runmodel function

Run a psychonetrics model

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

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

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)?
• approximate_SEs: Logical, should standard errors be approximated? If true, an approximate matrix inverse of the Fischer information is used to obtain the standard errors.
• warn_bounds: Should a warning be given when a parameter is estimated near its bounds?

Returns

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