polspline (version 1.1.19)

cpolyclass: Polyclass: polychotomous regression and multiple classification

Description

Classify new cases (cpolyclass), compute class probabilities for new cases (ppolyclass), and generate random multinomials for new cases (rpolyclass) for a polyclass model.

Usage

cpolyclass(cov, fit)
ppolyclass(data, cov, fit) 
rpolyclass(n, cov, fit)

Arguments

cov

covariates. Should be a matrix with fit\$ncov columns. For rpolyclass cov should either have one row, in which case all random numbers are based on the same covariates, or n rows in which case each random number has its own covariates.

fit

polyclass object, typically the result of polyclass.

data

there are several possibilities. If data is a vector with as many elements as cov has rows, each element of data corresponds to a row of cov; if only one value is given, the probability of being in that class is computed for all sets of covariates. If data is omitted, all class probabilities are provided.

n

number of pseudo random numbers to be generated.

Value

Most likely classes (cpolyclass), probabilities (cpolyclass), or random classes according to the estimated probabilities (rpolyclass).

References

Charles Kooperberg, Smarajit Bose, and Charles J. Stone (1997). Polychotomous regression. Journal of the American Statistical Association, 92, 117--127.

Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion) (1997). Annals of Statistics, 25, 1371--1470.

See Also

polyclass, plot.polyclass, summary.polyclass, beta.polyclass.

Examples

# NOT RUN {
data(iris)
fit.iris <- polyclass(iris[,5], iris[,1:4])
class.iris <- cpolyclass(iris[,1:4], fit.iris)
table(class.iris, iris[,5])
prob.setosa <- ppolyclass(1, iris[,1:4], fit.iris)
prob.correct <- ppolyclass(iris[,5], iris[,1:4], fit.iris) 
rpolyclass(100, iris[64,1:4], fit.iris)
# }