aggSEM: Group-level structural equation model search.

In gimme: Group Iterative Multiple Model Estimation

Group-level structural equation model search.

Description

Concatenates all individual-level data files and fits a group model to the data.

Usage

aggSEM(data   = "",
       out    = "",
       sep    = "",
       header = "",
       ar     = TRUE,
       plot   = TRUE,
       paths  = NULL,
       exogenous = NULL, 
       outcome   = NULL, 
       conv_vars        = NULL,
       conv_length      = 16, 
       conv_interval    = 1, 
       mult_vars        = NULL,
       mean_center_mult = FALSE,
       standardize      = FALSE,
       hybrid = FALSE,
       VAR    = FALSE)

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. If in list form, each item in the list (i.e., matrix) must be named.
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 when data are in a directory. "" indicates space-delimited, "/t" indicates tab-delimited, "," indicates comma delimited. Only necessary to specify if reading data in from physical directory.
header	Logical. Indicate TRUE for data files with a header, FALSE otherwise. 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, figures depicting relations among variables of interest will automatically be created. For aggregate-level plot, red paths represent positive weights and blue paths represent negative weights. Dashed lines denote lagged relations (lag 1) and solid lines are contemporaneous (lag 0). Defaults to TRUE.
paths	lavaan-style syntax containing paths with which to begin model estimation (optional). That is, Y~X indicates that Y is regressed on X, or X predicts Y. Paths can also be set to a specific value for estimation using lavaan-style syntax (e.g., 'V4 ~ 0.5*V3'), or set to 0 so that they will not be estimated (e.g., 'V4 ~ 0*V3'). If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. To reference lag variables, "lag" should be added to the end of the variable name with no separation. Defaults to NULL.
exogenous	Vector of variable names to be treated as exogenous (optional). That is, exogenous variable X can predict Y but cannot be predicted by Y. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names. Defaults to NULL.
outcome	Vector of variable names to be treated as outcome (optional). This is a variable that can be predicted by others but cannot predict. If no header is used, then variables should be referred to with V followed (with no separation) by the column number. If a header is used, variables should be referred to using variable names.
conv_vars	Vector of variable names to be convolved via smoothed Finite Impulse Response (sFIR). Defaults to NULL.
conv_length	Expected response length in seconds. For functional MRI BOLD, 16 seconds (default) is typical for the hemodynamic response function.
conv_interval	Interval between data acquisition. Currently conv_length/conv_interval must be a constant. For fMRI studies, this is the repetition time. Defaults to 1.
mult_vars	Vector of variable names to be multiplied to explore bilinear/modulatory effects (optional). All multiplied variables will be treated as exogenous (X can predict Y but cannot be predicted by Y). Within the vector, multiplication of two variables should be indicated with an asterik (e.g. V1*V2). If no header is used, variables should be referred to with V followed by the column number (with no separation). If a header is used, each variable should be referred to using variable names. If multiplication with the lag 1 of a variable is desired, the variable name should be followed by "lag" with no separation (e.g. V1*V2lag). Note that if multiplied variables are desired, at least one variable in the dataset must be specified as exogenous. Defaults to NULL.
mean_center_mult	Logical. If TRUE, the variables indicated in mult_vars will be mean-centered before being multiplied together. Defaults to FALSE.
standardize	Logical. If TRUE, all variables will be standardized to have a mean of zero and a standard deviation of one. Defaults to FALSE.
hybrid	Logical. If TRUE, enables hybrid-VAR models where both directed contemporaneous paths and contemporaneous covariances among residuals are candidate relations in the search space. Defaults to FALSE.
VAR	Logical. If true, VAR models where contemporaneous covariances among residuals are candidate relations in the search space. Defaults to FALSE.

Details

In main output directory:

• allBetas Matrix. Contains estimates for each path in the aggregate-level model. The row variable is the outcome and the column variable is the predictor variable.

• allStdErrors Matrix. Contains standard errors for each path in the aggregate-level model. The row variable is the outcome and the column variable is the predictor variable.

• allPathEstimates Contains estimate, standard error, p-value, and z-value for each path for the concatenated data.

• summaryFit Contains model fit information for the aggregate-level model.

• summaryPathsPlot Contains aggregate-level plot. Red paths represent positive weights and blue paths represent negative weights.

Author(s)

Stephanie Lane

Examples

## Not run: 
exFit <- aggSEM(data = ts)

## End(Not run)

plot(exFit)

gimme documentation built on Aug. 30, 2023, 1:08 a.m.