Analyze Multinomial Processing Tree Models
Compute FIA for MPTs
Check construction of MPT models.
Fit cognitive models for categorical data using model files
Function to fit MPT models (old)
Function to fit MPT models
Fit cognitive models for categorical data using an objective function
Generate or bootstrap data and get predictions from a model specified ...
Convenient function to get FIA for MPT
Creates an EQN model file oir MDT data file
Functions to transform MPT models.
tools:::Rd_package_title("MPTinR")
Plot observed versus predicted values for categorical data.
Provides MATLAB command to get FIA
Recognition memory ROCs used by Klauer & Kellen (2015)
Model Selection with MPTinR
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.