rftPval.Rd
Calculates p-values of a statistical field using random field theory
rftPval(D, c, k, u, n, resels, df, fieldType)
D | image dimensions |
---|---|
c | Threshold |
k | spatial extent in resels (minimum cluster size in resels) |
u | Number of clusters |
n | number of statistical field in conjunction |
resels | resel measurements of the search region |
df | degrees of freedom expressed as df = c(degrees of interest, degrees of error) |
fieldType |
|
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
Zachary P. Christensen
if (FALSE) { # 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 }