Linear Model at Every Voxel
mincLm(formula, data = NULL, subset = NULL, mask = NULL, maskval = NULL, parallel = NULL, cleanup = TRUE, conf_file = getOption("RMINC_BATCH_CONF"))
| formula | The linear model formula. The left-hand term consists of the MINC filenames over which to compute the models at every voxel.The RHS of the formula may contain one term with filenames. If so only the + operator may be used, and only two terms may appear on the RHS |
|---|---|
| data | The data frame which contains the model terms. |
| subset | Subset definition. |
| mask | Either a filename or a vector of values of the same length as the input files. The linear model will only be computed inside the mask. |
| maskval | the value in the mask used to select unmasked voxels, defaults to any positive intensity from 1-99999999 internally expanded to .5 - 99999999.5. If a number is specified voxels with intensities within 0.5 of the chosen value are considered selected. |
| parallel | how many processors to run on (default=single processor). Specified as a two element vector, with the first element corresponding to the type of parallelization, and the second to the number of processors to use. For local running set the first element to "local" or "snowfall" for back-compatibility, anything else will be run with batchtools see pMincApply Leaving this argument NULL runs sequentially. |
| cleanup | Whether or not to remove parallelization files |
| conf_file | A batchtools configuration file defaulting to |
mincLm returns a mincMultiDim object which contains a series of columns corresponding to the terms in the linear model. The first column is the F-statistic of the significance of the entire volume, the following columns contain the R-Squared term, the marginal t-statistics for each of the terms in the model along with their respective coefficients.
This function computes a linear model at every voxel of a set of files. The function is a close cousin to lm, the key difference
being that the left-hand side of the formula specification takes a series of filenames for MINC files.
If you encounter memory issues, it could be due to minc file caching.
Consider trying with the environment variable MINC_FILE_CACHE_MB set to
a small value like 1.
mincWriteVolume,mincFDR,mincMean, mincSd
# NOT RUN { getRMINCTestData() # read the text file describing the dataset gf <- read.csv("/tmp/rminctestdata/test_data_set.csv") # Compute a linear model at each voxel vs <- mincLm(jacobians_fixed_2 ~ Sex, gf) # }