Permutation test inference for generalised linear mixed models including corrections for multiple testing. The package was originally named after the permutation
based stepdown procedure of Romano and Wolf (2005), but has expanded to include Bonferroni, Holm's stepdown method, and no correction at all. The package provides
functions to estimate p-values and confidence intervals using models fits from either lme4
, glm
, or lm
in R. The permutational test statistic is either unweigted
or weighted by the covariance matrix of observations as described by Braun and Feng (2001). Confidence intervals are estimated using an iterative search procedure. All
the methods are described in Watson et al (2021).
As an example of usage the user can fit two models using lme4
. The models can be of different families.
fit1 <- lme4::lmer(y1 ~ treat + x1 + x2, data=data)
fit2 <- lme4::glmer(y2 ~ treat + x1 + x2, data= data)
Then these can be passed to the stepdown function:
stepdown(fitlist=list(fit1,fit2),
data=data,
n_permute = 1000,
nsteps=1000,
plots=TRUE,
verbose=TRUE,
type = "rw")
where type can be rw
for Romano-Wolf, h
or hr
for standard Holm and Holm using permutation tests respectively, b
or br
for standard Bonferroni or Bonferroni
using permutation tests, and none
for no correction for multiple testing. To use the weighted statistic the user can provide a covariance matrix to the
argument sigma
.
Braun and Feng 2001. Optimal Permutation Tests for the Analysis of Group Randomized Trials. Journal of the American Statistical Association 96(456):1424-1432
Romano and Wolf 2005. Exact and approximate stepdown methods for multiple hypothesis testing. Journal of the American Statistical Association 100: 94–108
Watson, Akinyemi, and Hemming. 2021 Multiple testing corrections for p-values and confidence intervals from generalised linear mixed models. arXiv 2107.10017v2
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