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minimal_example

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')

# 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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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