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

Classifies new observations using parameters determined by
the `cclcda`

-function.

1 2 |

`object` |
Object of class |

`newdata` |
Data frame of cases to be classified. |

`...` |
Further arguments are ignored. |

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.

A list with components:

`class` |
Vector (of class |

`posterior` |
Posterior probabilities for the classes.
For details of computation see |

Michael B\"ucker

`cclcda`

, `lcda`

, `predict.lcda`

, `cclcda2`

, `predict.cclcda2`

, `poLCA`

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)
``` |

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