gpt: Generalized Processing Tree Models
In danheck/gpt: Generalized Processing Tree Models
gpt R Documentation
Generalized Processing Tree Models
Description
Fits GPT models for multivariate data (one discrete and one or more continuous responses per trial).
Assumes that distribution of continuous variable(s) is a mixture distribution
with the MPT core structure defining the mixture probabilities.
Details
The GPT structure is implemented by an S4 class gpt, which contains the MPT structure (the S4 class mpt), a vector mapping the MPT branches to the underlying continuous distributions (mapvec), a list of univariate or multivariate basis distributions (each an S4 class contin with information about parameter spaces etc.), the parameter labels for theta and eta, and a vector with constant values for the parameters.
It is advisable to first check that a GPT model file is valid using read_gpt.
Next, one can either first generate some simulated data using gpt_gen
or fit data using gpt_fit. The fitting algorithm first uses an EM
algorithm before maximizing the full likelihood by gradient descent.
Note that restrictions on the parameter space are automatically taken into account
(e.g., variances must be positive).
Author(s)
Daniel W. Heck, dheck@uni-marburg.de
References
Heck, D. W., Erdfelder, E., & Kieslich, P. J. (2018).
Generalized processing tree models: Jointly modeling discrete and continuous variables.
Psychometrika, 83, 893–918. https://doi.org/10.1007/s11336-018-9622-0
danheck/gpt documentation built on March 29, 2025, 1:17 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| gpt | R Documentation |
Generalized Processing Tree Models
Description
Fits GPT models for multivariate data (one discrete and one or more continuous responses per trial). Assumes that distribution of continuous variable(s) is a mixture distribution with the MPT core structure defining the mixture probabilities.
Details
The GPT structure is implemented by an S4 class gpt, which contains the MPT structure (the S4 class mpt), a vector mapping the MPT branches to the underlying continuous distributions (mapvec), a list of univariate or multivariate basis distributions (each an S4 class contin with information about parameter spaces etc.), the parameter labels for theta and eta, and a vector with constant values for the parameters.
It is advisable to first check that a GPT model file is valid using read_gpt.
Next, one can either first generate some simulated data using gpt_gen
or fit data using gpt_fit. The fitting algorithm first uses an EM
algorithm before maximizing the full likelihood by gradient descent.
Note that restrictions on the parameter space are automatically taken into account
(e.g., variances must be positive).
Author(s)
Daniel W. Heck, dheck@uni-marburg.de
References
Heck, D. W., Erdfelder, E., & Kieslich, P. J. (2018). Generalized processing tree models: Jointly modeling discrete and continuous variables. Psychometrika, 83, 893–918. https://doi.org/10.1007/s11336-018-9622-0
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.