poLCA: An R Package for Polytomous Variable Latent Class Analysis

By Drew A. Linzer, Jeffrey B. Lewis. 2011

1,550 citations recorded by the source corpus.

poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/599b971997f296bebd28e47a869f178773e7ffe7

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Citations in the corpus

Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals (2018, positive): “2016) and the lmer and glmer functions in the lme4 package (Bates, Mächler, Bolker, and Walker 2015); and latent class models fit by poLCA in the poLCA package (Linzer and Lewis 2011).”
COVID-19–Associated Multisystem Inflammatory Syndrome in Children — United States, March–July 2020 (2020, background): “69 with 100 iterations to identify the optimal classification scheme (5).”
Ten Frequently Asked Questions About Latent Class Analysis (2018, neutral): “…Mplus (Muthén & Muthén, 1998 – 2017), Latent Gold (Vermunt & Magidson, 2013), MplusAutomation in R (Hallquist & Wiley, 2018), poLCA package in R (Linzer & Lewis, 2011), GLLAMM for Stata (RabeHesketh, Skrondal, & Pickles, 2004), and PROC LCA in SAS (Lanza, Collins, Lemmon, & Schafer, 2007),…”
Identifying Severity Standards on the Cognitive Test Anxiety Scale: Cut Score Determination Using Latent Class and Cluster Analysis (2018, methodology): “We first conducted an exploratory latent class analysis (ELCA) on Sample 1 to identify learners with similar test anxiety profiles using R 3.3.1 (R Core Team, 2016) and the poLCA package (Linzer & Lewis, 2011).”
Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic — United States, June 24–30, 2020 (2020, methodology): “Symptoms of a TSRD precipitated by the COVID–19 pandemic were assessed via the six–item Impact of Event Scale (IES–6) to screen for overlapping symptoms of PTSD, ASD, and ADs.”
A Bayesian information criterion for singular models (2013, methodology): “ing the information criteria. Specically, we optimized the standard dimensionbased BIC as well as sBIC ˚with ˚= 0:5;1;1:5;:::;r. Maximum likelihood estimates were computed using the R package poLCA (Linzer and Lewis, 2011). For settings (i)-(iii), our Table 2 reports the frequencies of how often a p”
Mixture Models: Latent Profile and Latent Class Analysis (2016, methodology): “This is done using the poLCA package in R (Linzer and Lewis, 2011; R Core Team, 2012): library(poLCA) 6 For more information on how exactly to properly perform such an analysis see (Bakk et al., 2013).”
Latent Class Analysis (2021, methodology): “In addition, routines and packages for the estimation of specific types of LC models are available for SAS, R, and Stata (see, for example, Lanza et al., 2007; Linzer and Lewis, 2011; and Skrondal and Rabe-Hesketh, 2004).”
Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach (2013, methodology): “…the fitting of an LCA model with Z as a covariate, which can be implemented with several publicly available and proprietary software packages (e.g., PROC LCA, the R package poLCA, Mplus, Latent GOLD; Lanza et al., 2011; Linzer & Lewis, 2011; Muthén & Muthén, 1998–2007; Vermunt & Magidson, 2005).”