In epf02013/r2sem: Easier Definitions of S.E.M. in R

Prerequisites

This code depends on the tidyverse library for the str_glue function

Example usage of the model definition function

library(here)

# import the file so we can use define_longitudinal_measurement_invariance_model
source(here("R/latent_growth_curve_model.r"))
df = data.frame(
  cm01ec_lat= c("ec01_cmpar1", "ec01_cmpar2", "ec01_cmpar3", "ec01_cmpar4"),
  cm03ec_lat= c("ec03_cmpar1", "ec03_cmpar2", "ec03_cmpar3", "ec03_cmpar4"),
  cm05ec_lat= c("ec05_cmpar1", "ec05_cmpar2", "ec05_cmpar3", "ec05_cmpar4"),
  cm07ec_lat= c("ec07_cmpar1", "ec07_cmpar2", "ec07_cmpar3", "ec07_cmpar4")
)

# You can generate CONFIGURAL, WEAK, STRONG, PARTIAL_STRONG, OR STRICT models
variable_of_interest_name = "ec"


ec_cm_no_growth <- define_latent_growth_curve_model(
  df,
  variable_of_interest_name,
  "NO_GROWTH",
  "PARTIAL_STRONG",
  c(2,3)
)

ec_cm_linear <- define_latent_growth_curve_model(
    df,
    variable_of_interest_name,
    "LINEAR",
    "PARTIAL_STRONG",
    c(2,3),
    c(0, 2, 4, 6)
)

ec_cm_latent_basis <- define_latent_growth_curve_model(
  df,
  variable_of_interest_name,
  "LATENT_BASIS",
  "STRONG",
  slope_weights = c(0, NA, NA, 6)
)

# You could then plug it into your cool cfa function
#ec_cm_latent_basis <- sem(ec_cm_latent_basis, data=dat, mimic = "mplus")
#summary(ec_cm_latent_basis, fit.measures=TRUE)

This is what the output of the model looks like configural

writeLines(ec_cm_latent_basis)

Partial Strong

writeLines(ec_cm_no_growth)

writeLines(ec_cm_linear)

epf02013/r2sem documentation built on Dec. 20, 2021, 5:21 a.m.