Plot optimized matrix or optimization gips object

Plot the heatmap of the MAP covariance matrix estimator or the convergence of the optimization method. The plot depends on the type argument.

Usage

# S3 method for class 'gips'
plot(
  x,
  type = NA,
  logarithmic_y = TRUE,
  logarithmic_x = FALSE,
  color = NULL,
  title_text = "Convergence plot",
  xlabel = NULL,
  ylabel = NULL,
  show_legend = TRUE,
  ylim = NULL,
  xlim = NULL,
  ...
)

Arguments

x

Object of a gips class.

type

A character vector of length 1. One of c("heatmap", "MLE", "best", "all", "both", "n0", "block_heatmap"):

"heatmap", "MLE" - Plots a heatmap of the Maximum Likelihood Estimator of the covariance matrix given the permutation. That is, the S matrix inside the gips object projected on the permutation in the gips object.
"best" - Plots the line of the biggest a posteriori found over time.
"all" - Plots the line of a posteriori for all visited states.
"both" - Plots both lines from "all" and "best".
"n0" - Plots the line of n0s that were spotted during optimization (only for "MH" optimization).
"block_heatmap" - Plots a heatmap of diagonally block representation of S. Non-block entries (equal to 0) are white for better clarity. For more information, see Block Decomposition - [1], Theorem 1 section in vignette("Theory", package = "gips") or in its pkgdown page.

The default value is NA, which will be changed to "heatmap" for non-optimized gips objects and to "both" for optimized ones. Using the default produces a warning. All other arguments are ignored for the type = "heatmap", type = "MLE", or type = "block_heatmap".

logarithmic_y, logarithmic_x

A boolean. Sets the axis of the plot in logarithmic scale.

color

Vector of colors to be used to plot lines.

title_text

Text to be in the title of the plot.

xlabel

Text to be on the bottom of the plot.

ylabel

Text to be on the left of the plot.

show_legend

A boolean. Whether or not to show a legend.

ylim

Limits of the y axis. When NULL, the minimum, and maximum of the log_posteriori_of_gips() are taken.

xlim

Limits of the x axis. When NULL, the whole optimization process is shown.

...

Additional arguments passed to other various elements of the plot.

Value

When type is one of "best", "all", "both" or "n0", returns an invisible NULL. When type is one of "heatmap", "MLE" or "block_heatmap", returns an object of class ggplot.

Examples

require("MASS") # for mvrnorm()

perm_size <- 6
mu <- runif(6, -10, 10) # Assume we don't know the mean
sigma_matrix <- matrix(
  data = c(
    1.0, 0.8, 0.6, 0.4, 0.6, 0.8,
    0.8, 1.0, 0.8, 0.6, 0.4, 0.6,
    0.6, 0.8, 1.0, 0.8, 0.6, 0.4,
    0.4, 0.6, 0.8, 1.0, 0.8, 0.6,
    0.6, 0.4, 0.6, 0.8, 1.0, 0.8,
    0.8, 0.6, 0.4, 0.6, 0.8, 1.0
  ),
  nrow = perm_size, byrow = TRUE
) # sigma_matrix is a matrix invariant under permutation (1,2,3,4,5,6)
number_of_observations <- 13
Z <- MASS::mvrnorm(number_of_observations, mu = mu, Sigma = sigma_matrix)
S <- cov(Z) # Assume we have to estimate the mean

g <- gips(S, number_of_observations)
if (require("graphics")) {
  plot(g, type = "MLE")
}


g_map <- find_MAP(g, max_iter = 30, show_progress_bar = FALSE, optimizer = "hill_climbing")
if (require("graphics")) {
  plot(g_map, type = "both", logarithmic_x = TRUE)
}


if (require("graphics")) {
  plot(g_map, type = "MLE")
}

# Now, the output is (most likely) different because the permutation
  # `g_map[[1]]` is (most likely) not an identity permutation.

g_map_MH <- find_MAP(g, max_iter = 30, show_progress_bar = FALSE, optimizer = "MH")
if (require("graphics")) {
  plot(g_map_MH, type = "n0")
}

Plot optimized matrix or optimization `gips` object

Usage

Arguments

Value

See also

Examples