Build Regression models over Drift Diffusion Model parameters using MCMC!

You can install latest version of RegDDM from CRAN:

install.packages("RegDDM")

For RStudio users, you may need the following:

install.packages("rstudioapi")

First, load the package and the example dataset.

library(RegDDM)
data(regddm_data)

data1 is the subject-level dataset:

head(regddm_data$data1)
#>   id     iq age gender  race education
#> 1  1 112.80  22      F White        14
#> 2  2 114.32  22      F White        16
#> 3  3 116.96  22      F Black        13
#> 4  4 111.68  31      F Black        16
#> 5  5 121.36  21      M Asian        16
#> 6  6 124.24  29      F White        18

data2 is the subject-level dataset:

head(regddm_data$data2)
#>   id memload response        rt
#> 1  1       1        1 1.1772806
#> 2  1       1        1 2.2207544
#> 3  1       6        1 4.4166550
#> 4  1       6        1 0.8540982
#> 5  1       3        1 1.3794191
#> 6  1       3        0 0.8278006

Specify the model using a list. In this example, the drift rate v is influenced by memload, which is the memory load of the trial. The subject’s iq is predicted by baseline drift rate v_0 (drift rate when memload is 0), the influence of memload on drift rate v_memload and covariates age and education:

model = list(
  v ~ memload,
  iq ~ v_memload + v_0 + age + education
)

Use the main function of RegDDM to automatically generate the RStan model and summary the results. This could take ~20 minutes to run. The rows starting with ‘beta_’ are the posterior distributions of regression parameters:

fit = regddm(
  regddm_data$data1,
  regddm_data$data2,
  model
)

print(fit)
#> RegDDM Model Summary
#> Number of subjects: 49
#> Number of trials: 6032
#> Model:
#>   v ~ memload
#>   iq ~ v_memload + v_0 + age + education
#> Family: gaussian
#> Sampling: 4 chains, 500 warmups and 1000 iterations were used. Longest elapsed time is 3218 s.
#> 
#> Regression coefficients:
#>         variable     mean     sd     2.5%   97.5% n_eff  Rhat
#> 1         beta_0 112.8863 12.308   87.834 135.485  1621 0.999
#> 2 beta_v_memload -54.3336 27.418 -110.982  -3.216   958 1.001
#> 3       beta_v_0  -3.6667  2.003   -7.883   0.156  2602 0.999
#> 4       beta_age   0.1293  0.329   -0.498   0.809  2541 0.998
#> 5 beta_education  -0.0422  0.601   -1.216   1.083  2652 0.999
#> 6          sigma   6.8007  0.826    5.350   8.599  2004 0.999
#> Maximum R-hat: 1.005

In this example, iq is negatively correlated with v_memload. The higher the influence of memload on drift rate, the lower the iq of the subject.

If you want to fit the model on your own data, you need to specify data1, data2 and model.

data1 is subject-level data table. It should contain the following: * id: unique indexing column for each subject. * other subject-level variables that we want to include in the regression. Missing values are supported

data2 is trial-level data table. It should contain the following: * id: the subject of each trial using the same index in data1. * rt: response time of the trial in seconds. * `response``: response the trial. must be either 0 or 1. * trial-level variables. These are the variables that differ by trial, such as difficulty of the task or different numbers on the screen. We assume that subjects’ behavior changes according to these variables. These variables cannot contain missing values.

model is the proposed dependency between these parameters. Default is an empty list. It must be a list of 0 - 5 formulas. The outcome of these formulas can be either: * one of the four DDM parameters a, t, z, v, modeling the relationship between DDM parameters and trial-level variables. * one formula for GLM regression, modeling the relationship between estimated DDM parameters and other subject-level variables.

family is the family of distribution of GLM. It can be either "gaussian", "bernoulli" or "poisson". Default is "gaussian".

init is how we initialize the MCMC algorithm. The "default" initialization should work in most conditions

prior determines whether to use the default prior for DDM parameters or not. Default is TRUE

stan_filename is the file location for the automatically generated stan model. If an empty string ’’ is provided, a temporary file will be created and deleted after the model is fit. Default is "stan_model.stan"

gen_model determines whether to generate the model or not. Default is TRUE.

fit_model determines whether to fit the model or not. Default is TRUE.

...: additional parameters used by rstan, including warmup,iter,chains,cores etc.

to be added