threshold: 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.
# NOT RUN {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
# }