Tutorial on tidymodels for Machine Learning

Set Up Data Set: Diamonds Separating Testing and Training Data: rsample Data Pre-Processing and Feature Engineering: recipes Defining and Fitting Models: parsnip Summarizing Fitted Models: broom Evaluating Model Performance: yardstick Tuning Model Parameters: tune and dials Preparing a parsnip Model for Tuning Preparing Data for Tuning: recipes Combine Everything: workflows Selecting the Best Model to Make the Final Predictions Summary Further Resources Session Info Updates caret is a well known R package for machine learning, which includes almost everything from data pre-processing to cross-validation. [Read More]

Tutorial: Rasch and 2PL Model in R

Setup Data Rasch Model Plots Model Identification Note on Item Parameters in eRm Package MML Estimation 2PL Model Model Fit Relative Fit of Rasch and 2PL Model Absolute Fit of the Rasch Model DIF Person Parameters ML MAP and EAP Item and Test Information References Recently, I wrote a summary of some illustrative IRT analyses for my students. Quickly, I realized that this might be of interest to others as well, and I am posting here a tutorial for the Rasch model and the 2PL model in R. [Read More]