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Plot Method for net_markov_order

Source: R/markov_order.R
plot.net_markov_order.Rd

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

# \donttest{
# 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
#> [1] 1
summary(res)
#>   order    loglik      AIC      BIC  df        g2 p_permutation p_asymptotic
#> 1     0 -2011.025 4028.050 4043.990  NA        NA            NA           NA
#> 2     1 -1805.112 3640.224 3719.922   9 411.78019   0.003322259 4.193315e-83
#> 3     2 -1789.863 3703.726 4033.146  36  30.35171   0.803986711 7.337874e-01
#> 4     3 -1701.468 3808.936 4887.520 136 173.56575   0.099667774 1.633824e-02
#>   significant
#> 1          NA
#> 2        TRUE
#> 3       FALSE
#> 4       FALSE
plot(res)

# }