Fit a linear mixed effects model for each structure in the results of anatGetAll.

anatLmer(formula, data, anat, REML = TRUE, control = lmerControl(),
  verbose = FALSE, start = NULL, parallel = NULL, safely = FALSE,
  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

anat

a subject by label matrix of anatomical summaries typically produced by anatGetAll

REML

whether to use use Restricted Maximum Likelihood or Maximum Likelihood

control

lmer control function

verbose

lmer verbosity control

start

lmer start function

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.

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.

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

Details

anatLmer, like its relative mincLmer provides an interface to running linear mixed effects models at every vertex. Unlike standard linear models testing hypotheses in linear mixed effects models is more difficult, since the denominator degrees of freedom are more difficult to determine. RMINC provides estimating degrees of freedom using the anatLmerEstimateDF function. For the most likely models - longitudinal models with a separate intercept or separate intercept and slope per subject - this approximation is likely correct. Be careful in using this approximations if using more complicated random effects structures.