fmri.smooth {fmri} | R Documentation |
Perform the adaptive weights smoothing procedure
fmri.smooth(spm, hmax = 4, adaptive=TRUE, lkern="Gaussian", skern="Plateau", weighted=TRUE)
spm |
object of class fmrispm |
hmax |
maximum bandwidth to smooth |
adaptive |
logical. TRUE (default) for adaptive smoothing |
lkern |
lkern specifies the location kernel. Defaults to
"Gaussian", other choices are "Triangle" and "Plateau". Note that the location kernel is applied to
(x-x_j)^2/h^2 , i.e. the use of "Triangle" corresponds to the
Epanechnicov kernel in nonparametric kernel regression. "Plateau" specifies a kernel that is equal to 1 in the interval (0,.3),
decays linearly in (.5,1) and is 0 for arguments larger than 1.
|
skern |
skern specifies the kernel for the statistical
penalty. Defaults to "Plateau", the alternatives are "Triangle" and "Exp".
"Plateau" specifies a kernel that is equal to 1 in the interval (0,.3),
decays linearly in (.3,1) and is 0 for arguments larger than 1.
lkern="Plateau" and lkern="Triangle" allow for much faster computation (saves up
to 50% CPU-time). lkern="Plateau" produces a less random weighting scheme. |
weighted |
weighted (logical) determines if weights contain the inverse of local
variances as a factor (Weighted Least Squares). weighted=FALSE does not employ the
heteroscedasticity of variances for the weighting scheme and is preferable if variance estimates
are highly variable, e.g. for short time series. |
This function performs the smoothing on the Statistical Parametric Map spm.
hmax
is the (maximal) bandwidth used in the last iteration. Choose
adaptive
as FALSE
for non adaptive
smoothing. lkern
can be used for specifying the
localization kernel. For comparison with non adaptive methods use
"Gaussian" (hmax given in FWHM), for better adaptation use "Plateau" or "Triangle"
(default, hmax given in voxel). For lkern="Plateau"
and lkern="Triangle"
thresholds may be inaccurate, due to a violation of
the Gaussian random field assumption under homogeneity. lkern="Plateau"
is expected to provide best results with adaptive smoothing.
skern
can be used for specifying the
kernel for the statistical penalty. "Plateau" is expected to provide the best results,
due to a less random weighting scheme.
The function handles zero variances by assigning a large value (1e20)
to these variances. Smoothing is restricted to voxel with spm$mask
.
object with class attributes "fmrispm" and "fmridata"
cbeta |
smoothed parameter estimate |
var |
variance of the parameter |
hmax |
maximum bandwidth used |
rxyz |
smoothness in resel space. all directions |
rxyz0 |
smoothness in resel space as would be achieved by a Gaussian filter with the same bandwidth. all directions |
scorr |
array of spatial correlations with maximal lags 5, 5, 3 in x,y and z-direction. |
bw |
vector of bandwidths (in FWHM) corresponding to the spatial correlation within the data. |
dim |
dimension of the data cube and residuals |
weights |
ratio of voxel dimensions |
vwghts |
ratio of estimated variances for the stimuli given by
vvector |
hrf |
Expected BOLD response for the specified effect |
Joerg Polzehl polzehl@wias-berlin.de, Karsten Tabelow tabelow@wias-berlin.de
Tabelow, K., Polzehl, J., Voss, H.U., and Spokoiny, V.. Analysing {fMRI} experiments with structure adaptive smoothing procedures, NeuroImage, 33:55-62 (2006).
Polzehl, J. and Spokoiny, V. (2006). Propagation-Separation Approach for Local Likelihood Estimation, Probab. Theory Relat. Fields 135, 335-362.
## Not run: fmri.smooth(spm, hmax = 4, lkern = "Gaussian")