In giorgiopaulon/LDDMM: Longitudinal Drift-Diffusion Mixed Models (LDDMM)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The following is a minimal example of a simple model fit.

# Load libraries
library(RColorBrewer)
library(ggplot2)
library(dplyr)
library(reshape2)
library(latex2exp)
library(lddmm)

theme_set(theme_bw(base_size = 14))
cols = brewer.pal(9, "Set1")

# Load the data
data('data')

# Descriptive plots
plot_accuracy(data)
plot_RT(data)

# Run the model
hypers = NULL
hypers$s_sigma_mu = hypers$s_sigma_b = 0.1

# Change the number of iterations when running the model
# Here the number is small so that the code can run in less than 1 minute
Niter = 25
burnin = 15
thin = 1
samp_size = (Niter - burnin) / thin

set.seed(123)
fit = LDDMM(data = data, 
             hypers = hypers, 
             Niter = Niter, 
             burnin = burnin, 
             thin = thin)

# Plot the results
plot_post_pars(data, fit, par = 'drift')
plot_post_pars(data, fit, par = 'boundary')

# Compute the WAIC to compare models
compute_WAIC(fit)

To extract relevant posterior draws or posterior summaries instead of simply plotting them, one can use the functions extract_post_mean or extract_post_draws. The following auxiliary functions are available by selecting the corresponding argument in the LDDMM() function:

• boundaries = "constant": constant boundary parameters over time, $b_{d,s}^{(i)}(t) = b_{d,s} + u_{d,s}^{(i)}$ using the article notation
• boundaries = "fixed": fixed boundaries across input predictors, $b_{d,s}^{(i)}(t) = b_{d}(t) + u^{(i)}_{d}(t)$ using the article notation
• boundaries = "fixed-constant": fixed and constant boundaries, $b_{d,s}^{(i)}(t) = b_{d} + u_{d}^{(i)}$ using the article notation

giorgiopaulon/LDDMM documentation built on Jan. 29, 2024, 7:33 p.m.