confint.rbindWald_lmm: Confidence Intervals From Combined Wald Tests Applied to...

In bozenne/repeated: Repeated Measurement Models for Discrete Times

Confidence Intervals From Combined Wald Tests Applied to Linear Mixed Models

Description

Combine pointwise or simultaneous confidence intervals relative to linear contrasts of parameters from different linear mixed models. Can also output p-values (corresponding to pointwise confidence intervals) or adjusted p-values (corresponding to simultaneous confidence intervals).

Usage

## S3 method for class 'rbindWald_lmm'
confint(
  object,
  parm,
  level = 0.95,
  df = NULL,
  method = NULL,
  columns = NULL,
  ordering = NULL,
  backtransform = NULL,
  ...
)

Arguments

object	a rbindWald_lmm object.
parm	Not used. For compatibility with the generic method.
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") and/or confidence intervals for pooled linear contrast estimates ("average", "pool.se", "pool.gls", "pool.gls1", "pool.rubin", "p.rejection")? Only relevant when effects = "Wald".
columns	[character vector] Columns to be output. Can be any of "estimate", "se", "statistic", "df", "null", "lower", "upper", "p.value".
ordering	[character] should the output be ordered by name of the linear contrast ("contrast") or by model ("model").
backtransform	[logical] should the estimates be back-transformed?
...	Not used. For compatibility with the generic method.

Details

Argument method: the following pooling are available:

• "average": average estimates

• "pool.se": weighted average of the estimates, with weights being the inverse of the squared standard error.

• "pool.gls": weighted average of the estimates, with weights being based on the variance-covariance matrix of the estimates. When this matrix is singular, the Moore–Penrose inverse is used which correspond to truncate the spectral decomposition for eigenvalues below 10^{-12}.

• "pool.gls1": similar to "pool.gls" with weights shrinked toward the average whenever they exceed 1 in absolute value.

• "pool.rubin": average of the estimates and compute the uncertainty according to Rubin's rule (Barnard et al. 1999). Validity requires the congeniality condition of Meng (1994).

• "p.rejection": proportion of null hypotheses where there is evidence for an effect. By default the critical quantile (defining the level of evidence required) is evaluated using a "single-step" method but this can be changed by adding adjustment method in the argument method, e.g. effects=c("bonferronin","p.rejection").

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