ISMatricesLoop: Internal function to loop Over Indicator Saturation Matrices...

In gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Internal function to loop Over Indicator Saturation Matrices with Block-Based GETS

Description

Loops through each IS matrix and performs block-based GETS model selection, using optional parallelisation.

Usage

ISMatricesLoop(blocks.is.list, ISmatrices, ratio.threshold, mXncol,
  parallel.options, i, ISblocks, y, y.n, mX, userEstArg, t.pval, wald.pval, 
  do.pet, arLjungB, archLjungB, normality.JarqueB, user.diagnostics, gofFunArg, 
  gof.method, mxkeep, include.gum, include.1cut, include.empty, max.paths, 
  turbo, tol, LAPACK, max.regs, print.searchinfo, clusterSpec, clusterVarlist, 
  blocks, max.block.size, mXnames, additional.block.search)

Arguments

blocks.is.list	Logical; if TRUE, assumes that ISblocks already contains a user-supplied block structure. If FALSE, blocks are generated internally.
ISmatrices	List of indicator saturation matrices, typically generated by create.ISmatrices, containing IIS, SIS, TIS, or UIS.
ratio.threshold	Numeric value used to determine the minimum ratio of observations to regressors in each block.
mXncol	Number of columns in the original regressor matrix mX, used to ensure feasible model sizes.
parallel.options	If not NULL, enables parallel processing with specified number of cores or a cluster object.
i	Index of the current IS matrix in ISmatrices being processed.
ISblocks	List of index blocks for each IS matrix. Can be partially pre-defined or built internally.
y	Response variable, a numeric vector or time-series.
y.n	Number of observations in the response variable.
mX	Matrix of regressors excluding indicator variables.
userEstArg	List specifying the user-defined estimator function and its arguments, passed to getsFun.
t.pval	Significance threshold for t-tests in the GETS model selection process.
wald.pval	Significance threshold for Wald tests in the GETS process.
do.pet	Logical; if TRUE, Parsimonious Encompassing Tests are performed during deletion steps.
arLjungB	List with AR order and significance level for the Ljung-Box test for autocorrelation in residuals.
archLjungB	List with ARCH order and significance level for testing heteroskedasticity in residuals.
normality.JarqueB	Significance level for the Jarque-Bera normality test. If NULL, the test is skipped.
user.diagnostics	List containing user-specified diagnostics function and p-value settings, passed to getsFun.
gofFunArg	List specifying the name and parameters of the goodness-of-fit function used.
gof.method	Character string specifying whether the GOF criterion is minimized ("min") or maximized ("max").
mxkeep	Integer indices of regressors that are forced to be retained in the model.
include.gum	Logical; if TRUE, the General Unrestricted Model is included as a terminal model.
include.1cut	Logical; if TRUE, the one-cut model is included as a terminal model.
include.empty	Logical; if TRUE, the empty model is included among terminal models.
max.paths	Maximum number of paths to explore in GETS model selection. If NULL, no limit is imposed.
turbo	Logical; if TRUE, avoids redundant path searches in GETS.
tol	Tolerance threshold for detecting multicollinearity among regressors.
LAPACK	Logical; if TRUE, uses LAPACK for QR decomposition in collinearity checks.
max.regs	Maximum number of regressions to perform per GETS path.
print.searchinfo	Logical; if TRUE, progress and diagnostics messages are printed during execution.
clusterSpec	Specification object passed to makeCluster to control parallel processing.
clusterVarlist	Character vector listing variable names to be exported to each cluster worker.
blocks	Integer specifying the number of blocks to divide the IS matrix into. If NULL, determined automatically.
max.block.size	Maximum number of indicators per block used during block construction.
mXnames	Character vector of regressor names in mX, used to identify retained terms.
additional.block.search	Default is FALSE. If isat() identifies (retains) too many indicators in the block search, a sensible model might not be estimable (e.g. due to a lack of available degrees of freedom). In this case, setting additional.block.search = TRUE carries out an additional block search using a Leave-one-Out method (e.g. for three blocks, indicators are assigned to block 1,2,3,1,2,3,1,2,3). This is an experimental feature.

Value

A list with:

ISfinalmodel	Vector of selected indicator names.
estimations.counter	Updated estimation counter.
getsFun.counter	Updated function call counter.
ISblocks	Final block structure.

See Also

See also isat and ISadditionalblocksearch

gets documentation built on July 29, 2026, 9:07 a.m.