Determine p-values.
fmri.pvalue(spm, mode="basic", na.rm=FALSE, minimum.signal = 0, alpha= 0.05)Object with class attributes "fmripvalue" and "fmridata"
p-value. use with plot for thresholding.
voxelsize ratio
data dimension
expected BOLD response for contrast (single stimulus only)
maximal pvalue as scale information
actual threshold used
fmrispm object
type of pvalue definition
na.rm specifies how NA's in the SPM are handled. NA's may occur
in voxel where the time series information did not allow for estimating parameters and their variances
or where the time series information where constant over time. A high (1e19) value of the variance
and a parameter of 0 are used to characterize NA's. If na.rm=TRUE the pvalue for the corresponding voxels
is set to 1. Otherwise pvalues are assigned according to the information found in the SPM at the voxel.
allows to specify a (positive) minimum value for detected signals. If minimum.signal >0 the thresholds are to conservative, this case needs further improvements.
Significance level in case of mode="FDR"
Karsten Tabelow tabelow@wias-berlin.de
If only a contrast is given in spm, we simply use a t-statistic
and define p-values according to random field theory for the resulting gaussian
field (sufficiently large number of df - see ref.). If spm is a
vector of length larger than one for each voxel, a chisq field is
calculated and evaluated (see
Worsley and Taylor (2006)). If delta is given, a cone statistics is
used.
The parameter mode allows for different kinds of p-value
calculation. mode="voxelwise" refers to voxelwise tests while
mode="Bonferroni" adjusts the significance level for multiple testing.
An alternative is mode="FDR" specifying signal detection by False
Discovery Rate (FDR) with proportion of false positives level specified by alpha.
The other choices apply results on excursion sets of random fields
(Worsley 1994, Adler 2003) for smoothed SPM's.
"basic" corresponds to a global definition of the
resel counts based on the amount of smoothness achieved by an equivalent
Gaussian filter. The propagation condition ensures, that under the
hypothesis
$$\hat{\Theta} = 0$$
adaptive smoothing performs like a
non adaptive filter with the same kernel function which justifies this
approach. "local"
corresponds to a more conservative setting, where the p-value is
derived from the estimated local resel counts that has been achieved by
adaptive smoothing. In contrast to "basic", "global" takes a global
median to adjust for the randomness of the weighting scheme generated
by adaptive smoothing. "global" and "local" are more conservative than
"basic", that is, they generate slightly larger p-values.
Polzehl, J. and Tabelow, K. (2007) fmri: A Package for Analyzing fmri Data, R News, 7:13-17 .
Tabelow, K., Polzehl, J., Voss, H.U., and Spokoiny, V. (2006). Analysing fMRI experiments with structure adaptive smoothing procedures, NeuroImage, 33:55-62.
Worsley, K.J., and Taylor, J.E., Detecting fMRI activation allowing for unknown latency of the hemodynamic response, NeuroImage 29:649-654 (2006).
fmri.lm, fmri.smooth, plot.fmridata,
fmri.cluster, fmri.searchlight
if (FALSE) fmri.pvalue(smoothresult)
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