clusterperm.lmer {permutes} | R Documentation |
Cluster-based permutation tests for time series data, based on mixed-effects models or other buildmer
models.
Description
Cluster-based permutation tests for time series data, based on mixed-effects models or other buildmer
models.
Usage
clusterperm.lmer(
formula,
data = NULL,
family = gaussian(),
weights = NULL,
offset = NULL,
series.var = ~0,
buildmerControl = list(direction = "order", crit = "LRT", quiet = TRUE, ddf = "lme4"),
nperm = 1000,
type = "regression",
parallel = FALSE,
progress = "none"
)
Arguments
formula |
A normal formula, possibly using |
data |
The data. |
family |
The family. |
weights |
An optional vector of prior case weights. This vector is evaluated in the calling environment, not in the formula or data frame! |
offset |
An optional vector containing a prior offset term. This vector is evaluated in the calling environment, not in the formula or data frame! |
series.var |
A one-sided formula giving the variable grouping the time series. |
buildmerControl |
Options overriding the defaults in |
nperm |
The number of permutations. |
type |
A character string of either |
parallel |
Whether to parallelize the permutation testing using plyr's |
progress |
A plyr |
Details
If not explicitly overridden in the buildmerControl
argument, the function changes a few buildmer defaults to make more sense for permutation testing. This includes setting direction='order'
and quiet=TRUE
. Set these options explicitly to override this behavior.
Because model comparisons are used for efficient permutation testing, REML is not supported.
Omega squared is not available for mixed-effects ANOVA; the w2
column will not be included in the resulting permutes
object.
The returned object provides the raw bootstrap samples in the pb
attribute. These are used to compute a corrected p-value using Maris & Oostenveld's (2007) cluster mass statistic, but can also be used by the user to compute other such statistics. These obviously significantly increase the size of the R object; if this becomes a problem, there is no harm in deleting them using e.g. attr(x,'perms') <- NULL
.
Value
A data frame.
Examples
# Testing a single EEG electrode, with random effects by participants
perms <- clusterperm.lmer(Fz ~ Deviant * Session + (Deviant * Session | Subject),
data=MMN,series.var=~Time)
# Testing a single EEG electrode, with random effects by participants, ANOVA inference
perms <- clusterperm.lmer(Fz ~ Deviant * Session + (Deviant * Session | Subject),
data=MMN,series.var=~Time,type='anova')