README.md

In samwycherley/thresholdr: thresholdr: Stability Tools for Threshold Vector Autoregressive Models in R

thresholdr

thresholdr is an R package providing a convenient wrapper for ThresholdStability.jl, a Julia package providing tools to determine the stability of threshold vector autoregressive models.

Installation

The package can be installed directly from GitHub using

devtools::install_github("samwycherley/thresholdr")

This package relies on JuliaCall, an R interface to Julia. You might initially need to take some additional steps to set this interface up - instructions can be found here.

Usage

To initialize the package, use

library(thresholdr)
th <- threshold_setup()

This will install Julia and ThresholdStability.jl if they are not installed already.

The first time the package is initialized might take a while as ThresholdStability.jl needs to precompile. If it appears stuck indefinitely at the message "Loading setup script for JuliaCall...", however, this may be a sign that JuliaCall has not been set up properly, in which case you may need to take the additional steps mentioned above.

Functions

Much like other R wrappers for Julia packages, such as diffeqr, thresholdr mostly provides a direct wrapper over the Julia functions, and the namespace is set-up so that the names of the R wrapper functions coincide with that of the functions in ThresholdStability.jl.

Some important exceptions: - functions are prefaced with th$. - those functions that end in ! in Julia, such as add_transition! (standard syntax in Julia for functions that modify an object in-place.) In thresholdr, these are renamed without the !, so add_transition! becomes add_transition. - those functions featuring Greek letters in ThresholdStability.jl: sdp_γ and sos_γ. ThresholdStability.jl includes aliases sdp_gamma and sos_gamma for these functions, which can be called from thresholdr. - CKSVAR_to_TAR is split into two functions: CKSVAR_to_TAR which returns the set of matrices Σ and CKSVAR_to_TAR_st_space which returns the set of state space constraint matrices X. - discreteswitchedsystem now requires all three arguments (set of matrices Σ, automaton G, state space constraint set X.)

Functions can return either an R object or a Julia object, and this can be specified by including need_return="R" or need_return="Julia" as an argument in thresholdr functions. Some Julia objects, such as automata or hybrid systems, cannot be converted to R objects automatically.

To convert a CKSVAR model and calculate the JSR, CJSR or SCJSR, we might run something like the following:

C =...  # some appropriately sized matrix(c(...), nc, nr)
Cstar = ...  # "
beta_tilde = ... # some c(...)
nlags = 2  # some integer

Sigma <- th$CKSVAR_to_TAR(C, Cstar, beta_tilde, nlags)  # returns an R object
G <- th$automaton_constructor(Sigma)
X <- th$CKSVAR_to_TAR(C, Cstar, beta_tilde, nlags)  # returns a Julia object as default
s <- th$discreteswitchedsystem(Sigma, G, X)

# Now we are in a position to find SCJSR etc.
th$jsr(s)  # upper bound on JSR
th$cjsr(s)  # upper bound on CJSR
th$sosbound_gamma(s, 2)  # upper bound on SCJSR through SOS method, with d=2

We can also construct the vectors needed for Sigma and X in R through lists.

Sigma1 <- matrix(c(...), nr, nc)  # some square matrix
Sigma2 <- ...  # another
Sigma3 <- ...
Sigma4 <- ...
Sigma <- list(Sigma1, Sigma2, Sigma3, Sigma4)  # this is compatible with dicreteswitchedsystem or automaton_constructor
G <- th$automaton_constructor(Sigma)  # will work 
# (NOTE only use `automaton_constructor` if the `Sigma$i`s are in the appropriate order (see ThresholdStability docs)!)
# Otherwise, construct the automaton manually (below)

X1 <- list(matrix(c(...), nr, nc), matrix(c(...), nr, nc))  # [E1, D1], the first pair of state space constraint matrices
X2 <- ...  # another
X3 <- ...
X4 <- ...
X <- list(X1, X2, X3, X4)
s <- th$discreteswitchedsystem(Sigma, G, X)

Automata conversion

While HybridSystems.jl automata from Julia are not automatically translated to an R equivalent, thresholdr provides functions that can translate automata from HybridSystems into a representation in R using the network package, and R equivalents of the functions used to build automata in Julia.

In the R representation, transitions are tracked by an adjacency matrix and labels are tracked similarly via a network attribute.

G <- th$GraphAutomaton(4)  # create a Julia GraphAutomaton `G` with 4 nodes
net <- th$graphautomaton(4)  # create an R representation of an automaton, `net`, with 4 nodes. 

G <- th$add_transition(G, 1, 2, 1)  # add transition 1 -> 2 with label 1 to the GraphAutomaton
net <- th$addr_transition(G, 1, 2, 1)  # same but for the R automaton
th$get.labels(net)  # retrieve labels for the R automaton

net <- th$automaton_jl_to_r(G)  # convert Julia automaton G to R automaton `net`
G <- th$automaton_r_to_jl(net)  # convert R automaton to Julia

samwycherley/thresholdr documentation built on July 2, 2023, 6:05 a.m.