a formula object, specifying the group variable and feature variables,
with the group variable on the left of a `~' operator, and the feature
variables, separated by
+ operators, on the right. If data is given,
all names used in the formula should be defined as variables in the
data frame.
data
a data frame in which to interpret the variables named in
formula. This is commonly refered to as the training data for the
discriminant function.
OPTIONAL ARGUMENTS:
weights
vector of observation weights. If supplied, the weighted mean and
covariances are computed for the feature variables specified in the
formula. The weights must be positive.
frequencies
a vector of observation frequencies.
subset
subset expression for rows.
na.omit
logical indicating whether to omit rows with missing values.
family
type of covariance family.
cov.structure
type of covariance structure.
prior
type of group prior.
printShort
if
TRUE, a short summary is printed.
printLong
if
TRUE, a long summary is printed.
plugIn
if
TRUE, save the plug-in group membership probabilities in
predResult.
predictive
if
TRUE, save the predictive group membership probabilities in
predResult.
unbiased
if
TRUE, save the unbiased group membership probabilities in
predResult.
crossValidate
if
TRUE, save the cross validated group membership probabilities in
predResult.
predResult
name of data set in which to save group membership probabilities.
doPlots
logical indicating whether to create a discriminant plot.
VALUE:
a
discrim obejct as described in the help for
discrim.