anova: Multivariate Wald Tests For Linear Mixed Model In LMMstar: Repeated Measurement Models for Discrete Times

Description

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

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```## 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:

• `null`: null hypothesis

• `statistic`: value of the test statistic

• `df.num`: degrees of freedom for the numerator (i.e. number of hypotheses)

• `df.denom`: degrees of freedom for the denominator (i.e. Satterthwaite approximation)

• `p.value`: p-value.

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## 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.