psychonetrics (version 0.13)

runmodel: 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 = FALSE,
                   warn_gradient = TRUE, warn_bounds = TRUE,
                   return_improper = TRUE, bounded = TRUE,
                   approximate_SEs = FALSE)

Value

An object of the class psychonetrics (psychonetrics-class)

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?

Author

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