lda
, qda
) for every combination of two variables.
Moreover, the classification borders are displayed and the apparent error rates are given in each title.partimat(x,...)
## S3 method for class 'default':
partimat(x, grouping, method = "lda", prec = 100,
nplots.vert, nplots.hor, main = "Partition Plot", name, mar,
plot.matrix = FALSE, plot.control = list(), ...)
## S3 method for class 'data.frame':
partimat(x, ...)
## S3 method for class 'matrix':
partimat(x, grouping, ..., subset, na.action = na.fail)
## S3 method for class 'formula':
partimat(formula, data = NULL, ..., subset, na.action = na.fail)
formula
is not given).formula
is not given).groups ~ x1 + x2 + ...
.
That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators.NA
s are
found. The default action is for the procedure to fail. An
alternative is na.omit
, which leads to rejection of cases with
missing valuesc(bottom, left, top, right)
which gives the lines of margin to be specified on the four sides of the plot.
Defaults are rep(0, 4)
if plot.matrix = TRUE
, c(5, 4, 2, 1) + 0.
TRUE
, like a scatterplot matrix;
if FALSE
(default) uses less space and arranges the plots drawparti
).method
(through drawparti
).drawparti
library(MASS)
data(iris)
partimat(Species ~ ., data = iris, method = "lda")
partimat(Species ~ ., data = iris, method = "lda",
plot.matrix = TRUE, imageplot = FALSE) # takes some time ...
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