predict.lcda | R Documentation |
Classifies new observations using the parameters determined by
the lcda
-function.
## S3 method for class 'lcda' predict(object, newdata, ...)
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 lcda
-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
lcda
, cclcda
, predict.cclcda
, cclcda2
, predict.cclcda2
, poLCA
# 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.