Crossvalidation Method for class discRob

DESCRIPTION:

Compute leave-one-out crossvalidation of a discRob object.

USAGE:

crossvalidate.discRob(object, doubt=0, digits=.Options$digits, 
                      trace=T, subset)

REQUIRED ARGUMENTS:

object
the discRob object constructed by the discRob function.

OPTIONAL ARGUMENTS:

doubt
a numerical variable, 0 <= doubt < (g-1) / g, where g is the number of groups, defining the minimum difference the maximum posterior probability of group membership must exceed all others before assigning the observation to that group. If there does not exist a posterior probability of a group membership for an observation that exceeds doubt + 1/g, the observation is assigned to an "unknown" group.
digits
the number of significant digits to retain in the posterior probability. Rounding the posterior probabilities is odne mostly for printing purposes.
trace
a logical flag for printing trace information. A classification table is printed for every 10 iterations.
subset
an integer or logical vector specifying which observations to crossvalidate.

VALUE:

an n x (g+1) data frame, where n is the number of observations and g is the number of groups, containing a group factor column followed by the posterior probability of group membership for each observation in the newdata data frame or the training data.

DETAILS:

Performs crossvalidation of the discriminant function contained in object . This function may take a considerable amount of time to compute the leave-one-out error rate.