MixfMRI (version 0.1-0)

LRT: Likelihood ratio tests

Description

Likelihood ratio tests for merging clusters.

Usage

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"))

Arguments

PV.gbd

a p-value vector of signals associated with voxels. length(PV.gbd) = N.

CLASS.gbd

a classification vector of signals associated with voxels. length(CLASS.gbd) = N.

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

q-value procedure for adjusting p-values.

Value

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 p-values.

Details

These functions perform likelihood ratio tests for merging clusters. Only p-values 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 (p-value) component were the same. The chi-square distribution with 1 degree of freedom is used.

lrt.betaab tests if alpha and beta of any two pairs of mixture (p-value) components were the same. The chi-square distribution with 2 degrees of freedom is used.

References

http://maitra.public.iastate.edu/

See Also

PARAM.

Examples

# NOT RUN {
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)

### p-values of rest clusters.
ret.lrt <- lrt(PV.gbd, ret$class, K = 2)
print(ret.lrt)
# }
# NOT RUN {
  ret.lrt2 <- lrt2(PV.gbd, ret$class, K = 3)
  print(ret.lrt2)
# }