predict.cclcda2 {lcda}

Predict method for Common Components Latent Class Discriminant Analysis 2 (CCLCDA2)

Description

Classifies new observations using parameters determined by the cclcda2-function.

Usage

## S3 method for class 'cclcda2'
predict(object, newdata, ...)

Arguments

object

Object of class cclcda2.

newdata

Data frame of cases to be classified.

...

Further arguments are ignored.

Details

Posterior probabilities for new observations using parameters determined by the cclcda2-function are computed. The classification of the new data is done by the Bayes decision function.

Value

A list with components:

class

Vector (of class factor) of classifications.

posterior

Posterior probabilities for the classes. For details of computation see cclcda2.

Author(s)

Michael B\"ucker

See Also

cclcda2, lcda, predict.lcda, cclcda, predict.cclcda, poLCA

Examples

# response probabilites
probs1 <- list()

probs1[[1]] <- matrix(c(0.7,0.1,0.1,0.1,0.1,0.7,0.1,0.1,
                        0.1,0.1,0.7,0.1,0.1,0.1,0.1,0.7), 
                      nrow=4, byrow=TRUE)
probs1[[2]] <- matrix(c(0.1,0.7,0.1,0.1,0.1,0.1,0.7,0.1,
                        0.1,0.1,0.1,0.7,0.7,0.1,0.1,0.1),
                      nrow=4, byrow=TRUE)
probs1[[3]] <- matrix(c(0.1,0.1,0.7,0.1,0.1,0.1,0.1,0.7,
                        0.7,0.1,0.1,0.1,0.1,0.7,0.1,0.1),
                      nrow=4, byrow=TRUE)
probs1[[4]] <- matrix(c(0.1,0.1,0.1,0.7,0.7,0.1,0.1,0.1,
                        0.1,0.7,0.1,0.1,0.1,0.1,0.7,0.1),
                      nrow=4, byrow=TRUE)

prior <- c(0.5,0.5)
wmk <- matrix(c(0.45,0.45,0.05,0.05,0.05,0.05,0.45,0.45),
              ncol=4, nrow=2, byrow=TRUE)
wkm <- apply(wmk*prior, 2, function(x) x/sum(x))

# generation of training data
data_temp <- poLCA.simdata(N = 1000, probs = probs1,
                           nclass = 2, ndv = 4, nresp = 4,
                           P=rep(0.25,4))
data <- data_temp$dat
lclass <- data_temp$trueclass
grouping <- numeric()
for (i in 1:length(lclass))
{
grouping[i] <- sample(c(1,2),1, prob=wkm[,lclass[i]])
}

# generation of test data
data_temp <- poLCA.simdata(N = 500, probs = probs1,
                           nclass = 2, ndv = 4, nresp = 4,
                           P=rep(0.25,4))
data.test <- data_temp$dat
lclass <- data_temp$trueclass
grouping.test <- numeric()
for (i in 1:length(lclass))
{
grouping.test[i] <- sample(c(1,2),1, prob=wkm[,lclass[i]])
}

# cclcda2-procedure
object <- cclcda2(data, grouping, m=4)
pred <- predict(object, data.test)$class
1-(sum(pred==grouping.test)/500)