anova: Multivariate Wald Tests For Linear Mixed Model

Description Usage Arguments Details Value References Examples

Description

Perform a Wald test testing simultaneously several null hypotheses corresponding to linear combinations of the model paramaters.

Usage

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## S3 method for class 'lmm'
anova(
  object,
  effects = NULL,
  rhs = NULL,
  df = !is.null(object$df),
  ci = FALSE,
  transform.sigma = NULL,
  transform.k = NULL,
  transform.rho = NULL,
  transform.names = TRUE,
  ...
)

## S3 method for class 'anova_lmm'
confint(object, parm, level = 0.95, method = "single-step", ...)

## S3 method for class 'anova_lmm'
print(x, level = 0.95, method = "single-step", print.null = FALSE, ...)

Arguments

object

a lmm object. Only relevant for the anova function.

effects

[character] Should the Wald test be computed for all variables ("all"), or only variables relative to the mean ("mean" or "fixed"), or only variables relative to the variance structure ("variance"), or only variables relative to the correlation structure ("correlation"). Can also be use to specify linear combinations of coefficients, similarly to the linfct argument of the multcomp::glht function.

rhs

[numeric vector] the right hand side of the hypothesis. Only used when the argument effects is a matrix.

df

[logical] Should a F-distribution be used to model the distribution of the Wald statistic. Otherwise a chi-squared distribution is used.

ci

[logical] Should a confidence interval be output for each hypothesis?

transform.sigma, transform.k, transform.rho, transform.names

are passed to the vcov method. See details section in coef.lmm.

...

Not used. For compatibility with the generic method.

parm

Not used. For compatibility with the generic method.

level

[numeric, 0-1] nominal coverage of the confidence intervals.

method

[character] type of adjustment for multiple comparisons: one of "none", "bonferroni", "single-step". Not relevant for the global test (F-test or Chi-square test) - only relevant when testing each hypothesis and adjusting for multiplicity.

x

an anova_lmm object. Only relevant for print and confint functions.

print.null

[logical] should the null hypotheses be printed in the console?

Details

By default confidence intervals and p-values are adjusted based on the distribution of the maximum-statistic. This is refered to as a single-step Dunnett multiple testing procedures in table II of Dmitrienko et al. (2013) and is performed using the multcomp package with the option test = adjusted("single-step").

Value

A list of matrices containing the following columns:

as well as an attribute contrast containing the contrast matrix encoding the linear combinations of coefficients (in columns) for each hypothesis (in rows).

References

Dmitrienko, A. and D'Agostino, R., Sr (2013), Traditional multiplicity adjustment methods in clinical trials. Statist. Med., 32: 5172-5218. https://doi.org/10.1002/sim.5990.

Examples

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## simulate data in the long format
set.seed(10)
dL <- sampleRem(100, n.times = 3, format = "long")

## fit Linear Mixed Model
eUN.lmm <- lmm(Y ~ X1 + X2 + X5, repetition = ~visit|id, structure = "UN", data = dL)

## chi-2 test
anova(eUN.lmm, df = FALSE)

## F-test
anova(eUN.lmm)
anova(eUN.lmm, effects = "all")
anova(eUN.lmm, effects = c("X1=0","X2+X5=10"), ci = TRUE)

LMMstar documentation built on Nov. 5, 2021, 1:07 a.m.