mlearning (version 1.2.1)

plot.confusion: Plot a confusion matrix

Description

Several graphical representations of confusion objects are possible: an image of the matrix with colored squares, a barplot comparing recall and precision, a stars plot also comparing two metrics, possibly also comparing two different classifiers of the same dataset, or a dendrogram grouping the classes relative to the errors observed in the confusion matrix (classes with more errors are pooled together more rapidly).

Usage

# S3 method for confusion
plot(
  x,
  y = NULL,
  type = c("image", "barplot", "stars", "dendrogram"),
  stat1 = "Recall",
  stat2 = "Precision",
  names,
  ...
)
confusion_image(
  x,
  y = NULL,
  labels = names(dimnames(x)),
  sort = "ward.D2",
  numbers = TRUE,
  digits = 0,
  mar = c(3.1, 10.1, 3.1, 3.1),
  cex = 1,
  asp = 1,
  colfun,
  ncols = 41,
  col0 = FALSE,
  grid.col = "gray",
  ...
)
confusionImage(
  x,
  y = NULL,
  labels = names(dimnames(x)),
  sort = "ward.D2",
  numbers = TRUE,
  digits = 0,
  mar = c(3.1, 10.1, 3.1, 3.1),
  cex = 1,
  asp = 1,
  colfun,
  ncols = 41,
  col0 = FALSE,
  grid.col = "gray",
  ...
)
confusion_barplot(
  x,
  y = NULL,
  col = c("PeachPuff2", "green3", "lemonChiffon2"),
  mar = c(1.1, 8.1, 4.1, 2.1),
  cex = 1,
  cex.axis = cex,
  cex.legend = cex,
  main = "F-score (precision versus recall)",
  numbers = TRUE,
  min.width = 17,
  ...
)
confusionBarplot(
  x,
  y = NULL,
  col = c("PeachPuff2", "green3", "lemonChiffon2"),
  mar = c(1.1, 8.1, 4.1, 2.1),
  cex = 1,
  cex.axis = cex,
  cex.legend = cex,
  main = "F-score (precision versus recall)",
  numbers = TRUE,
  min.width = 17,
  ...
)
confusion_stars(
  x,
  y = NULL,
  stat1 = "Recall",
  stat2 = "Precision",
  names,
  main,
  col = c("green2", "blue2", "green4", "blue4"),
  ...
)
confusionStars(
  x,
  y = NULL,
  stat1 = "Recall",
  stat2 = "Precision",
  names,
  main,
  col = c("green2", "blue2", "green4", "blue4"),
  ...
)
confusion_dendrogram(
  x,
  y = NULL,
  labels = rownames(x),
  sort = "ward.D2",
  main = "Groups clustering",
  ...
)
confusionDendrogram(
  x,
  y = NULL,
  labels = rownames(x),
  sort = "ward.D2",
  main = "Groups clustering",
  ...
)

Value

Data calculate to create the plots are returned invisibly. These functions are mostly used for their side-effect of producing a plot.

Arguments

x

a confusion object

y

NULL (not used), or a second confusion object when two different classifications are compared in the plot ("stars" type).

type

the kind of plot to produce ("image", the default, or "barplot", "stars", "dendrogram").

stat1

the first metric to plot for the "stars" type (Recall by default).

stat2

the second metric to plot for the "stars" type (Precision by default).

names

names of the two classifiers to compare

...

further arguments passed to the function. It can be all arguments or the corresponding plot.

labels

labels to use for the two classifications. By default, they are the same as vars, or the one in the confusion matrix.

sort

are rows and columns of the confusion matrix sorted so that classes with larger confusion are closer together? Sorting is done using a hierarchical clustering with hclust(). The clustering method is "ward.D2" by default, but see the hclust() help for other options). If FALSE or NULL, no sorting is done.

numbers

are actual numbers indicated in the confusion matrix image?

digits

the number of digits after the decimal point to print in the confusion matrix. The default or zero leads to most compact presentation and is suitable for frequencies, but not for relative frequencies.

mar

graph margins.

cex

text magnification factor.

asp

graph aspect ratio. There is little reasons to change the default value of 1.

colfun

a function that calculates a series of colors, like e.g., cm.colors() that accepts one argument being the number of colors to be generated.

ncols

the number of colors to generate. It should preferably be 2 * number of levels + 1, where levels is the number of frequencies you want to evidence in the plot. Default to 41.

col0

should null values be colored or not (no, by default)?

grid.col

color to use for grid lines, or NULL for not drawing grid lines.

col

color(s) to use for the plot.

cex.axis

idem for axes. If NULL, the axis is not drawn.

cex.legend

idem for legend text. If NULL, no legend is added.

main

main title of the plot.

min.width

minimum bar width required to add numbers.

Examples

data("Glass", package = "mlbench")
# Use a little bit more informative labels for Type
Glass$Type <- as.factor(paste("Glass", Glass$Type))

# Use learning vector quantization to classify the glass types
# (using default parameters)
summary(glass_lvq <- ml_lvq(Type ~ ., data = Glass))

# Calculate cross-validated confusion matrix and plot it in different ways
(glass_conf <- confusion(cvpredict(glass_lvq), Glass$Type))
# Raw confusion matrix: no sort and no margins
print(glass_conf, sums = FALSE, sort = FALSE)
# Plots
plot(glass_conf) # Image by default
plot(glass_conf, sort = FALSE) # No sorting
plot(glass_conf, type = "barplot")
plot(glass_conf, type = "stars")
plot(glass_conf, type = "dendrogram")

# Build another classifier and make a comparison
summary(glass_naive_bayes <- ml_naive_bayes(Type ~ ., data = Glass))
(glass_conf2 <- confusion(cvpredict(glass_naive_bayes), Glass$Type))

# Comparison plot for two classifiers
plot(glass_conf, glass_conf2)