RFT Statistical Results

Returns RFT based statistical results for a single statistical image

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

Arguments

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)
fieldType

  • T: T-field

  • F: F-field

  • X: Chi-square field'

  • Z: Gaussian field
RPVImg

resels per voxel image
k

minimum desired cluster size (default = 1)
threshType

a numeric value to threshTypeold the statistical field or a character of the following methods:

  • cRFT: computes a threshTypeold per expected cluster level probability

  • pRFT: uses the mask and pval calculates the minimum statistical threshTypeold

  • cFDR: uses an uncorrected threshTypeold at the alpha level and then computes and FDR threshTypeold based on cluster maxima

  • pFDR: computes the fdr threshTypeold for the entire field of voxels
pval

the p-value for estimating the threshTypeold (default = .05)
pp

the primary (initial) p-value for threshTypeolding (only used for FDR methods; default = .001)
n

number of images in conjunction
statdir

directory where output is saved (if not specified images are not saved)
verbose

enables verbose output

Value

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

  • SetStats: set-level statistics and number of clusters

  • ClusterStats: cluster-level statistics and descriptors

  • PeakStats: peak-level statistics and descriptor"

  • LabeledClusters: image of labeled clusters

  • threshTypeold: the threshTypeold used

Details

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 and the comparable Z statistic. 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 threshTypeolding the statistical field. statFieldThresh more fully describes using appropriate threshTypeolds for statistical fields and how pp plays a role in FDR threshTypeolding.

References

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.

Examples

if (FALSE) {
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 threshTypeold 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$fwhm, df, fieldType = "T",
                       fwhm$RPVImg)

}