The following is a minimal example of a simple model fit.
# Load libraries
library(RColorBrewer)
library(ggplot2)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
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')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
or fix the boundaries to the same level across predictors can be called
with the options boundaries = "constant" and
boundaries = "fixed", respectively.