rftResults: Produce Random Field Theory Statistical Results
In Tokazama/iClass: More intuitive syntax for manipulating imaging data

Description Usage Arguments Details Value Author(s) References Examples

View source: R/rftResults.R

Returns RFT based statistical results for a single statistical image

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rftResults(x, resels, fwhm, df, fieldType, rpvImage = NULL, k = 1,
  threshType = "pRFT", pval = 0.05, pp = 0.001, n = 1,
  verbose = FALSE)

`x`	statistical field image of class antsImage
`resels`	resel values for the mask
`fwhm`	full width at half maxima
`df`	degrees of freedom expressed as df = c(degrees of interest, degrees of error)
`rpvImage`	resels per voxel image
`k`	minimum desired cluster size (default = 1)
`threshType`	a numeric value to threshold the statistical field or a character of the following methods: cRFT: computes a threshold per expected cluster level probability pRFT: uses the mask and pval calculates the minimum statistical threshold cFDR: uses an uncorrected threshold at the alpha level and then computes and FDR threshold based on cluster maxima pFDR: computes the fdr threshold for the entire field of voxels
`pval`	the p-value for estimating the threshold (default = .05)
`pp`	the primary (initial) p-value for thresholding (only used for FDR methods; default = .001)
`n`	number of images in conjunction
`verbose`	enables verbose output
`fieldType:`	"T"T-field "F"F-field "X"Chi-square field" "Z"Gaussian field

rftPval is used to compute all family-wise error (FWE) corrected statistics while p.adjust is used to compute all false-discovery rate based statistics. All statistics herein involve implementation of random field theory (RFT) to some extent.

Both cluster-level and peak-level statistics are described by the uncorrected p-value along with the FDR and FWE corrected p-values for each cluster. Peak-level statistics are described by the maximum statistical value in each cluster. The ClusterStats table also contains coordinates for each cluster and the number of voxels therein. By default threshType = "pRFT" and pval=.05. Alternatively, the user may use a specific numeric value for thresholding the statistical field. statFieldThresh more fully describes using appropriate thresholds for statistical fields and how pp plays a role in FDR thresholding.

Outputs a statistical value to be used for threshold a statistical field image

setLevel:

set-level statistics and number of clusters

clusterLevel:

cluster-level statistics and descriptors

peakLevel:

peak-level statistics and descriptor"

clusterImage:

image of labeled clusters

threshold:

the threshold used

Zachary P. Christensen

Chumbley J., (2010) Topological FDR for neuroimaging

Friston K.J., (1996) Detecting Activations in PET and fMRI: Levels of Inference and Power

Worsley K.J., (1992) A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain.

## Not run: 
mnit1 <- antsImageRead(getANTsRData('mni'))
mask <- getMask(mnit1)
ilist <- list()
for (i in 1:10) {
 ilist <- lappend(ilist, antsImageClone(mnit1) * rnorm(1))
}
response <- rnorm(10)
imat <- imageListToMatrix(ilist, mask)
residuals <- matrix(nrow = nrow(imat), ncol = ncol(imat))
tvals <- matrix(nrow = nrow(imat), ncol = ncol(imat))
for (i in 1:ncol(imat)) {
 fit <- lm(response ~ imat[, i])
 tvals <- coefficients(fit)[2]
 residuals[, i] <- residuals(fit)
}
myfwhm <- estSmooth(residuals, mask, fit$df.residual)
res <- resels(mask, myfwhm$fwhm)
timg <- makeImage(mask, tvals)

# threshold to create peak values with p-value of .05 (default)
results1 <- rftResults(timg, res, myfwhm$fwhm, df, fieldType = "T",
                       threshType = "pRFT", pval = .05)

# threshold to create clusters with p-value of .05
results2 <- rftResults(timg, res, myfwhm$fwhm, df, fieldType = "T",
                       threshType = "cRFT", pval = .05)

# initial threshold at p-value of .001 followed by peak FDR threshold at
# p-value of .05
results3 <- rftResults(timg, res, myfwhm$fwhm, df, fieldType = "T",
                       threshType = "pFDR", pval = .05, pp=.01)

# initial threshold at p-value of .001 followed by cluster FDR threshold at
# p-value of .05
results4 <- rftResults(timg, res, myfwhm$fwhm, df, fieldType = "T",
                       threshType = "cFDR", pval = .05, pp = .01)

# correcting for non-isotropic
results5 <- rftResults(timg, res, myfwhm$fwhm, df, fieldType = "T",
                       fwhm$rpvImage)


## End(Not run)