Description Usage Arguments Details Value Author(s) References See Also Examples
Likelihood ratio tests for merging clusters.
1 2 3 4 5 6 7 8 9  lrt(PV.gbd, CLASS.gbd, K, H0.alpha = .FC.CT$LRT$H0.alpha,
H0.beta = .FC.CT$LRT$H0.beta)
lrt2(PV.gbd, CLASS.gbd, K, H0.mean = .FC.CT$LRT$H0.mean,
upper.beta = .FC.CT$INIT$BETA.beta.max, proc = c("1", "2", "weight"))
lrt.betamean(PV.gbd, CLASS.gbd, K, proc = c("1", "2"))
lrt.betaab(PV.gbd, CLASS.gbd, K, proc = c("1", "2"))

PV.gbd 
a pvalue vector of signals associated with voxels.

CLASS.gbd 
a classification vector of signals associated with voxels.

K 
number of clusters. 
H0.alpha 
null hypothesis for the alpha parameter of Beta distribution. 
H0.beta 
null hypothesis for the beta parameter of Beta distribution. 
H0.mean 
null hypothesis for the mean of Beta distribution. 
upper.beta 
BETA.beta.max, maximum value of beta parameter of Beta distribution. 
proc 
qvalue procedure for adjusting pvalues. 
These functions perform likelihood ratio tests for merging clusters. Only pvalues coordinates (Beta density) are tested, while voxel location coordinates (multivariate Normal density) are not involved in testing.
lrt.betamean
tests if means of any two pairs of mixture
(pvalue) component were the same.
The chisquare distribution with 1 degree of freedom is used.
lrt.betaab
tests if alpha and beta of any two pairs of mixture
(pvalue) components were the same.
The chisquare distribution with 2 degrees of freedom is used.
A matrix contains MLEs of parameters of Beta distribution under the null hypothesis and the union of null and alternative hypotheses. The matrix also contains testing statistics and pvalues.
WeiChen Chen and Ranjan Maitra.
http://maitra.public.iastate.edu/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  library(MixfMRI, quietly = TRUE)
set.seed(1234)
### Test 2d data.
da < pval.2d.mag
id < !is.na(da)
PV.gbd < da[id]
id.loc < which(id, arr.ind = TRUE)
X.gbd < t(t(id.loc) / dim(da))
ret < fclust(X.gbd, PV.gbd, K = 2, min.1st.prop = 0.95)
# print(ret)
### pvalues of rest clusters.
ret.lrt < lrt(PV.gbd, ret$class, K = 2)
print(ret.lrt)
ret.lrt2 < lrt2(PV.gbd, ret$class, K = 3)
print(ret.lrt2)

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