inst/doc/modification_indices.R

In umx: Structural Equation Modeling and Twin Modeling in R

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 5
)
library(umx)
library(OpenMx)
umx_set_auto_plot(FALSE)

## ----data-setup---------------------------------------------------------------
data("HS.ability.data", package = "OpenMx")

manifests = c("visual", "cubes", "flags", "paragrap", "sentence", "wordm",
  "addition", "counting", "straight")

# Scale for stable optimization (recommended for this dataset in umx)
df = umx_scale(HS.ability.data)

## ----one-factor, results = "hide", message = FALSE----------------------------
m1 = umxRAM("Holzinger_1f", data = df,
  umxPath("g", to = manifests),
  umxPath(v.m. = manifests),
  umxPath(v1m0 = "g")
)

## ----one-factor-summary, message = FALSE--------------------------------------
umxSummary(m1, refModels = FALSE)

## ----one-factor-mi, message = FALSE-------------------------------------------
umxMI(m1)

## ----three-factor, results = "hide", message = FALSE--------------------------
model3f = "
  spatial =~ visual + cubes + flags
  verbal  =~ paragrap + sentence + wordm
  speed   =~ addition + counting + straight
"
m3 = umxRAM(model3f, data = df, name = "Holzinger_3f")

## ----three-factor-summary, message = FALSE------------------------------------
umxSummary(m3, refModels = FALSE)

## ----three-factor-compare, message = FALSE------------------------------------
umxCompare(m3, m1)

## ----bifactor-multigroup-demo, eval = FALSE-----------------------------------
# # Bifactor + umxMI, or multi-group one-factor by sex
# modelBifactor = "
#   g =~ visual + cubes + flags + paragrap + sentence + wordm + addition + counting + straight
#   spatial =~ visual + cubes + flags
#   verbal  =~ paragrap + sentence + wordm
#   speed   =~ addition + counting + straight
#   spatial ~~ 0*verbal + 0*speed + 0*g
#   verbal  ~~ 0*speed + 0*g
#   speed   ~~ 0*g
# "
# mBio = umxRAM(modelBifactor, data = df, name = "Holzinger_bifactor")
# umxMI(mBio)
# 
# mMG = umxRAM("Holzinger_1f_MG", data = df, group = "Gender",
#   umxPath("g", to = manifests),
#   umxPath(v.m. = manifests),
#   umxPath(v1m0 = "g")
# )
# umxMI(mMG)