model.tables.Wald_lmm: Statistical Inference for Wald tests Applied to a Linear...

In bozenne/repeated: Repeated Measurement Models for Discrete Times

Statistical Inference for Wald tests Applied to a Linear Mixed Model

Description

Export estimates, standard errors, degrees-of-freedom, confidence intervals (CIs) and p-values relative to linear contrasts of parameters from a linear mixed model.

Usage

## S3 method for class 'Wald_lmm'
model.tables(
  x,
  effects = "Wald",
  level = 0.95,
  df = NULL,
  method = NULL,
  columns = NULL,
  backtransform = NULL,
  transform.names = TRUE,
  simplify = TRUE,
  ...
)

Arguments

x	a Wald_lmm object.
effects	[character] should the linear contrasts involved in the Wald test be output ("Wald"), the contrast matrix ("contrast"), or the name/value/type of the underlying mixed model parameters ("param")?
level	[numeric, 0-1] nominal coverage of the confidence intervals.
df	[logical] Should a Student's t-distribution be used to model the distribution of the Wald statistic. Otherwise a normal distribution is used.
method	[character] Should pointwise confidence intervals be output ("none") or simultaneous confidence intervals ("bonferroni", ..., "fdr", "single-step", "single-step2")?
columns	[character vector] Columns to be output. Can be any of "estimate", "se", "df", "quantile", "lower", "upper", "statistic", "null", "p.value".
backtransform	[logical] should the estimates, standard errors, and confidence intervals be backtransformed?
transform.names	[logical] should the name of the coefficients be updated to reflect the transformation that has been used? Only relevant when effects="contrast".
simplify	[logical] with argument effects="Wald", omit from the output attributes containing additional information (e.g. approximation error made when adjusting p-values). with argument effects="contrast" the output will be converted into a matrix (instead of a list of matrix) whenever possible.
...	Not used. For compatibility with the generic method.

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