prune: Stepdown model search by pruning non-significant parameters.

In SachaEpskamp/psychonetrics: Structural Equation Modeling and Confirmatory Network Analysis

Stepdown model search by pruning non-significant parameters.

Description

This function will (recursively) remove parameters that are not significant and refit the model.

Usage

prune(x, alpha = 0.01, adjust = c("none", "holm",
                    "hochberg", "hommel", "bonferroni", "BH", "BY",
                    "fdr"), matrices, runmodel = TRUE, recursive = FALSE,
                    verbose, log = TRUE, identify = TRUE, startreduce = 1,
                    limit = Inf, mode = c("tested","all"), ...)

Arguments

x	A psychonetrics model.
alpha	Significance level to use.
adjust	p-value adjustment method to use. See p.adjust.
matrices	Vector of strings indicating which matrices should be pruned. Will default to network structures.
runmodel	Logical, should the model be evaluated after pruning?
recursive	Logical, should the pruning process be repeated?
verbose	Logical, should messages be printed?
log	Logical, should the log be updated?
identify	Logical, should models be identified automatically?
startreduce	A numeric value indicating a factor with which the starting values should be reduced. Can be useful when encountering numeric problems.
limit	The maximum number of parameters to be pruned.
mode	Mode for adjusting for multiple comparisons. Should all parameters be considered as the total number of tests or only the tested parameters (parameters of interest)?
...	Arguments sent to runmodel

Value

An object of the class psychonetrics (psychonetrics-class)

Author(s)

Sacha Epskamp

See Also

stepup

Examples

# Load bfi data from psych package:
library("psychTools")
data(bfi)

# Also load dplyr for the pipe operator:
library("dplyr")

# Let's take the agreeableness items, and gender:
ConsData <- bfi %>% 
  select(A1:A5, gender) %>% 
  na.omit # Let's remove missingness (otherwise use Estimator = "FIML)

# Define variables:
vars <- names(ConsData)[1:5]

# Let's fit a full GGM:
mod <- ggm(ConsData, vars = vars, omega = "full")

# Run model:
mod <- mod %>% runmodel

# Prune model:
mod <- mod %>% prune(adjust = "fdr", recursive = FALSE)

SachaEpskamp/psychonetrics documentation built on Sept. 1, 2023, 3:40 a.m.