threshold: Classifying a Numerical Response Using a Threshold

In plaqr: Partially Linear Additive Quantile Regression

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

plaqr documentation built on May 2, 2019, 3:32 p.m.