The goal of tsnet
is to include helpful functions for dynamic network
modelling in psychology and surrounding fields. The package contains
functionality to estimate Bayesian GVAR models in Stan, as well as a
test for network comparison. Additionally, the package includes
functions to plot posterior estimates and centrality indices. More
information is provided in the associated preprint Siepe et
al. (2024).
You can install the released version of tsnet
from
CRAN with:
install.packages("tsnet")
You can install the development version of tsnet
from
GitHub with:
# install.packages("devtools")
devtools::install_github("bsiepe/tsnet")
The installation may take some time as the models are compiled upon installation.
The package includes the stan_gvar
function that can be used to
estimate a GVAR model with Stan. We use rstan
as a backend. More
details are included in the package documentation and the associated
preprint.
library(tsnet)
# Load example data
data(ts_data)
# use data of first individual
data <- subset(ts_data, id == "ID1")
# Estimate network
fit_stan <- stan_gvar(data[,-7],
cov_prior = "IW",
iter_warmup = 500,
iter_sampling = 500,
n_chains = 4)
# print summary
print(fit_stan)
This is an example of how to use the package to compare two network
models. We use here BGGM to estimate the networks, but the stan_gvar
function can be used as well.
library(tsnet)
# Load simulated time series data of two individuals
data(ts_data)
data_1 <- subset(ts_data, id == "ID1")
data_2 <- subset(ts_data, id == "ID2")
# Estimate networks
# (should perform detrending etc. in a real use case)
net_1 <- stan_gvar(data_1[,-7],
iter_sampling = 1000,
n_chains = 4)
net_2 <- stan_gvar(data_2[,-7],
iter_sampling = 1000,
n_chains = 4)
# Plot individual temporal network estimates
post_plot_1 <- posterior_plot(net_1)
You can then compare these networks, summarize the results and plot the test results. In this case, the test is significant for both the temporal and the contemporaneous network.
# Compare networks
compare_13 <- compare_gvar(net_1,
net_2,
return_all = TRUE,
n_draws = 1000)
# Print summary of results
print(compare_13)
# Plot test results
test_plot_13 <- plot(compare_13,
name_a = "Model A",
name_b = "Model B")
If you use the package, please cite the preprint that introduces the package and the test:
Siepe, B.S., Kloft, M. & Heck, D.W. (2024). Bayesian Estimation and Comparison of Idiographic Network Models. (https://osf.io/preprints/psyarxiv/uwfjc/)