predict.lcda {lcda}

Predict method for Latent Class Discriminant Analysis (LCDA)

Description

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

Usage

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

Arguments

object

Object of class lcda2.

newdata

Data frame of cases to be classified.

...

Further arguments are ignored.

Details

Posterior probabilities for new observations using parameters determined by the lcda-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 lcda.

Author(s)

Michael B\"ucker

See Also

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

Examples

# response probabilites for class 1
probs1 <- list()
probs1[[1]] <- matrix(c(0.7,0.1,0.1,0.1,0.1,0.7,0.1,0.1), 
                      nrow=2, byrow=TRUE)
probs1[[2]] <- matrix(c(0.1,0.7,0.1,0.1,0.1,0.1,0.7,0.1),
                      nrow=2, byrow=TRUE)
probs1[[3]] <- matrix(c(0.1,0.1,0.7,0.1,0.1,0.1,0.1,0.7),
                      nrow=2, byrow=TRUE)
probs1[[4]] <- matrix(c(0.1,0.1,0.1,0.7,0.7,0.1,0.1,0.1),
                      nrow=2, byrow=TRUE)

# response probabilites for class 2
probs2 <- list()
probs2[[1]] <- matrix(c(0.1,0.1,0.7,0.1,0.1,0.1,0.1,0.7),
                      nrow=2, byrow=TRUE)
probs2[[2]] <- matrix(c(0.1,0.1,0.1,0.7,0.7,0.1,0.1,0.1),
                      nrow=2, byrow=TRUE)
probs2[[3]] <- matrix(c(0.7,0.1,0.1,0.1,0.1,0.7,0.1,0.1),
                      nrow=2, byrow=TRUE)
probs2[[4]] <- matrix(c(0.1,0.7,0.1,0.1,0.1,0.1,0.7,0.1),
                      nrow=2, byrow=TRUE)

# generation of data
simdata1 <- poLCA.simdata(N = 500, probs = probs1, nclass = 2,
              ndv = 4, nresp = 4, missval = FALSE)

simdata2 <- poLCA.simdata(N = 500, probs = probs2, nclass = 2,
              ndv = 4, nresp = 4, missval = FALSE)

data1 <- simdata1$dat
data2 <- simdata2$dat

data <- cbind(rbind(data1, data2), rep(c(1,2), each=500))
names(data)[5] <- "grouping"
data <- data[sample(1:1000),]
grouping <- data[[5]]
data <- data[,1:4]

# lcda-procedure
object <- lcda(data, grouping=grouping, m=2)
pred.class <- predict(object, newdata=data)$class
sum(pred.class==grouping)/length(pred.class)