mincified version of lmer from lme4

mincLmer should be used the same way as a straight lmer call, except that the left hand side of the equation contains minc filenames rather than an actual response variable.

mincLmer(formula, data, mask, parallel = NULL, REML = TRUE,
  control = lmerControl(), start = NULL, verbose = 0L,
  temp_dir = getwd(), safely = FALSE, cleanup = TRUE,
  summary_type = "fixef", weights = NULL, resources = list(),
  conf_file = getOption("RMINC_BATCH_CONF"))

Arguments

formula	the lmer formula, filenames go on left hand side
data	the data frame, all items in formula should be in here
mask	the mask within which lmer is solved
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 and may take a long time.
REML	whether to use use Restricted Maximum Likelihood or Maximum Likelihood
control	lmer control function
start	lmer start function
verbose	lmer verbosity control
temp_dir	A directory to create temporary mask and registry files if parallel = c("sge", n). This should not be tempdir() so that workers can see these files. Defaults to the current working directory.
safely	whether or not to wrap the per-voxel lmer code in an exception catching block (tryCatch), when TRUE this will downgrade errors to warnings and return NA for the result.
cleanup	Whether or not to cleanup registry files after a queue parallelized run
summary_type	Either one of fixef: default and equivalent to older versions of RMINC, returns fixed effect coefficients and t-values ranef: returns random effect coefficients and t-values both: both fixed and random effects anova: return the F-statistic for each fixed effect or a function to be used to generate the summary
weights	weights to be applied to each observation
resources	A list of resources to use for the jobs, for example list(nodes = 1, memory = "8G", walltime = "01:00:00") . See system.file("parallel/pbs_script.tmpl", package = "RMINC") and system.file("parallel/sge_script.tmpl", package = "RMINC") for more examples
conf_file	A batchtools configuration file

Value

a matrix where rows correspond to number of voxels in the file and columns to the number of terms in the formula, with both the beta coefficient and the t-statistic being returned. In addition, an extra column keeps the log likelihood, and another whether the mixed effects fitting converged or not.

Details

mincLmer provides an interface to running linear mixed effects models at every voxel. Unlike standard mincLm, however, testing hypotheses in linear mixed effects models is more difficult, since the denominator degrees of freedom are more difficult to determine. RMINC provides two alternatives: (1) estimating degrees of freedom using the mincLmerEstimateDF function, and (2) comparing two separate models using mincLogLikRatio (which in turn can be corrected using mincLogLikRatioParametricBootstrap). For the most likely models - longitudinal models with a separate intercept or separate intercept and slope per subject - both of these approximations are likely correct. Be careful in using these approximations if using more complicated random effects structures.
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

lmer for description of lmer and lmer formulas; mincLm

Examples

# NOT RUN {
vs <- mincLmer(filenames ~ age + sex + (age|id), data=gf, mask="mask.mnc")
mincWriteVolume(vs, "age-term.mnc", "tvalue-age")
# run in parallel with multiple processors on the local machine
vs <- mincLmer(filenames ~ age + sex + (age|id),
               data=gf,
               mask="mask.mnc",
               parallel=c("snowfall", 4))
# run in parallel with multiple processors over the sge batch queueing system
vs <- mincLmer(filenames ~ age + sex + (age|id),
               data=gf,
               mask="mask.mnc",
               parallel=c("sge", 4))
# estimate degrees of freedom
vs <- mincLmerEstimateDF(vs)
# correct for multiple comparisons using the False Discovery Rate
(qvals <- mincFDR(vs))
# generate another model with a more complex curve for the age term
library(splines)
vs2 <- mincLmer(filenames ~ ns(age,2) + sex + (age|id), data=gf, mask="mask.mnc")
# see if that more complex age term was worth it
modelCompare <- mincLogLikRatio(vs, vs2)
mincFDR(modelCompare)
# see if there was any bias in those p-value estimates (takes a few minutes)
modelCompare <- mincLogLikRatioParametricBootstrap(modelCompare)
mincFDR(modelCompare)
# }