psychonetrics (version 0.13.1)

partialprune: Partial pruning of multi-group models

Description

This function will search for a multi-group model with equality constrains on some but not all parameters. This is called partial pruning (Epskamp, Isvoranu, & Cheung, 2020; Haslbeck, 2020). The algorithm is as follows: 1. remove all parameters not significant at alpha in all groups (without equality constrains), 2. create a union model with all parameters included in any group included in all groups and constrained equal. 3. Stepwise free equality constrains of the parameter that features the largest sum of modification indices until BIC can no longer be improved. 4. Select and return (by default) the best model according to BIC (original model, pruned model, union model and partially pruned model).

Usage

partialprune(x, alpha = 0.01, matrices, verbose, 
             combinefun = c("unionmodel", "intersectionmodel", "identity"), 
             return = c("partialprune","best","union_equal","prune"),
             criterion = "bic",  best = c("lowest","highest"),
             final_prune = c("saturated","partialprune"), ...)

Arguments

x

A psychonetrics model.

alpha

Significance level to use.

matrices

Vector of strings indicating which matrices should be pruned. Will default to network structures.

verbose

Logical, should messages be printed?

combinefun

Function used to combine models of different groups.

return

What model to return? "best" for best fitting model (according to BIC), "partialprune" for the partialpruned model, "union_equal" for the union model with equality constraints, and "prune" for the originally pruned model without equality constraints.

best

Should the lowest or the highest index of criterion be used to select the final model?

criterion

What criterion to use for the model selection in the last step? Defaults to "bic" for BIC selection.

final_prune

Should the last prune step be based on removing edges not significant in the last model in the partialprune algorithm or the first model (saturated model) in the algorithm? Defaults to "saturated". Set to "partialprune" to mimic psychonetrics < 0.13.1 behavior.

...

Arguments sent to prune.

Author

Sacha Epskamp <[e-mail]>

References

Epskamp, S., Isvoranu, A. M., & Cheung, M. (2020). Meta-analytic gaussian network aggregation. PsyArXiv preprint. DOI:10.31234/osf.io/236w8.

Haslbeck, J. (2020). Estimating Group Differences in Network Models using Moderation Analysis. PsyArXiv preprint. DOI:10.31234/osf.io/926pv.

Examples

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

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

# Let's take the extroversion items, and gender:
ExData <- bfi %>%
  select(E1:E5, gender) %>% 
  na.omit # Let's remove missingness (otherwise use Estimator = "FIML)

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

# Saturated estimation:
mod_saturated <- ggm(ExData, 
                     vars = vars,
                     groups = "gender")

# Partial prune model:
mod_partial <- mod_saturated 
  partialprune

# Obtain the networks:
getmatrix(mod_partial, "omega")

# Differences:
getmatrix(mod_partial, "omega")[[1]] -
  getmatrix(mod_partial, "omega")[[2]]

# Difference detected in edge 4 - 5
# }