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$s_sigma_b <- 0.1 # Change the number of iterations when running the model # Here the number is small so that the code can run in less than 1 minute Niter <- 25 burnin <- 15 thin <- 1 samp_size <- (Niter - burnin) / thin set.seed(123) fit <- LDDMM(data = data, hypers = hypers, Niter = Niter, burnin = burnin, thin = thin) # Plot the results plot_post_pars(data, fit, par = 'drift') plot_post_pars(data, fit, par = 'boundary') # Compute the WAIC to compare models compute_WAIC(fit)
To extract relevant posterior draws or posterior summaries instead of simply plotting them, one can use the functions extract_post_mean
or extract_post_draws
.
Auxiliary functions that assume constant boundary parameters over time ($b_{d,s}^{(i)}(t) = b_{d,s} + u_{d,s}^{(i)}$ using the article notation) or fix the boundaries to the same level across input predictors ($b_{d,s}^{(i)}(t) = b_{d}(t) + u^{(i)}_{d}(t)$ using the article notation) can be called with the options boundaries = "constant"
and boundaries = "fixed"
, respectively.
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