Loops through each IS matrix and performs block-based GETS model selection, using optional parallelisation.
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)A list with:
Vector of selected indicator names.
Updated estimation counter.
Updated function call counter.
Final block structure.
Logical; if TRUE, assumes that ISblocks already contains a
user-supplied block structure. If FALSE, blocks are generated
internally.
List of indicator saturation matrices, typically generated by
create.ISmatrices, containing IIS, SIS, TIS, or UIS.
Numeric value used to determine the minimum ratio of observations to regressors in each block.
Number of columns in the original regressor matrix mX, used to
ensure feasible model sizes.
If not NULL, enables parallel processing with specified number of
cores or a cluster object.
Index of the current IS matrix in ISmatrices being processed.
List of index blocks for each IS matrix. Can be partially pre-defined or built internally.
Response variable, a numeric vector or time-series.
Number of observations in the response variable.
Matrix of regressors excluding indicator variables.
List specifying the user-defined estimator function and its arguments,
passed to getsFun.
Significance threshold for t-tests in the GETS model selection process.
Significance threshold for Wald tests in the GETS process.
Logical; if TRUE, Parsimonious Encompassing Tests are performed
during deletion steps.
List with AR order and significance level for the Ljung-Box test for autocorrelation in residuals.
List with ARCH order and significance level for testing heteroskedasticity in residuals.
Significance level for the Jarque-Bera normality test. If NULL,
the test is skipped.
List containing user-specified diagnostics function and p-value settings,
passed to getsFun.
List specifying the name and parameters of the goodness-of-fit function used.
Character string specifying whether the GOF criterion is
minimized ("min") or maximized ("max").
Integer indices of regressors that are forced to be retained in the model.
Logical; if TRUE, the General Unrestricted Model is included as a
terminal model.
Logical; if TRUE, the one-cut model is included as a terminal model.
Logical; if TRUE, the empty model is included among terminal models.
Maximum number of paths to explore in GETS model selection. If NULL,
no limit is imposed.
Logical; if TRUE, avoids redundant path searches in GETS.
Tolerance threshold for detecting multicollinearity among regressors.
Logical; if TRUE, uses LAPACK for QR decomposition in collinearity
checks.
Maximum number of regressions to perform per GETS path.
Logical; if TRUE, progress and diagnostics messages are printed
during execution.
Specification object passed to makeCluster to control parallel
processing.
Character vector listing variable names to be exported to each cluster worker.
Integer specifying the number of blocks to divide the IS matrix into.
If NULL, determined automatically.
Maximum number of indicators per block used during block construction.
Character vector of regressor names in mX, used to identify
retained terms.
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.
See also isat and ISadditionalblocksearch