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).
estimate_profiles(
df,
n_profiles,
models = NULL,
variances = "equal",
covariances = "zero",
package = "mclust",
select_vars = NULL,
...
)data.frame of numeric data; continuous indicators are required for mixture modeling.
Integer vector of the number of profiles (or mixture components) to be estimated.
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.
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.
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).
Character. Which package to use; 'OpenMx', 'mclust', or 'MplusAutomation' (requires Mplus to be installed). Default: 'OpenMx'.
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.
A list of class 'tidyLPA'.
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:
Equal variances across classes; no covariances between observed variables
Varying variances across classes; no covariances between observed variables
Equal variances and equal covariances across classes
Varying variances and equal covariances (not available for package = 'mclust')
Equal variances and varying covariances (not available for package = '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 package = 'mclust' is used,
models = c(4, 5) are not available. Use package = 'OpenMx' or
package = 'Mplus' to estimate these models.
# 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)
#> tidyLPA analysis using mclust:
#>
#> Model Classes AIC BIC Entropy prob_min prob_max n_min n_max BLRT_p
#> 1 1 3 178.42 199.79 0.98 0.99 1.00 0.21 0.50 0.01
# \donttest{
# Example 2:
iris %>%
subset(select = c("Sepal.Length", "Sepal.Width",
"Petal.Length")) %>%
estimate_profiles(n_profiles = 1:4, models = 1:3)
#> The 'variances'/'covariances' arguments were ignored in favor of the 'models' argument.
#> tidyLPA analysis using mclust:
#>
#> Model Classes AIC BIC Entropy prob_min prob_max n_min n_max BLRT_p
#> 1 1 1 1150.81 1168.87 1.00 1.00 1.00 1.00 1.00
#> 2 1 2 885.64 915.74 0.99 1.00 1.00 0.34 0.66 0.01
#> 3 1 3 765.16 807.31 0.93 0.94 1.00 0.27 0.40 0.01
#> 4 1 4 758.71 812.90 0.87 0.70 0.96 0.09 0.39 0.01
#> 5 2 1 1150.81 1168.87 1.00 1.00 1.00 1.00 1.00
#> 6 2 2 770.96 810.10 1.00 1.00 1.00 0.33 0.67 0.01
#> 7 2 3 702.55 762.76 0.89 0.91 1.00 0.31 0.35 0.01
#> 8 2 4 695.51 776.80 0.88 0.91 0.95 0.09 0.35 0.06
#> 9 3 1 857.33 893.46 1.00 1.00 1.00 1.00 1.00
#> 10 3 2 699.26 747.43 1.00 1.00 1.00 0.33 0.67 0.01
#> 11 3 3 656.14 716.36 0.92 0.94 1.00 0.33 0.34 0.01
#> 12 3 4 652.81 725.06 0.90 0.71 0.97 0.05 0.34 0.05
# 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 analysis using mclust:
#>
#> Model Classes AIC BIC Entropy prob_min prob_max n_min n_max BLRT_p
#> 1 1 1 266.05 275.21 1.00 1.00 1.00 1.00 1.00
#> 2 1 2 210.85 226.12 0.97 0.98 1.00 0.32 0.68 0.01
#> 3 1 3 178.42 199.79 0.98 0.99 1.00 0.21 0.50 0.01
#> 4 1 4 182.67 210.14 0.94 0.87 1.00 0.09 0.50 0.45
#> 5 2 1 266.05 275.21 1.00 1.00 1.00 1.00 1.00
#> 6 2 2 178.74 198.58 1.00 1.00 1.00 0.29 0.71 0.01
#> 7 2 3 161.79 192.32 0.97 0.98 1.00 0.21 0.50 0.01
#> 8 2 4 164.95 206.16 0.96 0.95 1.00 0.12 0.50 0.58
# }