New Classes

The LAN extension (HDDM >= 0.9.0), provides three new classes which are LAN-enabled versions of the respective classes in base HDDM. These new classes are,

  • The HDDMnn() class

  • The HDDMnnStimCoding() class

  • The HDDMnnRegressor() class

The usage mirrors what you are used to from standard HDDM equivalents.

What changes is that you now use the model argument to specify one of the models you find listed in the hddm.model_config.model_config dictionary (you can also provide a custom model, for which you should look into the respective section in this documentation).

Moreover, you have to be a little more careful when specifying the include argument, since the ability to use new models comes with new parameters. To help get started here, the hddm.model_config.model_config dictionary provides you a hddm_include key for every model-specific sub-dictionary. This let’s you fit all parameters of a given model. To keep some parameters fixed, remove them respectively from the resulting list.

Short example

import hddm
model = 'angle'
cavanagh_data = hddm.load_csv(hddm.__path__[0] + '/examples/cavanagh_theta_nn.csv')
model_ = hddm.HDDMnn(cavanagh_data,
                     model = model,
                     include = hddm.model_config.model_config[model]['hddm_include'],
                     is_group_model = False)
Using default priors: Uninformative
model_.sample(1000, burn = 200)
[-----------------100%-----------------] 1000 of 1000 complete in 260.3 sec
<pymc.MCMC.MCMC at 0x13e449650>
model_.get_traces()
v a z_trans t theta
0 0.370402 1.325747 0.023242 0.284196 0.253870
1 0.338917 1.328545 0.062895 0.283047 0.248485
2 0.386179 1.321476 0.054727 0.285712 0.250671
3 0.387484 1.323711 -0.019109 0.274198 0.253445
4 0.370557 1.323342 0.015675 0.277691 0.255681
... ... ... ... ... ...
795 0.325748 1.331846 0.113685 0.270311 0.252461
796 0.337564 1.315446 0.111898 0.286141 0.252236
797 0.387142 1.309284 0.036839 0.286663 0.238878
798 0.388073 1.313791 -0.013604 0.271768 0.235831
799 0.397477 1.314008 -0.007186 0.276948 0.242729

800 rows × 5 columns

model_.gen_stats()
mean std 2.5q 25q 50q 75q 97.5q mc err
v 0.369154 0.0207375 0.329893 0.355813 0.369495 0.382592 0.409568 0.00111918
a 1.31224 0.0212032 1.26826 1.29879 1.31332 1.32755 1.3514 0.00180178
z 0.504951 0.00604908 0.493251 0.500775 0.504934 0.509041 0.517023 0.000311986
t 0.283719 0.00943542 0.265774 0.277639 0.283707 0.290191 0.302331 0.00070058
theta 0.242432 0.0127552 0.216824 0.234284 0.242875 0.251645 0.265587 0.00103379