# Predict method for Common Components Latent Class Discriminant Analysis (CCLCDA) Classifies new observations using parameters determined by the `cclcda`-function. UTF-8 ```r ## 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. ## Returns 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 ```r # 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) ```

predict.cclcda function