confint.mlmm: Confidence Intervals for Multiple Linear Mixed Models

In bozenne/repeated: Repeated Measurement Models for Discrete Times

Confidence Intervals for Multiple Linear Mixed Models

Description

Compute pointwise or simultaneous confidence intervals relative to parameter or linear contrasts of parameters from group-specific 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 'mlmm'
confint(
  object,
  parm = NULL,
  level = 0.95,
  method = NULL,
  df = NULL,
  columns = NULL,
  backtransform = NULL,
  ordering = "parameter",
  ...
)

Arguments

object	an mlmm object, output of mlmm.
parm	Not used. For compatibility with the generic method.
level	[numeric,0-1] the confidence level of the confidence intervals.
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")?
ordering	[character] should the output be ordered by type of parameter (parameter) or by model (by). Only relevant for mlmm objects.n
...	other arguments are passed to confint.Wald_lmm.

Details

Statistical inference following pooling is performed according to Rubin's rule whose validity requires the congeniality condition of Meng (1994). Pooling estimates: available methods are:

• "average": average estimates

• "pool.fixse": weighted average of the estimates, with weights being the inverse of the squared standard error. The uncertainty about the weights is neglected.

• "pool.se": weighted average of the estimates, with weights being the inverse of the squared standard error. The uncertainty about the weights is computed under independence of the variance parameters between models.

• "pool.gls": weighted average of the estimates, with weights being based on the variance-covariance matrix of the estimates. When this matrix is singular, its spectral decomposition is truncated when the correspodning eigenvalues are below 10^{-12}. The uncertainty about the weights is neglected.

• "pool.gls1": similar to "pool.gls" but ensure that the weights are at most 1 in absolute value by shrinking toward the average.

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

References

Meng X. L.(1994). Multiple-imputation inferences with uncongenial sources of input. Statist. Sci.9, 538–58.

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