semtree.control: SEM Tree Control Object

In semtree: Recursive Partitioning for Structural Equation Models

SEM Tree Control Object

Description

A semtree.control object contains parameters that determine the tree growing process. These parameters include choices of different split candidate selection procedures and hyperparameters of those. Calling the constructor without parameters creates a default control object. A number of tree growing methods are included in with this package: 1. 'naive' splitting takes the best split value of all possible splits on each covariate. 2. 'fair' selection is so called because it tests all splits on half of the data, then tests the best split value for each covariate on the other half of the data. The equal footing of each covariate in this two phase test removes bias from testing variables with many possible splits compared to those with few. 3. "fair3" does the phases described above, with an additional step of retesting all of the split values on the best covariate found in the second phase. Variations in the sample from subsetting are removed and bias in split selection further reduced. 4. 'score' implements modern score-based statistics.

Usage

semtree.control(
  method = c("naive", "score", "fair", "fair3"),
  min.N = 20,
  max.depth = NA,
  alpha = 0.05,
  alpha.invariance = NA,
  folds = 5,
  exclude.heywood = TRUE,
  progress.bar = TRUE,
  verbose = FALSE,
  bonferroni = FALSE,
  use.all = FALSE,
  seed = NA,
  custom.stopping.rule = NA,
  mtry = NA,
  report.level = 0,
  exclude.code = NA,
  linear = TRUE,
  min.bucket = 10,
  naive.bonferroni.type = 0,
  missing = "ignore",
  use.maxlm = FALSE,
  strucchange.from = 0.15,
  strucchange.to = NULL,
  strucchange.nrep = 50000,
  refit = TRUE,
  ctsem_sd = FALSE
)

Arguments

method	Default: 'naive'. One out of c("score","fair","naive") for either an unbiased two-step selection algorithm, a naive take-the-best, or a score-based testing scheme.
min.N	Default: 10. Minimum sample size per a node, used to determine whether to continue splitting a tree or establish a terminal node.
max.depth	Default: NA. Maximum levels per a branch. Parameter for limiting tree growth.
alpha	Default: 0.05. Significance level for splitting at a given node.
alpha.invariance	Default: NA. Significance level for invariance tests. If NA, the value of alpha is used.
folds	Default: 5. Defines the number of folds for the "cv" method.
exclude.heywood	Default: TRUE. Reports whether there is an identification problem in the covariance structure of an SEM tested.
progress.bar	Default: NA. Option to disable the progress bar for tree growth.
verbose	Default: FALSE. Option to turn on or off all model messages during tree growth.
bonferroni	Default: FALSE. Correct for multiple tests with Bonferroni type correction.
use.all	Treatment of missing variables. By default, missing values stay in a decision node. If TRUE, cases are distributed according to a maximum likelihood principle to the child nodes.
seed	Default: NA. Set a random number seed for repeating random fold generation in tree analysis.
custom.stopping.rule	Default: NA. Otherwise, this can be a boolean function with a custom stopping rule for tree growing.
mtry	Default: NA. Number of sample columns to use in SEMforest analysis.
report.level	Default: 0. Values up to 99 can be used to increase the number of onscreen reports for semtree analysis.
exclude.code	Default: NA. NPSOL error code for exclusion from model fit evaluations when finding best split. Default: Models with errors during fitting are retained.
linear	If TRUE (default), the structural equation model is assumed to not contain any nonlinear parameter constraints and scores are computed analytically, resulting in a shorter runtime. Only relevant for models fitted with OpenMx.
min.bucket	Minimum bucket size. This is the minimum size any node must have, such that a given split is considered valid. Minimum bucket size is a lower bound to the sample size in the terminal nodes of a tree.
naive.bonferroni.type	Default: 0. When set to zero, bonferroni correction for the naive test counts the number of dichotomous tests. When set to one, bonferroni correction counts the number of variables tested.
missing	Missing value treatment. Default is ignore
use.maxlm	Use MaxLR statistic for split point selection (as proposed by Arnold et al., 2021)
strucchange.from	Strucchange argument. See their package documentation.
strucchange.to	Strucchange argument. See their package documentation.
strucchange.nrep	Strucchange argument. See their package documentation.
refit	If TRUE (default) the initial model is fitted on the data provided to semtree.
ctsem_sd	If FALSE (default) no standard errors of CT model parameters are computed. Requesting standard errors increases runtime.

Value

A control object containing a list of the above parameters.

Author(s)

Andreas M. Brandmaier, John J. Prindle, Manuel Arnold

References

Brandmaier, A.M., Oertzen, T. v., McArdle, J.J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18(1), 71-86.

Arnold, M., Voelkle, M. C., & Brandmaier, A. M. (2021). Score-guided structural equation model trees. Frontiers in Psychology, 11, Article 564403. https://doi.org/10.3389/fpsyg.2020.564403

See Also

semtree

Examples

	# create a control object with an alpha level of 1%
	my.control <- semtree.control(alpha=0.01)

	# set the minimum number of cases per node to ten
	my.control$min.N <- 10
	
	# print contents of the control object
	print(my.control)

semtree documentation built on May 29, 2024, 4:05 a.m.