Exam5.3 Inference using empirical standard error with different Bias connection

References

  1. Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.

See also

Examples

data(DataSet4.1) DataSet4.1$trt <- factor(x = DataSet4.1$trt) DataSet4.1$block <- factor( x = DataSet4.1$block) ##---REML estimates on page 172 library(lme4) # library(lmerTest) Exam5.3REML <- lmer( formula = y ~ trt + (1|block) , data = DataSet4.1 , REML = TRUE # , control = lmerControl() , start = NULL # , verbose = 0L # , subset # , weights # , na.action # , offset , contrasts = NULL , devFunOnly = FALSE # , ... ) ##---Standard Error Type "Model Based" with no Bias Connection AnovaExam5.3REML <- anova( object = Exam5.3REML ) AnovaExam5.3REML
#> Analysis of Variance Table #> Df Sum Sq Mean Sq F value #> trt 14 183.41 13.1 1.5312
##---Standard Error Type "Model Based" with "Kenward-Roger approximation" Bias Connection # library(pbkrtest) anova( object = Exam5.3REML, ddf = "Kenward-Roger")
#> Analysis of Variance Table #> Df Sum Sq Mean Sq F value #> trt 14 183.41 13.1 1.5312
##---ML estimates on page 172 Exam5.3ML <- lmer( formula = y ~ trt + ( 1|block ) , data = DataSet4.1 , REML = FALSE # , control = lmerControl() , start = NULL # , verbose = 0L # , subset # , weights # , na.action # , offset , contrasts = NULL , devFunOnly = FALSE # , ... ) ##---Standard Error Type "Model Based" with no Bias Connection AnovaExam5.3ML <- anova( object = Exam5.3ML ) AnovaExam5.3ML
#> Analysis of Variance Table #> Df Sum Sq Mean Sq F value #> trt 14 175.26 12.518 2.0736
##---Standard Error Type "Model Based" with "Kenward-Roger approximation" Bias Connection anova( object = Exam5.3ML, ddf = "Kenward-Roger")
#> Analysis of Variance Table #> Df Sum Sq Mean Sq F value #> trt 14 175.26 12.518 2.0736