ipred (version 0.6-14)

predict.inclass: Predictions from an Inclass Object

Description

Predicts the class membership of new observations through indirect classification.

Usage

## S3 method for class 'inclass':
predict(object, cFUN, intbag=NULL, newdata, ...)

Arguments

object

object of class inclass, see inclass.

cFUN

fixed function which classifies the predicted intermediates.

intbag

logical. If pFUN = bagging bagging either stabilises the prediction of the intermediate variables or the classifications. If intbag = TRUE intermediates are aggregated, bagging of the whole classifying procedure is performed oth

newdata

data frame to be classified.

...

additional arguments corresponding to the predictive models specified in inclass. If the intermediate is nominal and pFUN = rpart or bagging one argument has to be type = "c

Value

• The vector of predicted classes is returned.

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.

See Also

inclass

Examples

# 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) {
  z <- (Y > threshold)
  resp <- as.factor(ifelse((z[,1] + z[,2]) > 1, 1, 0))
  return(resp)
}

formula <- flist(y1+y2~x1+x2)

fit <- inclass(formula, pFUN = lm, data = dataset)

predict.inclass(object = fit, cFUN = classify, newdata = dataset)


data(Smoking)

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

classify <- function(data){
  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 <- TVPS+BPNL+COHB~TarY+NicY+COY+Sex+Age

fit <- inclass(formula, pFUN = lm, data = smoking)

predict.inclass(object = fit, cFUN = classify, newdata = smoking)


data(GlaucomaMVF)
glaucoma <- GlaucomaMVF[,-67]
# 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(clv+lora+cs~., data = glaucoma, pFUN = bagging)
predict.inclass(object = fit, cFUN = classify, newdata = glaucoma)