prune {psychonetrics}

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)