R: Classifying a Numerical Response Using a Threshold

threshold

R Documentation

Classifying a Numerical Response Using a Threshold

Description

Classification of a numerical response into a “high” class and “low” class using a threshold. This function can be used with any model that has a numerical outcome and allows for prediction using the predict function.

Usage

threshold(fit, t, newdata=NULL, ...)

Arguments

`fit`	any model with a numerical response.
`t`	the desired threshold value. All values above `t` will be labeled “1”and all values below `t` will be labeled “0”.
`newdata`	an optional data frame in which to look for variables with which to predict. If omitted, no prediction is done.
`...`	additional argument(s) for methods in the `predict` function.

Value

`pred.class`	if `newdata` is not `NULL`, then `pred.class` is a vector of predicted classes for `newdata`. If `newdata` is `NULL`, then `pred.class` is `NULL`.
`t`	the threshold.
`train.class`	a vector of the predicted classes of the data used in `fit`.
`true.class`	a vector of the true classes of the data used in `fit`.
`train.error`	a scalar equal to the `mean(train.class != true.class)`.
`true.high`	the number of observations in class“1” using the data used in `fit`.
`true.low`	the number of observations in class “1” using the data used in `fit`.
`false.high`	the number of observations truly in class “0”, but predicted to be in class “1” using the data used in `fit`.
`false.low`	the number of observations truly in class “1”, but predicted to be in class “1” using the data used in `fit`.
`call`	the `call` of `fit`.
`formula`	the formula used in `fit`.

Author(s)

Adam Maidman

Examples

data(simData)
fit <- plaqr(y~.,~z1+z2,data=simData)
testdata <- .5*simData[4,2:6]
trh <- threshold(fit, t=9, newdata=testdata)
trh$pred.class
trh