prune function

Stepdown model search by pruning non-significant parameters.

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

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

Returns

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)