rftPval.Rd
Calculates p-values of a statistical field using random field theory
rftPval(D, c, k, u, n, resels, df, fieldType)
image dimensions
Threshold
spatial extent in resels (minimum cluster size in resels)
Number of clusters
number of statistical field in conjunction
resel measurements of the search region
degrees of freedom expressed as df = c(degrees of interest, degrees of error)
T: T-field
F: F-field
X: Chi-square field'
Z: Gaussian field
The probability of obtaining the specified cluster
Pcor: corrected p-value Pu: uncorrected p-value Ec: expected number of clusters ek: expected number of resels per cluster
This function calculates p-values of a thresholded statistical field at various levels:
set-level rft.pval(D, c, k, u, n, resels, df, fieldType)
cluster-level rft.pval(D, 1, k, u, n, resels, df, fieldType)
peak-level rft.pval(D, 1, 0, u, n, resels, df, fieldType)
Where set-level refers to obtaining the set of clusters, cluster-level refers to a specific cluster, and peak-level refers to the maximum (or peak) of a single cluster.
Friston K.J., (1994) Assessing the Significance of Focal Activations Using Their Spatial Extent.
Friston K.J., (1996) Detecting Activations in PET and fMRI: Levels of Inference and Power.
Worlsey K.J., (1996) A Unified Statistical Approach for Determining Significant Signals in Images of Cerebral Activation.
rftResults, resels
if (FALSE) { # \dontrun{
# using rftPval for hypothetical 3D t-statistical image
# assume resels have been calculated and df = c(dfi, dfe)
# peak RFT p-value (peak = the maximum of a specific cluster)
peakP <- rftPval(3, 1, 0, peak, 1, resels, df, fieldType = "T")$Pcor
# cluster RFT p-value (u = the value the statistical field was threshold at
# and k = the size of the cluster in resels)
clusterP <- rftPval(3, 1, k, u, 1, resels, df, fieldType = "T")$Pcor
# set RFT p-value
setP <- rftPval(3, numberOfClusters, minimumClusterSize, u, 1, resels, df,
fieldType = "T"
)$Pcor
} # }