tidyLPA: estimate_profiles – R documentation

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estimate_profiles

Estimate latent profiles

Description

Estimates latent profiles (finite mixture models) using the open source package mclust, 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; 'mclust' or 'MplusAutomation' (requires Mplus to be installed). Default: 'mclust'.
`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., `Mclust`, or `mplusModeler`.

Details

Six models are currently available in tidyLPA, corresponding to the most common requirements. These are:

Equal variances and covariances fixed to 0
Varying variances and covariances fixed to 0
Equal variances and equal covariances
Varying variances and equal covariances (not able to be fit w/ mclust)
Equal variances and varying covariances (not able to be fit w/ mclust)
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 mclust is used, models = c(1, 2, 3, 6) are the only models available.

Value

A list of class 'tidyLPA'.

Examples

iris_sample <- iris[c(1:4, 51:54, 101:104), ] # to make example run more quickly

# 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"))

tidyLPA

Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software

v1.0.8

MIT + file LICENSE

Authors

Joshua M Rosenberg [aut, cre], Caspar van Lissa [aut], Jennifer A Schmidt [ctb], Patrick N Beymer [ctb], Daniel Anderson [ctb], Matthew J. Schell [ctb]

Initial release