model.tables.mlmm: Statistical Inference and parametrization of Multiple Linear...

In bozenne/repeated: Repeated Measurement Models for Discrete Times

Statistical Inference and parametrization of Multiple Linear Mixed Model

Description

Combine estimated parameters with their uncertainty (standard errors, degrees-of-freedom, confidence intervals and p-values) from group-specific linear mixed models or a table describing each parameter (type, associated sigma or k parameter, ...).

Usage

## S3 method for class 'mlmm'
model.tables(
  x,
  parm = NULL,
  level = 0.95,
  method = NULL,
  df = NULL,
  columns = NULL,
  backtransform = NULL,
  ordering = "parameter",
  ...
)

Arguments

x	a mlmm object.
method	[character] type of adjustment for multiple comparisons, one of "none", "bonferroni", ..., "fdr", "single-step", "single-step2". and/or method(s) to pool the estimates, one of "average", "pool.se", "pool.gls", "pool.gls1", "pool.rubin". Only relevant when effects = "Wald".
...	arguments to be passed to confint.lmm.
effects	[character] Should the CIs/p-values for all coefficients be output ("all"), or only for mean coefficients ("mean" or "fixed"), or only for variance coefficients ("variance"), or only for correlation coefficients ("correlation"). Alternatively can be "param" to output the name and characteristics of each parameter (type, strata, ...).
simplify	[logical] omit from the output the backtransform attribute. Not relevant when the argument effects="param",

Details

When effects is not "param", this function simply calls confint with a specific value for the argument column.

Value

A data.frame object.

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