ipred (version 0.9-15)

predict.inclass: Predictions from an Inclass Object

Description

Predicts the class membership of new observations through indirect classification.

Usage

# S3 method for inclass
predict(object, newdata, ...)

Value

The vector of predicted classes is returned.

Arguments

object

object of class inclass, see inclass.

newdata

data frame to be classified.

...

additional arguments corresponding to the predictive models specified in inclass.

Details

Predictions of class memberships are calculated. i.e. values of the intermediate variables are predicted and classified following cFUN, see inclass.

References

David J. Hand, Hua Gui Li, Niall M. Adams (2001), Supervised classification with structured class definitions. Computational Statistics & Data Analysis 36, 209--225.

Andrea Peters, Berthold Lausen, Georg Michelson and Olaf Gefeller (2003), Diagnosis of glaucoma by indirect classifiers. Methods of Information in Medicine 1, 99-103.

See Also

inclass

Examples

if (FALSE) {
# Simulation model, classification rule following Hand et al. (2001)

theta90 <- varset(N = 1000, sigma = 0.1, theta = 90, threshold = 0)

dataset <- as.data.frame(cbind(theta90$explanatory, theta90$intermediate))
names(dataset) <- c(colnames(theta90$explanatory),
colnames(theta90$intermediate))

classify <- function(Y, threshold = 0) {
  Y <- Y[,c("y1", "y2")]
  z <- (Y > threshold)
  resp <- as.factor(ifelse((z[,1] + z[,2]) > 1, 1, 0))
  return(resp)
}

formula <- response~y1+y2~x1+x2

fit <- inclass(formula, data = dataset, pFUN = list(list(model = lm)), 
 cFUN = classify)

predict(object = fit, newdata = dataset)


data("Smoking", package = "ipred")

# explanatory variables are: TarY, NicY, COY, Sex, Age
# intermediate variables are: TVPS, BPNL, COHB
# reponse is defined by:

classify <- function(data){
  data <- data[,c("TVPS", "BPNL", "COHB")]
  res <- t(t(data) > c(4438, 232.5, 58))
  res <- as.factor(ifelse(apply(res, 1, sum) > 2, 1, 0))
  res
}

response <- classify(Smoking[ ,c("TVPS", "BPNL", "COHB")])
smoking <- cbind(Smoking, response)

formula <- response~TVPS+BPNL+COHB~TarY+NicY+COY+Sex+Age

fit <- inclass(formula, data = smoking, 
  pFUN = list(list(model = lm)), cFUN = classify)


predict(object = fit, newdata = smoking)
}

data("GlaucomaMVF", package = "ipred")
library("rpart")
glaucoma <- GlaucomaMVF[,(names(GlaucomaMVF) != "tension")]
# explanatory variables are derived by laser scanning image and intra occular pressure
# intermediate variables are: clv, cs, lora
# response is defined by

classify <- function (data) {
  attach(data) 
  res <- ifelse((!is.na(clv) & !is.na(lora) & clv >= 5.1 & lora >= 
        49.23372) | (!is.na(clv) & !is.na(lora) & !is.na(cs) & 
        clv < 5.1 & lora >= 58.55409 & cs < 1.405) | (is.na(clv) & 
        !is.na(lora) & !is.na(cs) & lora >= 58.55409 & cs < 1.405) | 
        (!is.na(clv) & is.na(lora) & cs < 1.405), 0, 1)
  detach(data)
  factor (res, labels = c("glaucoma", "normal"))
}

fit <- inclass(Class~clv+lora+cs~., data = glaucoma, 
             pFUN = list(list(model = rpart)), cFUN = classify)

data("GlaucomaM", package = "TH.data")
predict(object = fit, newdata = GlaucomaM)