library(neuroim2)
library(purrr)
library(assertthat)
library(locfit)
bvec <- read_vec("testdata/rscan01.nii.gz")
mask <- read_vol("testdata/mask.nii")
test_that("can run turbo_clust on a NeuroVec", {
cres <- turbo_cluster(bvec, mask, K=100,
sigma1=1, sigma2=4,
filter_method="bspline")
})
test_that("can run turbo_clust on a NeuroVec with nreps=5", {
cres <- turbo_cluster(bvec, mask, K=100, sigma1=1,
sigma2=1.5,
filter=list(lp=.05, hp=.33),
filter_method="bspline",
sample_frac=.3, nreps=12)
})
test_that("can bandpass filter a NeuroVec using bsplines", {
sbvec <- filter_vec(bvec, mask, hp=.8, lp=.04, method="bspline")
})
test_that("can knn_shrink a NeuroVec", {
sbvec <- knn_shrink(bvec, mask, k=4)
})
test_that("can filter, then shrink, then compute gradient of a NeuroVec", {
sbvec <- filter_vec(bvec, mask, hp=.8, lp=.03, method="bspline")
sbvec <- knn_shrink(sbvec, mask, k=4)
grad <- correlation_gradient(sbvec, mask)
})
test_that("meta_clust a turbo_cluster result", {
cres <- turbo_cluster(bvec, mask, K=100, sigma1=1, sigma2=4, filter=list(lp=.03, hp=.33), filter_method="bspline")
})
test_that("commute_time cluster", {
cres <- commute_cluster(bvec,mask,K=64, ncomp=100, sigma1=.73, sigma2=6,alpha=0,
filter=list(lp=.03, hp=.33), filter_method="bspline")
})
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