Create a heatmap of the results of cluster-based permutation testing.
# S3 method for permutes
plot(
x,
type = c("LRT", "F", "t", "beta", "w2", "cluster_mass"),
breaks = NULL,
sig = NULL,
...
)A ggplot2 object.
Output of permu.test or clusterperm.lmer or associated functions. You may want to subset it if you want to simulate zooming in.
The quantity to plot. For ANOVA, the options are 'LRT' (default; this is the permutation statistic, which is a likelihood-ratio-test statistic), 'F', 'p' (this is the permutation p-value of the permutation statistic), or 'w2' (omega squared; not available for mixed-effects models). For regression, the options are 't', 'beta', or 'p'.
The granularity of the labels of the x axis. Pass `unique(x[,2])' to get a tick for every timepoint. Combine this trick with subsetting of your dataset, and perhaps averaging over all your dependent variables, to `zoom in' on your data to help you determine precisely where significance begins and stops to occur.
An optional column name. If specified, only cases with a value of <.05 in this column will be plotted. NULL by default, but suggested to be set to 'p.cluster_mass' for easy visualization of the cluster-mass-test results, or 'p' to directly use the permutation p-values.
Other arguments, which will be ignored (the ellipsis is provided for consistency with the generic plot() method).