plot.net_markov_order: Plot Method for net_markov_order

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Plot Method for net_markov_order

Description

Two-panel professional visualization:

• Panel A: log-likelihood, AIC, BIC across tested orders with the selected order highlighted (both the permutation-selected order and the BIC-minimizing order are marked).

• Panel B: permutation null density per order with the observed G^2 as a vertical marker; colored by rejection at alpha.

Uses the Okabe-Ito colorblind-safe palette.

Usage

## S3 method for class 'net_markov_order'
plot(x, panel = c("both", "ic", "permutation"), combined = TRUE, ...)

Arguments

x	A net_markov_order object.
panel	Which panel(s) to render: "both", "ic", or "permutation". Default "both".
combined	When panel = "both" and combined = TRUE (default), the two panels are drawn side-by-side. If gridExtra is installed they are arranged into a single drawable/saveable gtable (returned); otherwise base grid viewports draw both panels and a named list of the two ggplots is returned invisibly. When FALSE, returns that named list (ic, permutation) without drawing. Ignored when panel != "both".
...	Ignored.

Value

A ggplot (single panel); for panel = "both", either a gridExtra gtable (when gridExtra is installed) or a named list of two ggplots (ic, permutation) drawn side-by-side and returned invisibly.

Examples

# First-order Markov data: test should select order 1
set.seed(1)
states <- letters[1:4]
tm <- matrix(runif(16), 4, 4, dimnames = list(states, states))
tm <- tm / rowSums(tm)
seqs <- lapply(1:30, function(.) {
  s <- character(50); s[1] <- sample(states, 1)
  for (i in 2:50) s[i] <- sample(states, 1, prob = tm[s[i - 1], ])
  s
})
res <- markov_order_test(seqs, max_order = 3, n_perm = 300, seed = 1)
res$optimal_order
summary(res)
plot(res)

Nestimate documentation built on July 11, 2026, 1:09 a.m.