-
- Description:
Purpose
HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.
Features
- Uses hierarchical Bayesian estimation (via PyMC) of DDM parameters to allow simultaneous estimation of subject and group parameters, where individual subjects are assumed to be drawn from a group distribution. HDDM should thus produce better estimates when less RT values are measured compared to other methods using maximum likelihood for individual subjects (i.e. DMAT or fast-dm).
- Heavily optimized likelihood functions for speed (Navarro & Fuss, 2009).
- Flexible creation of complex models tailored to specific hypotheses (e.g. estimation of separate drift-rates for different task conditions; or predicted changes in model parameters as a function of other indicators like brain activity).
- Estimate trial-by-trial correlations between a brain measure (e.g. fMRI BOLD) and a diffusion model parameter using the HDDMRegression model.
- Built-in Bayesian hypothesis testing and several convergence and goodness-of-fit diagnostics.
- Changes to previous version:
-
New and improved HDDM model with the following changes:
-
Priors: by default model will use informative priors
(see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm)
If you want uninformative priors, set
informative=False
. - Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
- Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
-
The old model has been renamed to
HDDMTransformed
. - HDDMRegression and HDDMStimCoding are also using this model.
-
Priors: by default model will use informative priors
(see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm)
If you want uninformative priors, set
- HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
- Improved online documentation at http://ski.clps.brown.edu/hddm_docs
- A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
-
Ratcliff's quantile optimization method for single subjects and
groups using the
.optimize()
method - Maximum likelihood optimization.
- Many bugfixes and better test coverage.
- hddm_fit.py command line utility is depracated.
-
New and improved HDDM model with the following changes:
- BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Csv
- Tags: Bayesian Estimation, Drift Model, Hierarchical
- Archive: download here
Other available revisons
-
Version Changelog Date 0.5 -
New and improved HDDM model with the following changes:
-
Priors: by default model will use informative priors
(see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm)
If you want uninformative priors, set
informative=False
. - Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
- Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
-
The old model has been renamed to
HDDMTransformed
. - HDDMRegression and HDDMStimCoding are also using this model.
-
Priors: by default model will use informative priors
(see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm)
If you want uninformative priors, set
- HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
- Improved online documentation at http://ski.clps.brown.edu/hddm_docs
- A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
-
Ratcliff's quantile optimization method for single subjects and
groups using the
.optimize()
method - Maximum likelihood optimization.
- Many bugfixes and better test coverage.
- hddm_fit.py command line utility is depracated.
April 24, 2013, 02:53:07 0.4 - Handling of outliers via mixture model. http://ski.clps.brown.edu/hddm_docs/howto.html#deal-with-outliers
- New model HDDMRegression to allow estimation of trial-by-trial regressions with a covariate. http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model
- New model HDDMStimulusCoding. http://ski.clps.brown.edu/hddm_docs/howto.html#code-subject-responses
- New model HLBA -- a hierarchical Linear Ballistic Accumulator model (hddm.HLBA).
- Posterior predictive quantile plots (see model.plot_posterior_quantiles()).
November 26, 2012, 19:49:49 0.2 Initial Announcement on mloss.org.
March 15, 2012, 01:45:41 -
New and improved HDDM model with the following changes:
Comments
No one has posted any comments yet. Perhaps you'd like to be the first?
Leave a comment
You must be logged in to post comments.