Package 'RegDDM'

Generate simulated binary decision data using DDM

Description

This function generates a simulated dataset under different configurations It can be used to test the performance and functionality of RegDDM. The outcome variable is y, which is influenced by different variables.

Usage

generate_sim_data(
  N = 30,
  n_each = 100,
  n_xvar = 2,
  beta_0 = 0,
  beta_c1 = 0,
  beta_c2 = 0,
  beta_v_0 = 0,
  beta_v_x1 = 0,
  beta_v_x2 = 0,
  sigma_y = 1,
  sigma_v = 0,
  y_family = "gaussian"
)

Arguments

N	Number of subjects.
n_each	Number of trials per subject
n_xvar	Number of trial-level variables influencing drift rate
beta_0	Intercept
beta_c1	Slope of c1
beta_c2	Slope of c2
beta_v_0	Slope of v_0
beta_v_x1	Slope of v_x1
beta_v_x2	Slope of v_x2
sigma_y	Standard deviation of error term of y, Only used when y_family is "gaussian"
sigma_v	Contaminant level for drift rate v.
y_family	Family of distribution of y. Can be either "gaussian", "bernoulli" or "poisson"

Value

A named list with four elements. data1_true and data2_true are true values of DDM parameters of each subject and trial. data1 and data2 removed those hidden variables.

Examples

sim_data = generate_sim_data()
sim_data$data1
sim_data$data2

---

Get the rstan fit of regddmfit objects

Description

Get the stanfit object of the regddmfit object to perform further analysis and diagnosis.

Usage

get_stan_fit(fit)

Arguments

fit	A regddmfit object to summary

Value

A stanfit object.

---

Printed summary of RegDDM fit object

Description

Summarize the posterior distributions of estimated regression coefficients and print necessary information.

Usage

## S3 method for class 'regddmfit'
print(x, digits = 3, ...)

Arguments

x	An regddmfit object to print
digits	digits of the output results. Default value is 3.
...	Unused...

Value

No values are returned.

See Also

summary.regddmfit Table summaries of the regddmfit object.

---

Bayesian hierachical generalized linear regression using Drift-Diffusion Model

Description

regddm makes it easy to fit a single Bayesian hierarchical drift-diffusion model (DDM) that estimates the DDM parameters of each subject and uses the estimated parameters as variables in a generalized linear regression.

Usage

regddm(
  data1,
  data2,
  model = list(),
  family = "gaussian",
  init = "default",
  prior = TRUE,
  stan_filename = "",
  gen_model = TRUE,
  fit_model = TRUE,
  warmup = 500,
  iter = 1000,
  chains = 4,
  cores = 4,
  ...
)

Arguments

data1	Subject-level dataframe with column such as age and gender. It must contain an id column unique for each subject.
data2	Trial-level dataframe. It must contain three columns: id, response and rt. It can also contain additional trial-level variables such as experiment condition.
model	A list containing 0-5 formulas, specifying the dependence structure between variables.
family	Family of distribution of y. Can be gaussian, bernoulli or poisson.
init	Either default or other values supported by stan function of RStan
prior	A losigtic value, specifying whether or not to use default prior for DDM parameters. By default, prior = TRUE.
stan_filename	A string specifying the automatically generated stan file name. By default, an empty string '' is provided. A temporary file will be created and deleted after the model is fit.
gen_model	A logistic value indicating weather or not to generate the model. If not, RegDDM will not generate the code but use the existing stan code from stan_filename instead.
fit_model	A logistic value indicating weather or not to fit the model. If not, RegDDM will only generate the code and return an unfitted regddmfit object.
warmup	Number of warm-up iterations. Default is 500.
iter	Number of iterations, which must be greater than warmup. Default value is 1000.
chains	Number of chains to run for diagnosis. Default value is 4.
cores	Number of cores to run the chains. It is best to make cores = chains. Default value is 4.
...	Other parameters sent to stan function of RStan.

Value

A regddmfit object.

References

To be added

Examples

# Note: each example takes about 20 minutes to run. During this period, you
# may not be able to open/save files or see the progress. To prevent this,
# it is recommended to copy, paste and run the example code in the console.

## Not run: 
# Example analysis over the simulated tutorial dataset.
data(regddm_tutorial)
model = list(v ~ x1, y ~ v_0 + v_x1 + c1)
fit1 = regddm(
  regddm_tutorial$data1,
  regddm_tutorial$data2,
  model,
  stan_filename = ""
)
print(fit1)

# Alternatively, subjects' DDM parameters can be used as the outcome.
model = list(v ~ x1, v_x1 ~ y + c1)
fit2 = regddm(
  regddm_tutorial$data1,
  regddm_tutorial$data2,
  model,
  stan_filename = ""
)
print(fit2)

## End(Not run)

---

Simulated example dataset

Description

This is the same simulated dataset used in the tutorial part of the RegDDM paper.

$v = x_1\times v_{x_1}$

$y \sim N(1 + 1\times v_{x_1} + 1\times c_1, 1)$

Usage

regddm_tutorial

Format

An object of class list of length 2.

---

Class of models fitted by RegDDM

Description

regddmfit is an S3 object sotring the fitted models of RegDDM. It contains information used to fit the model and the resulting stanfit and can be summarized and printed using summary and print.

Usage

regddmfit(data1, data2, model, family, stan_fit)

Arguments

data1	Subject-level data frame.
data2	Trial-level data frame.
model	A list containing 0-5 formulas, specifying the dependence structure between variables.
family	Family of distribution of the outcome.
stan_fit	Fitted stan model.

Details

Use methods(class = "regddmfit") for a list of available methods.

---

Summary of RegDDM fit object

Description

Summarize the posterior distributions of estimated parameters and group them into four categories.

Usage

## S3 method for class 'regddmfit'
summary(object, ...)

Arguments

object	A regddmfit object to summary
...	parameters passed to summary,stanfit-method

Value

The summary method returns a named list of glm_coefficiets, subject_ddm_param, group_param, and missing_value. Each element is a tibble data frame of posterior summary statistics of the regression coefficient, DDM parameter of each subject, group mean and standard deviation of DDM parameters and covariates, plus the estimated missing values.

See Also

print.regddmfit a printed summary of the regddmfit object.

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