confint.Wald_lmm: Confidence Intervals From Wald Tests Applied to a Linear...

In bozenne/repeated: Repeated Measurement Models for Discrete Times

Confidence Intervals From Wald Tests Applied to a Linear Mixed Model

Description

Compute pointwise or simultaneous confidence intervals relative to linear contrasts of parameters from a linear mixed model. Pointwise confidence intervals have nominal coverage w.r.t. a single contrast whereas simultaneous confidence intervals have nominal coverage w.r.t. to all contrasts. Can also output p-values (corresponding to pointwise confidence intervals) or adjusted p-values (corresponding to simultaneous confidence intervals).

Usage

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

Arguments

object	a Wald_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")?
columns	[character vector] Columns to be output. Can be any of "estimate", "se", "statistic", "df", "null", "lower", "upper", "p.value".
backtransform	[logical] should the estimates, standard errors, and confidence intervals be backtransformed?
...	Not used. For compatibility with the generic method.

Details

Available methods are:

• "none", "bonferroni", "single-step2"

• "holm", "hochberg", "hommel", "BH", "BY", "fdr": adjustment performed by [stats::p.adjust()], no confidence interval is computed.

• "single-step", "free", "Westfall", "Shaffer": adjustment performed by [multcomp::glht()], for all but the first method no confidence interval is computed.

Note: method "single-step" adjusts for multiple comparisons using equicoordinate quantiles of the multivariate Student's t-distribution over all tests, instead of the univariate quantiles. It assumes equal degrees-of-freedom in the marginal and is described in section 7.1 of Dmitrienko et al. (2013) under the name single-step Dunnett procedure. The name "single-step" is borrowed from the multcomp package. In the book Bretz et al. (2010) written by the authors of the package, the procedure is refered to as max-t tests which is the terminology adopted in the LMMstar package.
When degrees-of-freedom differs between individual hypotheses, method "single-step2" is recommended. It simulates data using copula whose marginal distributions are Student's t-distribution (with possibly different degrees-of-freedom) and elliptical copula with parameters the estimated correlation between the test statistics (via the copula package). It then computes the frequency at which the simulated maximum exceed the observed maximum and appropriate quantile of simulated maximum for the confidence interval.

References

Barnard and Rubin, Small-sample degrees of freedom with multiple imputation. Biometrika (1999), 86(4):948-955.
Dmitrienko, A. and D'Agostino, R., Sr (2013), Traditional multiplicity adjustment methods in clinical trials. Statist. Med., 32: 5172-5218. Frank Bretz, Torsten Hothorn and Peter Westfall (2010), Multiple Comparisons Using R, CRC Press, Boca Raton.

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