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,...)
"partimat"(x, grouping, method = "lda", prec = 100, nplots.vert, nplots.hor, main = "Partition Plot", name, mar, plot.matrix = FALSE, plot.control = list(), ...)
"partimat"(x, ...)
"partimat"(x, grouping, ..., subset, na.action = na.fail)
"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 values on any required variable. (Note: If given, this argument must be named.) c(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) + 0.1
otherwise.TRUE
, like a scatterplot matrix;
if FALSE
(default) uses less space and arranges the plots optimal
(using a fuzzy algorithm) in an array by plotting each pair of variables once.drawparti
).method
(through drawparti
).drawparti
library(MASS)
data(iris)
partimat(Species ~ ., data = iris, method = "lda")
## Not run:
# partimat(Species ~ ., data = iris, method = "lda",
# plot.matrix = TRUE, imageplot = FALSE) # takes some time ...
# ## End(Not run)
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