glmcoeff {BHMSMAfMRI} R Documentation

## Fit GLM to the data time-series and obtain GLM coefficients along with standard error estimates

### Description

Fits General Linear Model to the time-series corresponding to each voxel in the data and returns the standardized GLM coefficients and their standard error estimates.

### Usage

```glmcoeff(nsubject, grid, Data, DesignMatrix)
```

### Arguments

 `nsubject` Number of subjects included in the analysis. `grid` The number of voxels in one row (or, one column) of the brain slice of interest. Must be a power of 2. The total number of voxels is grid^2. The maximum grid value for this package is 512. `Data` The data in form of an array with dimension (nsubject,grid,grid,ntime), where ntime is the size of the time series for each voxel. `DesignMatrix` The design matrix used to generate the data.

### Value

A list containing the following.

 `GLMCoeffStandardized ` An array of dimension (nsubject, grid, grid), containing for each subject the standardized GLM coefficients obtained by fitting GLM to the time-series corresponding to the voxels. `GLMEstimatedSE ` An array of dimension (nsubject, grid, grid), containing for each subject the estimated standard errors of the GLM coefficients.

### Author(s)

Nilotpal Sanyal <nsanyal@stanford.edu>, Marco Ferreira <marf@vt.edu>

### References

Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J., Frith, C.D., Frackowiak, R.S.J., 1994. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2 (4), 189-210.

### Examples

```nsubject <- 3
grid <- 8
Data <- array(dim=c(3,8,8,10),rnorm(3*8*8*10))
DesignMatrix <- cbind( c(rep(c(1,0),5)), rep(1,10) )
glm.fit <- glmcoeff(nsubject, grid, Data, DesignMatrix)
dim(glm.fit\$GLMCoeffStandardized)
#[1] 3 8 8
```

[Package BHMSMAfMRI version 1.3 Index]