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Apply arbitrary R function at every voxel

Source: R/minc_voxel_statistics.R
mincApply.Rd

Can execute any R function at every voxel for a set of MINC files

Usage

mincApply(
  filenames,
  function.string,
  mask = NULL,
  maskval = NULL,
  reduce = FALSE
)

Arguments

filenames

The MINC files over which to apply the function. Have to be the same sampling.

function.string

The function which to apply. Can only take a single argument, which has to be 'x'. See details and examples.

mask

The filename of a mask - function will only be evaluated inside the mask.

maskval

An integer specifying the value inside the mask where to apply the function. If left blank (the default) then anything above 0.5 will be considered inside the mask. This argument only works for mincApply, not pMincApply.

reduce

Whether to filter masked voxels from the resulting object

Value

out The output is a matrix with the same number of rows a the file sizes and the same number of columns as output by the function that was applied. Cast into one of the MINC classes to make writing it out with mincWriteVolume easier.

Details

mincApply allows one to execute any R function at every voxel of a set of files. mincApply runs in the current R process; see also pMincApply and qMincApply for parallel variants. Unless the function to be applied takes a single argument, which will take on the voxel values at every voxel, a wrapper function has to be written. The mincApply function also supports passing, instead of a function, a quoted expression containing a free variable 'x', again taking on all voxel values; this is illustrated in the examples. (Not this is not supported by pMincApply.) The output of this function also has to be a simple vector or scalar. Note that interpreted R can be very slow. Mindnumbingly slow. It therefore pays to write optimal functions or, whenever available, use the optimized equivalents. In other words and to give one example, use mincLm rather than applying lm, and if lm must be applied, try to use lm.fit rather than plain lm. Also consider using parallel apply methods. When using the pbs method, one can also set the options –> TMPDIR,MAX_NODES and WORKDIR
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.

See also

mincWriteVolume,mincMean,mincSd,mincLm,mincAnova

Examples

if (FALSE) { # \dontrun{
getRMINCTestData()
gf <- read.csv("/tmp/rminctestdata/test_data_set.csv")
ma <- mincApply(gf$jacobians_fixed_2,quote(mean(x)));
mincWriteVolume(ma, "means.mnc")
### run the whole thing in parallel on the local machine
testFunc <- function (x) { return(c(1,2))}
pout <- pMincApply(gf$jacobians_fixed_2,
                   testFunc,
                   workers = 4,
                   global = c('gf','testFunc'))
mincWriteVolume(pout, "pmincOut.mnc")

### pbs example 1 (hpf)
getRMINCTestData()
gf <- read.csv("/tmp/rminctestdata/test_data_set.csv")
testFunc <- function (x) { return(c(1,2))}
options(TMPDIR="/localhd/$PBS_ID")
pout <- pMincApply(gf$jacobians_fixed_2,
                   testFunc,
                   workers = 4,
                   method="pbs",
                   global = c('gf','testFunc'))

### pbs example 2 (scinet)
getRMINCTestData()
gf <- read.csv("/tmp/rminctestdata/test_data_set.csv")
testFunc <- function (x) { return(c(1,2))}
options(WORKDIR="$SCRATCH")
options(MAX_NODES=8)
options(TMP_DIR="/tmp")
pout <- pMincApply(gf$jacobians_fixed_2,
                   testFunc,
                   modules=c("intel","openmpi","R/3.1.1"),
                   workers = 4,
                   method="pbs",
                   global = c('gf','testFunc'))

#NOTE 1: On SCINET jobs are limited to 32*48 hours of cpu time
#NOTE 2: On SCINET the Rmpi library must be compiled for use with R 3.1.1
} # }