Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang [(2022)] (https://link.springer.com/article/10.1007/s11336-021-09832-8) and Liu & Wang (2023). Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support ‘OpenMP’. Both continuous and unordered categorical response variables are supported.
You can install the development version of spfa from GitHub, with:
# install.packages("devtools")
devtools::install_github("wwang1370/spfa")Various examples and help files have been compiled using the knitr package to generate HTML output, and are available on the package help file. User contributions are welcome!
library(spfa)
## basic example code includes fitting an spfa model for response time
# RT <- spfa::simdata[,1:8]
# spfa_example <- spfa(data = RT,
# dimension = rep(0, ncol(RT)),
# discrete = rep(F, ncol(RT)))Liu, Y., & Wang, W. (2022). Semiparametric factor analysis for item-level response time data. Psychometrika, 87 (2), 666-692.
Liu, Y., & Wang, W. (2023). What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data. Retrieved from http://arxiv.org/abs/2303.10079