plot.gips {gips} | R Documentation |
Plot optimized matrix or optimization gips
object
Description
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 |
type |
A character vector of length 1. One of
The default value is |
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 |
xlim |
Limits of the x axis. When |
... |
Additional arguments passed to other various elements of the plot. |
Value
When type
is one of "best"
, "all"
or "both"
,
returns an invisible NULL
.
When type
is one of "heatmap"
, "MLE"
or "block_heatmap"
,
returns an object of class ggplot
.
See Also
-
find_MAP()
- Usually, theplot.gips()
is called on the output offind_MAP()
. -
project_matrix()
- The function used withtype = "MLE"
. -
gips()
- The constructor of agips
class. Thegips
object is used as thex
parameter.
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.