coef.rbindWald_lmm: Extract Coefficients From Combined Wald Tests Applied to...

In bozenne/repeated: Repeated Measurement Models for Discrete Times

Extract Coefficients From Combined Wald Tests Applied to Linear Mixed Models

Description

Combine estimated values across linear contrasts applied on parameters from different linear mixed models.

Usage

## S3 method for class 'rbindWald_lmm'
coef(
  object,
  effects = "Wald",
  method = "none",
  ordering = NULL,
  transform.names = TRUE,
  backtransform = NULL,
  simplify = TRUE,
  ...
)

Arguments

object	a rbindWald_lmm object.
effects	[character] should the linear contrasts involved in the Wald test be output ("Wald"), or the value of the linear mixed model parameters ("all")?
method	[character vector] should the estimated value for the linear contrasts be output (one of "none", "bonferroni", ..., "single-step2") and/or pooled linear contrast estimate(s) ("average", "pool.se", "pool.gls", "pool.gls1", "pool.rubin", "p.rejection")? Only relevant when effects = "Wald".
ordering	[character] should the output be ordered by name of the linear contrast ("contrast") or by model ("model").
transform.names	[logical] should the name of the coefficients be updated to reflect the transformation that has been used? Only relevant when effects="all".
backtransform	[logical] should the estimates be back-transformed?
simplify	[logical] should the output be a vector or a list with one element specific to each possible ordering (i.e. contrast or model). Only relevant when argument method refers to multiple comparisons and not to a pooling method.
...	Not used. For compatibility with the generic method.

Details

Argument effects: when evaluating the proportion of rejected hypotheses (effects="p.rejection") a "single-step" method will be used by default to evaluate the critical quantile. This can be changed by adding adjustment method, e.g. effects=c("bonferronin","p.rejection", in the argument.

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