A toolbox for constructing potential landscapes for Ising networks. The parameters of the networks can be directly supplied by users or estimated by the
IsingFit package by van Borkulo and Epskamp (2016) https://CRAN.R-project.org/package=IsingFit from empirical data. The Ising model’s Boltzmann distribution is preserved for the potential landscape function. The landscape functions can be used for quantifying and visualizing the stability of network states, as well as visualizing the simulation process.
You can install the development version of Isinglandr from GitHub with:
# install.packages("devtools") devtools::install_github("Sciurus365/Isinglandr")
library(Isinglandr) #> Registered S3 method overwritten by 'Isinglandr': #> method from #> print.landscape simlandr # A toy network and its landscape Nvar <- 10 m <- rep(0, Nvar) w <- matrix(0.1, Nvar, Nvar) diag(w) <- 0 result1 <- make_2d_Isingland(m, w) plot(result1)
## Multiple networks together result4 <- make_Ising_grid( all_thresholds(seq(-0.1, 0.1, 0.1), .f = `+`), whole_weiadj(seq(0.5, 1.5, 0.5)), m, w ) %>% make_2d_Isingland_matrix() plot(result4) #> Scale for x is already present. #> Adding another scale for x, which will replace the existing scale.
A shiny app is included in this package to show the landscape for the Ising network of major depressive disorder. The network parameters can be manipulated to see how they influence the landscape and the simulation. Run
shiny_Isingland_MDD() to start it.