summary.Wald_lmm: Summary of Testing for a Linear Mixed Models

In bozenne/repeated: Repeated Measurement Models for Discrete Times

Summary of Testing for a Linear Mixed Models

Description

Estimates, p-values, and confidence intevals for linear hypothesis testing, possibly adjusted for multiple comparisons.

Usage

## S3 method for class 'Wald_lmm'
summary(
  object,
  print = TRUE,
  seed = NULL,
  columns = NULL,
  legend = TRUE,
  digits = 3,
  digits.df = 1,
  digits.p.value = 3,
  sep = ": ",
  ...
)

Arguments

object	an Wald_lmm object, output of anova.
print	[logical] should the output be printed in the console. Can be a vector of length 2 where the first element refer to the global tests and the second to the individual tests.
seed	[integer] value that will be set before adjustment for multiple comparisons to ensure reproducible results. Can also be NULL: in such a case no seed is set.
columns	[character vector] Columns to be displayed for each null hypothesis. Can be any of "type", "estimate", "se", "statistic", "df", "null", "lower", "upper", "p.value".##'
legend	[logical] should explanations about the content of the table be displayed.
digits	[interger, >0] number of digits used to display estimates.
digits.df	[interger, >0] number of digits used to display degrees-of-freedom.
digits.p.value	[interger, >0] number of digits used to display p-values.
sep	[character] character string used to separate the type of test (e.g. mean, variance) and the name of the test.
...	arguments method, level, and backtransform passed to confint.Wald_lmm

Details

By default a single step max-test adjustment adjustment is performed in presence of multiple comparisons. It is carried out either using the multcomp package (equal degrees-of-freedom, method="single-step") or using the copula package (unequal degrees-of-freedom, method="single-step2"). See the argument method of confint.Wald_lmm for other adjustments for multiple comparisons.

When considering multiple multivariate Wald tests, adjustment for multiple comparisons for the univariate Wald tests is performed within each multivariate Wald test. The number of tests ajusted for equal the first degree-of-freedom of the multivariate Wald statistic.

Adding the value "type" in argument "columns" ensures that the type of parameter that is being test (mean, variance, correlation) is output.

Value

NULL

bozenne/repeated documentation built on July 16, 2025, 11:16 p.m.