fmri.lm {fmri} | R Documentation |
Estimate the parameters and variances in a linear model.
fmri.lm(data, z, actype = "accalc", hmax = 3.52, vtype = "var", step = 0.01, contrast = c(1), vvector = c(1), keep = "all")
data |
object of class "fmridata" |
z |
designmatrix specifying the expected BOLD response(s) and additional components for trend and other effects. |
actype |
string describing the type of handling autocorrelation of time series. "nonac", "ac", "accalc", "smooth" |
hmax |
bandwidth for smoothing autocorrelation parameter if actype = "smooth" |
vtype |
method of estimating residual variance (only "var" implemented) |
step |
step size for binning autocorrelations (see details) |
contrast |
contrast vector |
vvector |
vector defining the parameters for which the
covariance matrix is returned as well as the corresponding length of
the vector cbeta in each voxel |
keep |
string describing the amount of data returned. If keep=="all" residuals
are included in the returned object. This triggers variance estimation
in fmri.smooth to based on adaptively smoothed residuals.
Otherwise variance estimation is based on the estimated smootheness of the measured
data. This is less memory and time consuming, but expected to be
less accurate. |
This function performs parameter estimation in the linear model.
It implements a two step procedure. After primary estimation of the
parameters in the first step residuals
are obtained. If actype
%in%
c("ac", "accalc", "smooth")
an AR(1) model is fitted, in each voxel, to
the time series of residuals. The estimated AR-coefficient is corrected for bias.
If actype=="smooth"
the estimated AR-coefficients are spatially smoothed using bandwidth hmax
.
If actype
%in%
c("ac", "smooth")
the linear model is prewithened
using the estimated (smoothed) AR-coefficients. Parameter
and variance estimates are then obtained from the prewithened
data. The argument keep
describes the amount of data which is
returned. If "essential" only the estimated effects
tilde{gamma}_i = C^Ttilde{β}_i
and their
estimated variances are returned. "all" gives the full data, including
residuals, temporal autocorrelation.
If vvector
is given and has length greater than 1, the
covariance matrix for the stimuli given therein are returned
(varm
) and vwghts
contains an estimate for the ratio of
the variances of the parameter for the stimuli indicated in
vvector
. cbeta
then contains the corresponding parameter
estimates and thus is a vector of corresponding length in each voxel.
If warning "Local smoothness characterized by large bandwidth" occurs,
check scorr
elements. If correlation drops with lag towards
zero, data has been pre-smoothed. Adaption can then only be of limited
use. If correlation does not go to zero, check the residuals of the
linear model for unexplained structure (spin saturation in first
scans? discuard them!).
object with class attributes "fmrispm" and "fmridata"
beta |
estimated parameters |
cbeta |
estimated contrast of parameters |
var |
estimated variance of the contrast of parameters. |
varm |
covariance matrix of the parameters given by vvector |
res |
raw (integer size 2) vector containing residuals of the estimated linear model up to scale factor resscale. |
resscale |
resscale*extract.data(object,"residuals") are the residuals. |
dim |
dimension of the data cube and residuals |
arfactor |
estimated autocorrelation parameter |
rxyz |
array of smoothness from estimated correlation for each voxel in resel space (for analysis without smoothing) |
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. |
weights |
ratio of voxel dimensions |
vwghts |
ratio of estimated variances for the stimululi given by
vvector |
mask |
head mask. |
df |
Degrees of freedom for t-statistics. |
hrf |
expected BOLD response for contrast |
vvector
is intended to be used for delay of the HRF using
its first derivative. Do not mix with the contrast
argument, since
unexpected side effects may occur. Look out for updates of this package.
Karsten Tabelow tabelow@wias-berlin.de
Worsley, K.J. (2005). Spatial smoothing of autocorrelations to control the degrees of freedom in fMRI analysis. NeuroImage, 26:635-641.
Worsley, K.J., Liao, C., Aston, J., Petre, V., Duncan, G.H., Morales, F., Evans, A.C. (2002). A general statistical analysis for fMRI data. NeuroImage, 15:1-15.
# Example 1 data <- list(ttt=writeBin(rnorm(32*32*32*107),raw(),4), mask=array(1,c(32,32,32)),dim=c(32,32,32,107)) class(data) <- "fmridata" hrf <- fmri.stimulus(107, c(18, 48, 78), 15, 2) z <- fmri.design(hrf,2) model <- fmri.lm(data,z,keep="all") plot(extract.data(data)[16,16,16,]) lines(extract.data(data)[16,16,16,] - extract.data(model,"residuals")[16,16,16,],col=2)