gimme (version 0.1-1)

gimmeSEM: Group iterative multiple model estimation.

Description

This function identifies structural equation models for each individual that consist of both group-level and individual-level paths.

Usage

gimmeSEM(data     = "",
         out      = "",
         sep      = "",
         header   = ,
         ar       = FALSE,
         plot     = TRUE,
         subgroup = FALSE,
         paths    = NULL,
         deconvolve_hrf = FALSE,
         control  = NULL)

Arguments

data

The path to the directory where the data files are located. Each file must contain one matrix for each individual containing a T (time) by p (number of variables) matrix where the columns represent variables and the rows represent time.

out

The path to the directory where the results will be stored. This must be generated by the user prior to running the function.

sep

The spacing of the data files. "" indicates space-delimited, "/t" indicates tab-delimited, "," indicates comma delimited.

header

Logical. Indicate TRUE for data files with a header.

ar

Logical. If TRUE, begins search for group model with autoregressive (AR) paths open. Defaults to FALSE.

plot

Logical. If TRUE, graphs depicting relations among variables of interest will automatically be created. For individual-level plots, red paths represent positive weights and blue paths represent negative weights. For the group-level plot, black represents

subgroup

Logical. If TRUE, subgroups are generated based on similarities in model features using the walktrap.community function from the igraph package.

paths

lavaan-style syntax containing paths with which to begin model estimation. That is, Y~X indicates that Y is regressed on X, or X predicts Y. If no header is used, then variables should be referred to with V followed (with no separation) by t

deconvolve_hrf

In development. Defaults to FALSE.

control

In development. Defaults to NULL.

itemize

• subgroupkPathCounts

strong

• k
• k

item

subgroupkPlot

cr

In individual output directory (where id represents the original file name for each individual):

• idBetas

Contains individual-level estimates of each path for each individual. idStdErrors Contains individual-level standard errors for each path for each individual. idPlot Contains individual-level plots. Red paths represent positive weights and blue paths represent negative weights.

Details

In main output directory:

• indivPathEstimates

{Contains estimate, standard error, p-value, and z-value for each path for each individual} summaryFit {Contains model fit information for individual-level models. If subgroups are requested, this file also contains the subgroup membership for each individual.} summaryPathCountMatrix Contains counts of total number of paths, both contemporaneous and lagged, estimated for the sample. The row variable is the outcome and the column variable is the predictor variable. summaryPathCounts {Contains summary count information for paths identified at the group-, subgroup (if subgroup = TRUE), and individual-level.} summaryPathsPlot {Produced if plot = TRUE. Contains figure with group, subgroup (if subgroup = TRUE), and individual-level paths for the sample. Black paths are group-level, green paths are subgroup-level, and grey paths are individual-level, where the thickness of the line represents the count.}

References

Gates, K.M. & Molenaar, P.C.M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63, 310-319.

Examples

paths <- 'V2 ~ V1
          V3 ~ V4lag'

gimmeSEM(data     = "C:/data100",
         out      = "C:/data100_gimme_out",
         sep      = ",",
         header   = FALSE,
         ar       = TRUE,
         plot     = TRUE,
         paths    = paths,
         subgroup = FALSE)