https://github.com/tengfei-emory/sltca

SLTCA: Scalable and Robust Latent Trajectory Class Analysis Using Artificial Likelihood
https://github.com/tengfei-emory/sltca

latent-class-analysis latent-trajectories

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SLTCA: Scalable and Robust Latent Trajectory Class Analysis Using Artificial Likelihood

• Host: GitHub
• URL: https://github.com/tengfei-emory/sltca
• Owner: tengfei-emory
• License: gpl-3.0
• Created: 2020-05-04T21:19:24.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-10-01T22:12:39.000Z (over 4 years ago)
• Last Synced: 2024-11-05T03:42:58.521Z (2 months ago)
• Topics: latent-class-analysis, latent-trajectories
• Language: R
• Size: 92.8 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SLTCA

[![CRAN Status Badge](http://www.r-pkg.org/badges/version/SLTCA)](http://cran.r-project.org/web/packages/SLTCA)

[![Downloads badge](https://cranlogs.r-pkg.org/badges/SLTCA)](https://cranlogs.r-pkg.org/badges/SLTCA)

[![Total downloads](https://cranlogs.r-pkg.org/badges/grand-total/SLTCA)](https://cranlogs.r-pkg.org/badges/grand-total/SLTCA)

[![](https://img.shields.io/badge/doi-10.1111/biom.13366-blue.svg)](https://doi.org/10.1111/biom.13366)

SLTCA: Scalable and Robust Latent Trajectory Class Analysis Using Artificial Likelihood

# News

The package is on [CRAN](https://cran.r-project.org/package=SLTCA) now. This repository will mainly serve as a platform for bug reporting (at [Issues](https://github.com/tengfei-emory/SLTCA/issues)) while we will post the lastest version on CRAN. To access the latest version on GitHub, please go to branch [CRAN](https://github.com/tengfei-emory/SLTCA/tree/CRAN). Thanks for considering using our software!

# Installation Guide

```{r}

install.packages("SLTCA")

library(SLTCA)

```

Currently `SLTCA` supports R version >= 3.5.0.

# Example: analyze a simulated dataset

## Data simulation

By default, the function `simulation(n)` generates a dataset with n observations under the scenario 1 described by Hart, Fei and Hanfelt (2020). 

```{r}

# generate a dataset with 500 individuals

dat <- simulation(500)

```

Specifically, it returns a data frame of 2 latent classes with 6 longitudinal features `y.1` to `y.6`, including count (`y.1` and `y.2`), binary (`y.3` and `y.4`) and continuous (`y.5` and `y.6`) features. The data frame also consists of individual identifiers (`id`), corresponding time of longitudinal features (`time`) and the number of visit (`num_obs`). In addition, variable `baselinecov` is a binary baseline risk factor of latent classes. Variable `latent` is the true latent class labels.

## Model fitting

The analysis for the dataset `dat` can be conducted by running `SLTCA` function:

```{r}

res <- SLTCA(k=1,dat=dat,num_class=2,id="id",time="time",num_obs="num_obs",features=paste("y.",1:6,sep=''),

             Y_dist=c('poi','poi','bin','bin','normal','normal'),

             covx="baselinecov",ipw=1,stop="tau",tol=0.005,max=50,

             varest=T,balanced=T,MSC='EQIC',verbose=T)

```

Please refer to the function documentation for more details.

# References

Hart, Fei and Hanfelt (2020), [Scalable and Robust Latent Trajectory Class Analysis Using Artificial Likelihood](https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13366). Biometrics, Accepted Author Manuscript. doi:10.1111/biom.13366