runmodel: Run a psychonetrics model

In psychonetrics: Structural Equation Modeling and Confirmatory Network Analysis

Run a psychonetrics model

Description

This is the main function used to run a psychonetrics model.

Usage

runmodel(x, level = c("gradient", "fitfunction"), addfit =
                 TRUE, addMIs = TRUE, addSEs = TRUE, addInformation =
                 TRUE, log = TRUE, verbose, optim.control,
                 analyticFisher = TRUE, warn_improper = TRUE,
                 warn_gradient = TRUE, return_improper = TRUE, bounded
                 = TRUE)

Arguments

x	A psychonetrics model.
level	Level at which the model should be estimated. Defaults to "gradient" to indicate the analytic gradient should be used.
addfit	Logical, should fit measures be added?
addMIs	Logical, should modification indices be added?
addSEs	Logical, should standard errors be added?
addInformation	Logical, should the Fisher information be added?
log	Logical, should the log be updated?
verbose	Logical, should messages be printed?
optim.control	A list with options for optimr
analyticFisher	Logical, should the analytic Fisher information be used? If FALSE, numeric information is used instead.
return_improper	Should a result in which improper computation was used be return? Improper computation can mean that a pseudoinverse of small spectral shift was used in computing the inverse of a matrix.
warn_improper	Logical. Should a warning be given when at some point in the estimation a pseudoinverse was used?
warn_gradient	Logical. Should a warning be given when the average absolute gradient is > 1?
bounded	Logical. Should bounded estimation be used (e.g., variances should be positive)?

Value

An object of the class psychonetrics (psychonetrics-class)

Author(s)

Sacha Epskamp

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

psychonetrics documentation built on Oct. 3, 2023, 5:09 p.m.