Construct and analyze confusion matrices
Description
Confusion matrices compare two classifications (usually one done automatically using a machine learning algorithm versus the true classication represented by a manual classification by a specialist... but one can also compare two automatic or two manual classifications against each other).
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38  confusion(x, ...)
## Default S3 method:
confusion(x, y = NULL, vars = c("Actual", "Predicted"),
labels = vars, merge.by = "Id", useNA = "ifany", prior, ...)
## S3 method for class 'mlearning'
confusion(x, y = response(x),
labels = c("Actual", "Predicted"), useNA = "ifany", prior, ...)
## S3 method for class 'confusion'
print(x, sums = TRUE, error.col = sums, digits = 0,
sort = "ward", ...)
## S3 method for class 'confusion'
summary(object, type = "all", sort.by = "Fscore",
decreasing = TRUE, ...)
## S3 method for class 'summary.confusion'
print(x, ...)
## S3 method for class 'confusion'
plot(x, y = NULL, type = c("image", "barplot", "stars",
"dendrogram"), stat1 = "Recall", stat2 = "Precision", names, ...)
confusionImage(x, y = NULL, labels = names(dimnames(x)), sort = "ward",
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", ...)
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 = "Fscore (precision versus recall)", numbers = TRUE, min.width = 17,
...)
confusionStars(x, y = NULL, stat1 = "Recall", stat2 = "Precision", names, main,
col = c("green2", "blue2", "green4", "blue4"), ...)
confusionDendrogram(x, y = NULL, labels = rownames(x), sort = "ward",
main = "Groups clustering", ...)
prior(object, ...)
## S3 method for class 'confusion'
prior(object, ...)
prior(object, ...) < value
## S3 replacement method for class 'confusion'
prior(object, ...) < value

Arguments
x 
an object. 
y 
another object, from which to extract the second classification, or

vars 
the variables of interest in the first and second classification
in the case the objects are lists or data frames. Otherwise, this argument
is ignored and 
labels 
labels to use for the two classifications. By default, it is
the same as 
merge.by 
a character string with the name of variables to use to merge
the two data frames, or 
useNA 
do we keep NAs as a separate category? The default 
prior 
class frequencies to use for first classifier that
is tabulated in the rows of the confusion matrix. For its value, see here
under, the 
sums 
is the confusion matrix printed with rows and columns sums? 
error.col 
is a column with class error for first classifier added (equivalent to flase negative rate of FNR)? 
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. 
sort 
are rows and columns of the confusion matrix sorted so that
classes with larger confusion are closer together? Sorting is done
using a hierachical clustering with 
object 
a 'confusion' object. 
sort.by 
the statistics to use to sort the table (by default, Fmeasure, the F1 score for each class = 2 * recall * precision / (recall + precision)). 
decreasing 
do we sort in increasing or decreasing order? 
type 
the type of graph to plot (only 
stat1 
first statistic to compare in the stars plot. 
stat2 
second statistic to compare in the stars plot. 
... 
further arguments passed to the function. In particular for

numbers 
are actual numbers indicated in the confusion matrix image? 
mar 
graph margins. 
cex 
text magnification factor. 
cex.axis 
idem for axes. If 
cex.legend 
idem for legend text. If 
asp 
graph aspect ration. There is little reasons to cvhange the default value of 1. 
col 
color(s) to use fir the graph. 
colfun 
a function that calculates a series of colors, like e.g.,

ncols 
the number of colors to generate. It should preferrably 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 
names 
names of the two classifiers to compare. 
main 
main title of the graph. 
min.width 
minimum bar width required to add numbers. 
value 
a single positive numeric to set all class frequencies to this
value (use 1 for relative frequencies and 100 for relative freqs in percent),
or a vector of positive numbers of the same length as the levels in the
object. If the vector is named, names must match levels. Alternatively,
providing 
Value
A confusion matrix in a 'confusion' object. prior()
returns the
current class frequencies associated with first classification tabulated, i.e.,
for rows in the confusion matrix.
Author(s)
Philippe Grosjean <Philippe.Grosjean@umons.ac.be> and Kevin Denis <Kevin.Denis@umons.ac.be>
See Also
mlearning
, hclust
,
cm.colors
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59  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(glassLvq < mlLvq(Type ~ ., data = Glass))
## Calculate crossvalidated confusion matrix and plot it in different ways
(glassConf < confusion(cvpredict(glassLvq), Glass$Type))
## Raw confusion matrix: no sort and no margins
print(glassConf, sums = FALSE, sort = FALSE)
## Graphs
plot(glassConf) # Image by default
plot(glassConf, sort = FALSE) # No sorting
plot(glassConf, type = "barplot")
plot(glassConf, type = "stars")
plot(glassConf, type = "dendrogram")
summary(glassConf)
summary(glassConf, type = "Fscore")
## Build another classifier and make a comparison
summary(glassNaiveBayes < mlNaiveBayes(Type ~ ., data = Glass))
(glassConf2 < confusion(cvpredict(glassNaiveBayes), Glass$Type))
## Comparison plot for two classifiers
plot(glassConf, glassConf2)
## When the probabilities in each class do not match the proportions in the
## training set, all these calculations are useless. Having an idea of
## the real proportions (socalled, priors), one should first reweight the
## confusion matrix before calculating statistics, for instance:
prior1 < c(10, 10, 10, 100, 100, 100) # Glass types 13 are rare
prior(glassConf) < prior1
glassConf
summary(glassConf, type = c("Fscore", "Recall", "Precision"))
plot(glassConf)
## This is very different than if glass types 13 are abundants!
prior2 < c(100, 100, 100, 10, 10, 10) # Glass types 13 are abundants
prior(glassConf) < prior2
glassConf
summary(glassConf, type = c("Fscore", "Recall", "Precision"))
plot(glassConf)
## Weight can also be used to construct a matrix of relative frequencies
## In this case, all rows sum to one
prior(glassConf) < 1
print(glassConf, digits = 2)
## However, it is easier to work with relative frequencies in percent
## and one gets a more compact presentation
prior(glassConf) < 100
glassConf
## To reset row class frequencies to original propotions, just assign NULL
prior(glassConf) < NULL
glassConf
prior(glassConf)
