Package index • pbkrtest

Model comparison ~
`KRmodcomp()`	F-test and degrees of freedom based on Kenward-Roger approximation
`PBrefdist()`	Calculate reference distribution using parametric bootstrap
`PBmodcomp()` `seqPBmodcomp()`	Model comparison using parametric bootstrap methods.
`SATmodcomp()`	F-test and degrees of freedom based on Satterthwaite approximation
`X2modcomp()`	Chisq test
Data sets ~
`beets`	Sugar beets data
`budworm`	Budworm data
Others These are functions not mentioned above.
`anovax()` `print(<anovax>)`	anova like function
`anovax_list()`	Various different tests for model comparison
`as.data.frame(<PBmodcomp>)` `as.data.frame(<summary_PBmodcomp>)`	Coerce PBmodcomp objects to data frames
`compare_column_space()`	Compare column spaces
`cov_matrix()`	Compute Marginal Covariance Matrix of the Response
`fun1()`	fun1
`fun2()`	fun2
`getLRT()`	Likelihood Ratio Test Between Nested Models
`get_Lb_ddf()` `Lb_ddf()` `get_ddf_Lb()` `ddf_Lb()`	Adjusted denominator degrees of freedom for linear estimate for linear mixed model.
`getKR()` `getSAT()`	Extract (or "get") components from a `KRmodcomp` or `SATmodcomp` object.
`get_nested_model_info()`	Resolve Nested Model Representation
`internal-pbkrtest`	pbkrtest internal
`vcovAdj()`	Adjusted covariance matrix for linear mixed models according to Kenward and Roger
`model2restriction_matrix()` `restriction_matrix2model()` `make_model_matrix()` `make_restriction_matrix()`	Conversion between a model object and a restriction matrix
`pb2_modcomp()`	Parametric Bootstrap Model Comparison
`pb_refdist()` `pb_refdist_sequential()`	Parametric Bootstrap Reference Distribution
`refit(<lm>)` `refit(<lme>)` `refit(<gls>)`	Refit nlme model with New Response
`simulate(<gls>)` `simulate(<lme>)`	Simulate Response Vectors from Fitted nlme Models
`simulate0()`	Simulate Response Vectors from a Fitted Mixed Model
`summarize_pb()`	Summarize parametric bootstrap results for LRT
Complete reference
`anovax()` `print(<anovax>)`	anova like function
`anovax_list()`	Various different tests for model comparison
`as.data.frame(<PBmodcomp>)` `as.data.frame(<summary_PBmodcomp>)`	Coerce PBmodcomp objects to data frames
`compare_column_space()`	Compare column spaces
`cov_matrix()`	Compute Marginal Covariance Matrix of the Response
`beets`	Sugar beets data
`budworm`	Budworm data
`fun1()`	fun1
`fun2()`	fun2
`getLRT()`	Likelihood Ratio Test Between Nested Models
`get_Lb_ddf()` `Lb_ddf()` `get_ddf_Lb()` `ddf_Lb()`	Adjusted denominator degrees of freedom for linear estimate for linear mixed model.
`getKR()` `getSAT()`	Extract (or "get") components from a `KRmodcomp` or `SATmodcomp` object.
`get_nested_model_info()`	Resolve Nested Model Representation
`internal-pbkrtest`	pbkrtest internal
`vcovAdj()`	Adjusted covariance matrix for linear mixed models according to Kenward and Roger
`KRmodcomp()`	F-test and degrees of freedom based on Kenward-Roger approximation
`model2restriction_matrix()` `restriction_matrix2model()` `make_model_matrix()` `make_restriction_matrix()`	Conversion between a model object and a restriction matrix
`PBrefdist()`	Calculate reference distribution using parametric bootstrap
`pb2_modcomp()`	Parametric Bootstrap Model Comparison
`PBmodcomp()` `seqPBmodcomp()`	Model comparison using parametric bootstrap methods.
`pb_refdist()` `pb_refdist_sequential()`	Parametric Bootstrap Reference Distribution
`refit(<lm>)` `refit(<lme>)` `refit(<gls>)`	Refit nlme model with New Response
`SATmodcomp()`	F-test and degrees of freedom based on Satterthwaite approximation
`simulate(<gls>)` `simulate(<lme>)`	Simulate Response Vectors from Fitted nlme Models
`simulate0()`	Simulate Response Vectors from a Fitted Mixed Model
`summarize_pb()`	Summarize parametric bootstrap results for LRT
`X2modcomp()`	Chisq test

Reference

Model comparison

Data sets

Others

Complete reference