estimate_profiles: Estimate latent profiles

In jrosen48/tidyLPA: Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software

Estimate latent profiles

Description

Estimates latent profiles (finite mixture models) using either the open source package mclust or [OpenMx:mxModel]{OpenMx}, or the commercial program Mplus (using the R-interface of MplusAutomation).

Usage

estimate_profiles(
  df,
  n_profiles,
  models = NULL,
  variances = "equal",
  covariances = "zero",
  package = "mclust",
  select_vars = NULL,
  ...
)

Arguments

df	data.frame of numeric data; continuous indicators are required for mixture modeling.
n_profiles	Integer vector of the number of profiles (or mixture components) to be estimated.
models	Integer vector. Set to NULL by default, and models are constructed from the variances and covariances arguments. See Details for the six models available in tidyLPA.
variances	Character vector. Specifies which variance components to estimate. Defaults to "equal" (constrain variances across profiles); the other option is "varying" (estimate variances freely across profiles). Each element of this vector refers to one of the models you wish to run.
covariances	Character vector. Specifies which covariance components to estimate. Defaults to "zero" (do not estimate covariances; this corresponds to an assumption of conditional independence of the indicators); other options are "equal" (estimate covariances between items, constrained across profiles), and "varying" (free covariances across profiles).
package	Character. Which package to use; 'OpenMx', 'mclust', or 'MplusAutomation' (requires Mplus to be installed). Default: 'OpenMx'.
select_vars	Character. Optional vector of variable names in df, to be used for model estimation. Defaults to NULL, which means all variables in df are used.
...	Additional arguments are passed to the estimating function; i.e., mxRun, Mclust, or mplusModeler.

Details

Six models are currently available in tidyLPA, corresponding to the most common requirements. All models estimate the observed variable means for each class. The remaining parameters are:

1. Equal variances across classes; no covariances between observed variables

2. Varying variances across classes; no covariances between observed variables

3. Equal variances and equal covariances across classes

4. Varying variances and equal covariances (not available for package = 'mclust')

5. Equal variances and varying covariances (not available for package = 'mclust')

6. Varying variances and varying covariances

Two interfaces are available to estimate these models; specify their numbers in the models argument (e.g., models = 1, or models = c(1, 2, 3)), or specify the variances/covariances to be estimated (e.g.,: variances = c("equal", "varying"), covariances = c("zero", "equal")). Note that when package = 'mclust' is used, models = c(4, 5) are not available. Use package = 'OpenMx' or package = 'Mplus' to estimate these models.

Value

A list of class 'tidyLPA'.

Examples

# to make example run more quickly
iris_sample <- iris[c(1:10, 51:60, 101:114), ]

# Example 1:
iris_sample %>%
  subset(select = c("Sepal.Length", "Sepal.Width",
    "Petal.Length")) %>%
  estimate_profiles(3)


# Example 2:
iris %>%
  subset(select = c("Sepal.Length", "Sepal.Width",
    "Petal.Length")) %>%
  estimate_profiles(n_profiles = 1:4, models = 1:3)

# Example 3:
iris_sample %>%
  subset(select = c("Sepal.Length", "Sepal.Width",
    "Petal.Length")) %>%
  estimate_profiles(n_profiles = 1:4, variances = c("equal", "varying"),
                    covariances = c("zero", "zero"))

jrosen48/tidyLPA documentation built on Feb. 23, 2024, 11:33 p.m.