Group iterative multiple model estimation.
Description
This function identifies structural equation models for each individual that consist of both grouplevel and individuallevel paths.
Usage
1 2 3 4 5 6 7 8 9 10 11 
Arguments
data 
The path to the directory where the data files are located, or the name of the list containing each individual's time series. Each file or matrix 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 (optional). If specified, a copy of output files will be replaced in directory. If directory at specified path does not exist, it will be created. 
sep 
The spacing of the data files. "" indicates spacedelimited, "/t" indicates tabdelimited, "," indicates comma delimited. Only necessary to specify if reading data in from physical directory. 
header 
Logical. Indicate TRUE for data files with a header. Only necessary to specify if reading data in from physical directory. 
ar 
Logical. If TRUE, begins search for group model with autoregressive (AR) paths open. Defaults to TRUE. 
plot 
Logical. If TRUE, graphs depicting relations among variables of interest will automatically be created. For individuallevel plots, red paths represent positive weights and blue paths represent negative weights. For the grouplevel plot, black represents grouplevel paths, grey represents individuallevel paths, and (if subgroup = TRUE) green represents subgrouplevel paths. For the grouplevel plot, the width of the edge corresponds to the count. Defaults to TRUE. 
subgroup 
Logical. If TRUE, subgroups are generated based on similarities
in model features using the 
paths 

groupcutoff 
Cutoff value for group level paths. Defaults to .75, indicating that a path must be significant across 75% of individuals to be included as a grouplevel path. 
subcutoff 
Cutoff value for subgroup level paths. Defaults to .5, indicating that a path must be significant across at least 50% of the individuals in a subgroup to be considered a subgrouplevel path. 
diagnos 
In development. Defaults to FALSE. 
Details
In main output directory:
indivPathEstimates Contains estimate, standard error, pvalue, and zvalue for each path for each individual. If subgroup = TRUE and subgroups are found, then a column is present containing the subgroup membership for each individual. Also contains the level at which each path was estimated: group, subgroup, or individual.
summaryFit Contains model fit information for individuallevel 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 individuallevel.
summaryPathsPlot Produced if plot = TRUE. Contains figure with group, subgroup (if subgroup = TRUE), and individuallevel paths for the sample. Black paths are grouplevel, green paths are subgrouplevel, and grey paths are individuallevel, where the thickness of the line represents the count.
In subgroup output directory (if subgroup = TRUE):
subgroupkPathCounts Contains counts of relations among lagged and contemporaneous variables for the kth subgroup.
subgroupkPlot Contains plot of group, subgroup, and individual level paths for the kth subgroup. Black represents grouplevel paths, grey represents individuallevel paths, and green represents subgrouplevel paths.
Note: if a subgroup of size n = 1 is discovered, subgrouplevel output is not produced.
In individual output directory (where id represents the
original file name for each individual):
idBetas Contains individuallevel estimates of each path for each individual.
idStdErrors Contains individuallevel standard errors for each path for each individual.
idPlot Contains individuallevel plots. Red paths represent positive weights and blue paths represent negative weights.
Author(s)
Stephanie Lane
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, 310319.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Not run:
paths < 'V2 ~ V1
V3 ~ V4lag'
fit < gimmeSEM(data = simData,
out = "C:/simData_out",
subgroup = TRUE)
print(fit, mean = TRUE)
print(fit, subgroup = 1, mean = TRUE)
print(fit, file = "group_1_1", estimates = TRUE)
print(fit, subgroup = 2, fitMeasures = TRUE)
plot(fit, file = "group_1_1")
## End(Not run)
