3, type = "3", test.statistic = "Chisq")
+ 
+   mod3_gls <- nlme::gls(
+     FEV1 ~ RACE + SEX + ARMCD * AVISIT,
+     correlation = nlme::corSymm(form = ~ as.numeric(AVISIT) | USUBJID),
+     weights = nlme::varIdent(form = ~ 1 | as.numeric(AVISIT)),
+     data = fev_data_complete_gls,
+     method = "REML"
+   )
+   result3_gls <- car::Anova(mod3_gls, type = "3")
+ 
+   expect_equal(
+     as.numeric(logLik(mod3)),
+     as.numeric(logLik(mod3_gls)),
+     tolerance = 1e-2
+   )
+   expect_equal(result3$Chisq, result3_gls$Chisq[-1], tolerance = 1e-4)
+ 
+   if (require("lmerTest", quietly = TRUE)) {
+     mod3_lmer_kr <- lmerTest::lmer(
+       FEV1 ~ RACE + SEX + ARMCD * AVISIT + (0 + AVISIT | USUBJID),
+       data = fev_data_complete_gls,
+       control = lme4::lmerControl(
+         check.nobs.vs.nRE = "ignore",
+         check.conv.grad = list(action = "warning", tol = 2e-2, relTol = NULL),
+       )
+     )
+     mod3_kr <- mmrm(
+       FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
+       data = fev_data,
+       method = "Kenward-Roger"
+     )
+     expect_equal(
+       as.numeric(logLik(mod3_lmer_kr)),
+       as.numeric(logLik(mod3_kr)),
+       tolerance = 1e-2
+     )
+     result3_kr <- car::Anova(mod3_kr, type = "3", test.statistic = "F")
+     result3_lmer_kr <- car::Anova(
+       mod3_lmer_kr,
+       type = "3",
+       test.statistic = "F"
+     )
+     expect_equal(
+       result3_kr$F,
+       result3_lmer_kr$F[-1],
+       tolerance = 1e-1
+     )
+     expect_equal(
+       result3_kr$Res.Df,
+       result3_lmer_kr$Df.res[-1],
+       tolerance = 1e-1
+     )
+   }
+ }
mmrm() registered as car::Anova extension

Attaching package: 'lmerTest'

The following object is masked from 'package:lme4':

    lmer

The following object is masked from 'package:stats':

    step

Warning message:
In h_contr_sum_type3_contrasts(object) :
  Type III tests can give misleading results for models without an intercept
> 
> # Type 3 tests are compatible with nlme::gls for higher-order interaction
> 
> if (require("car", quietly = TRUE)) {
+   fev_data_complete <- na.omit(fev_data)
+ 
+   fev_data_complete_gls <- fev_data_complete
+   contrasts(fev_data_complete_gls$AVISIT) <- contr.sum(length(levels(
+     fev_data_complete_gls$AVISIT
+   )))
+   contrasts(fev_data_complete_gls$RACE) <- contr.sum(length(levels(
+     fev_data_complete_gls$RACE
+   )))
+   contrasts(fev_data_complete_gls$ARMCD) <- contr.sum(length(levels(
+     fev_data_complete_gls$ARMCD
+   )))
+ 
+   mod <- mmrm(
+     formula = FEV1 ~
+       ARMCD +
+       RACE +
+       AVISIT +
+       RACE * AVISIT * ARMCD +
+       FEV1_BL +
+       ar1(AVISIT | USUBJID),
+     data = fev_data_complete
+   )
+   result <- car::Anova(mod, type = "3", test.statistic = "Chisq")
+ 
+   mod_gls <- nlme::gls(
+     FEV1 ~
+       ARMCD +
+       RACE +
+       AVISIT +
+       RACE * AVISIT * ARMCD +
+       FEV1_BL,
+     correlation = nlme::corAR1(form = ~ as.numeric(AVISIT) | USUBJID),
+     data = fev_data_complete_gls,
+     method = "REML"
+   )
+   result_gls <- car::Anova(mod_gls, type = "3")
+ 
+   expect_equal(
+     as.numeric(logLik(mod)),
+     as.numeric(logLik(mod_gls)),
+     tolerance = 1e-1
+   )
+   expect_equal(result$Chisq, result_gls$Chisq[-1], tolerance = 1e-5)
+ }
> 
> proc.time()
   user  system elapsed 
   7.53    0.62    8.14