zhenkewu / slamr

Fast Algorithms for Fitting Structured Latent Attribute Models (SLAM) in R

slamR: Structured Latent Attribute Models in R

An R package for Structured Latent Attribute Models (SLAM)

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Maintainer: Zhenke Wu, [email protected]

References: If you are using slamR for clustering multivariate binary observations with SLAM, please cite the following papers:

	Citation	Paper Link
SLAM - multilevel latent attributes and multilevel tree-structured responses, unknown Q	Gu Y, Li M, Xu G, Wu Z (2020+). Interpretable Clustering of Hierarchical Dependent Binary Data: A Doubly-Multi-Resolution Approach. In progress.	link
SLAM - known Q	Gu Y and Xu G (2019). Learning attribute patterns in high-dimensional structured latent attribute models. Journal of Machine Learning Research 20.115: 1-58	Link
SLAM - hierarchical attributes, unknown Q	Gu Y and Xu G (2019). Identification and Estimation of Hierarchical Latent Attribute Models. arXiv:1906.07869	link

Table of content

• 1. Installation
• 2. Overview
• 2. Example

Installation

install.packages("devtools",repos="https://cloud.r-project.org")
devtools::install_github("zhenkewu/slamR")

Overview

Structured latent attribute models (SLAMs) are a special family of discrete latent variable models widely used in social and biological sciences. This paper considers the problem of learning significant attribute patterns from a SLAM with potentially high-dimensional configurations of the latent attributes. We address the theoretical identifiability issue, propose a penalized likelihood method for the selection of the attribute patterns, and further establish the selection consistency in such an overfitted SLAM with a diverging number of latent patterns. The good performance of the proposed methodology is illustrated by simulation studies and two real datasets in educational assessments.

slamR works for

• one-level binary responses
  • known Q, unknown attribute set
  • unknown Q, unknown attribute set
• two-level binary responses
  • unknown or known Q, unknown attribute sets at both levels

Examples (two-level binary responses)

• 1. Example code to use slamR is at inst/example/compare_flat.R
• 2. restricted latent class analysis with pre-specified # of factors but unknown # of clusters

• Example of the Tree Structure of Observed ICD-9 Codes

• Incorporate tree structure

• doubly-multi-resolution approach for dealing with two-level multivariate binary data