Anatomy Linear Mixed Effects Model

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.