predict.cclcda: Predict method for Common Components Latent Class...

Description Usage Arguments Details Value Author(s) See Also Examples

Description

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

Usage

1
2
## S3 method for class 'cclcda'
predict(object, newdata, ...)

Arguments

object

Object of class cclcda.

newdata

Data frame of cases to be classified.

...

Further arguments are ignored.

Details

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

Author(s)

Michael B\"ucker

See Also

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

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
# 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]])
}

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

lcda documentation built on May 2, 2019, 8:50 a.m.

Related to predict.cclcda in lcda...