Description Usage Arguments Details Value References Examples
One sample percentile bootstrap test with cluster correction for multiple comparisons
1 2 | trimbt.ccmc(x, nullval = 0, tr = 0.2, alpha = 0.05, bt = TRUE,
nboot = 599)
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x |
A list or a matrix. In the first case x[1] contains the data for the first group, x[2] the data for the second group, etc. Length(x) = the number of groups = J. If stored in a matrix, columns correspond to groups. |
nullval |
The null value against which to compare the trimmed mean - default = 0. |
tr |
The amount of trimming - default 0.2. Set tr=0 for a t-test on means. |
alpha |
Alpha level - default 0.05. |
bt |
Set to TRUE to compute critical t values, p values and confidence intervals based on boostrap-t distributions, otherwise use standard calculations - default = TRUE |
nboot |
Number of bootstrap samples - default = 599 |
One sample t-test on trimmed means with cluster correction for multiple comparisons
A list of univariate t values, p values and confidence intervals, as well as cluster-based statistics.
estimate = mean or trimmed mean
ci = confidence interval
tval = t values
pval = p values
cluster.th = cluster threshold
cluster.map = vector of cluster IDs
cluster.sig = statistical significance based on cluster test
NA
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # no effect:
set.seed(21)
x <- matrix(rnorm(100), ncol = 5)
trimbt.ccmc(x,nullval=0,tr=0,alpha=.05,bt=FALSE,nboot=599)
# cluster of length 3:
set.seed(21)
x <- matrix(rnorm(100), ncol = 5)
x[,3:5] <- x[,3:5] + 1
trimbt.ccmc(x,nullval=0,tr=0,alpha=.05,bt=FALSE,nboot=599)
# use bootstrap-t thresholds
trimbt.ccmc(x,nullval=0,tr=0,alpha=.05,bt=TRUE,nboot=599)
# get cluster statistics for cluster 1:
out <- trimbt.ccmc(x,nullval=0,tr=0,alpha=.05,bt=TRUE,nboot=599)
c2sum <- sum(out$tval[out$cluster.map==1]^2)
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