Vertex Mixed Effects Models

Source: R/minc_vertex_statistics.R

Perform linear mixed effects model fitting for vertex data. vertexLmer should be used the same way as a straight lmer call, except that the left hand side of the equation contains vertex filenames rather than an actual response variable.

vertexLmer(formula, data, mask = NULL, parallel = NULL, REML = TRUE,
  control = lmerControl(), start = NULL, verbose = 0L, safely = FALSE,
  summary_type = "fixef")

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
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

Details

vertexLmer, 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 vertexLmerEstimateDF 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 approximation if using more complicated random effects structures.

See also

lmer for description of lmer and lmer formulas; mincLm