inst/doc/Introduction_to_tidyLPA.R

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

## ----echo = FALSE-------------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)

## ----eval = FALSE-------------------------------------------------------------
# install.packages("tidyLPA")

## ----gh-installation, eval = FALSE--------------------------------------------
# install.packages("devtools")
# devtools::install_github("data-edu/tidyLPA")

## ----message = F--------------------------------------------------------------
library(tidyLPA)
library(dplyr)

## -----------------------------------------------------------------------------
pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    single_imputation() %>%
    estimate_profiles(3)

## ----eval = FALSE-------------------------------------------------------------
# pisaUSA15[1:100, ] %>%
#     select(broad_interest, enjoyment, self_efficacy) %>%
#     single_imputation() %>%
#     estimate_profiles(3, package = "MplusAutomation")

## ----eval = TRUE--------------------------------------------------------------
set.seed(42)
pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    single_imputation() %>%
    scale() %>%
    estimate_profiles(3) %>% 
    plot_profiles()

## ----eval = TRUE--------------------------------------------------------------
pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    single_imputation() %>%
    estimate_profiles(1:3, 
                      variances = c("equal", "varying"),
                      covariances = c("zero", "varying")) %>%
    compare_solutions(statistics = c("AIC", "BIC"))

## ----eval = FALSE-------------------------------------------------------------
# pisaUSA15[1:100, ] %>%
#     select(broad_interest, enjoyment, self_efficacy) %>%
#     single_imputation() %>%
#     estimate_profiles(3,
#                       package = "mplus",
#                       ANALYSIS = "starts = 100, 20;")

## ----eval = FALSE-------------------------------------------------------------
# pisaUSA15[1:100, ] %>%
#     select(broad_interest, enjoyment, self_efficacy) %>%
#     single_imputation() %>%
#     estimate_profiles(3,
#                       prior = priorControl())

## ----eval = TRUE--------------------------------------------------------------
pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    estimate_profiles(3, 
                      variances = "varying",
                      covariances = "varying")

## ----eval = TRUE--------------------------------------------------------------
m3 <- pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    estimate_profiles(3)

get_estimates(m3)

## ----eval = TRUE--------------------------------------------------------------
pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    scale() %>%
    estimate_profiles(4) %>%
    plot_profiles()

pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    poms() %>%
    estimate_profiles(4) %>%
    plot_profiles()

## ----eval = TRUE--------------------------------------------------------------
get_data(m3)

## -----------------------------------------------------------------------------
m4 <- pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    single_imputation() %>% 
    estimate_profiles(c(3, 4))

get_data(m4)

## -----------------------------------------------------------------------------
get_fit(m4)