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CRAN-RELEASE
DESCRIPTION
NAMESPACE
README.Rmd
README.md
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gscaLCA.Rproj

README.md

gscaLCA

The goal of gscaLCA is to execute Latent Class Analysis by using Generalized Structure Component Analaysis(GSCA).

Installation

You can install the released version of gscaLCA from CRAN with:

install.packages("gscaLCA") 

And the development version from GitHub with:hee6904/gscaLCA

# install.packages("devtools")
devtools::install_github("hee6904/gscaLCA")

Example

This is a basic example.

library(gscaLCA)
#> Loading required package: gridExtra
#> Loading required package: ggplot2
#> Loading required package: stringr
#> Loading required package: progress
#> Loading required package: psych
#> 
#> Attaching package: 'psych'
#> The following objects are masked from 'package:ggplot2':
#> 
#>     %+%, alpha
#> Loading required package: fastDummies
#> Loading required package: fclust
#> Loading required package: MASS
#> Loading required package: devtools
#> Loading required package: usethis
#> Loading required package: doSNOW
#> Loading required package: foreach
#> Loading required package: iterators
#> Loading required package: snow
#> Loading required package: doParallel
#> Loading required package: parallel
#> 
#> Attaching package: 'parallel'
#> The following objects are masked from 'package:snow':
#> 
#>     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#>     clusterExport, clusterMap, clusterSplit, makeCluster,
#>     parApply, parCapply, parLapply, parRapply, parSapply,
#>     splitIndices, stopCluster
## basic example code

Running gscaLCA AddHealth data. The number of cluster is 2 and the number of bootstrap is 20.

R2 = gscaLCA(AddHealth, varnames = names(AddHealth)[2:6], num.cluster = 2)
#> Warning in gscaLCA(AddHealth, varnames = names(AddHealth)[2:6], num.cluster
#> = 2): Listwise delection was used. Uncompleted data is not available in the
#> current version
#> =========================================================
#> LCA by using Fuzzing Clusterwise GSCA
#> =========================================================
#> Fit for 2 latent classes: 
#>  number of used observations: 5066 
#>  number of deleted observation: 48 
#>  number of bootstrap for SE: 20 / 20 
#> 
#> MODEL FIT -----------------------------------------------
#>  FIT      :  0.9997 
#>  AFIT     :  0.9997 
#>  FPI      :  0.6818 
#>  NCE      :  0.7504 
#>  
#> Estimated Latent Class Prevalnces (%) -------------------
#>  44.91% 55.09% 
#>  
#> Conditional item response probability -------------------
#>  $Smoking
#>            Class Category Estimate
#> 1 Latent Class 1      Yes   0.9653
#> 2 Latent Class 1       No   0.0347
#> 3 Latent Class 2      Yes   0.3956
#> 4 Latent Class 2       No   0.6044
#> 
#> $Alcohol
#>            Class Category Estimate
#> 1 Latent Class 1      Yes   0.9684
#> 2 Latent Class 1       No   0.0316
#> 3 Latent Class 2      Yes   0.6686
#> 4 Latent Class 2       No   0.3314
#> 
#> $Drug
#>            Class Category Estimate
#> 1 Latent Class 1      Yes   0.4497
#> 2 Latent Class 1       No   0.5503
#> 3 Latent Class 2      Yes   0.0262
#> 4 Latent Class 2       No   0.9738
#> 
#> $Marijuana
#>            Class Category Estimate
#> 1 Latent Class 1      Yes   0.9754
#> 2 Latent Class 1       No   0.0246
#> 3 Latent Class 2      Yes   0.1985
#> 4 Latent Class 2       No   0.8015
#> 
#> $Cocaine
#>            Class Category Estimate
#> 1 Latent Class 1      Yes   0.3956
#> 2 Latent Class 1       No   0.6044
#> 3 Latent Class 2      Yes   0.0254
#> 4 Latent Class 2       No   0.9746

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