Scoring: Predictive model scoring

In targeted: Targeted Inference

Predictive model scoring

Description

Predictive model scoring

Usage

scoring(
  response,
  ...,
  type = "quantitative",
  levels = NULL,
  metrics = NULL,
  weights = NULL,
  names = NULL,
  messages = 1
)

Arguments

response	Observed response
...	model predictions (continuous predictions or class probabilities (matrices))
type	continuous or categorical response (the latter is automatically chosen if response is a factor, otherwise a continuous response is assumed)
levels	(optional) unique levels in response variable
metrics	which metrics to report
weights	optional frequency weights
names	optional names of models coments (given as ..., alternatively these can be named arguments)
messages	controls amount of messages/warnings (0: none)

Value

Numeric matrix of dimension m x p, where m is the number of different models and p is the number of model metrics

Examples

data(iris)
set.seed(1)
dat <- csplit(iris,2)
g1 <- NB(Species ~ Sepal.Width + Petal.Length, data=dat[[1]])
g2 <- NB(Species ~ Sepal.Width, data=dat[[1]])
pr1 <- predict(g1, newdata=dat[[2]], wide=TRUE)
pr2 <- predict(g2, newdata=dat[[2]], wide=TRUE)
table(colnames(pr1)[apply(pr1,1,which.max)], dat[[2]]$Species)
table(colnames(pr2)[apply(pr2,1,which.max)], dat[[2]]$Species)
scoring(dat[[2]]$Species, pr1=pr1, pr2=pr2)
## quantitative response:
scoring(response=1:10, prediction=rnorm(1:10))

targeted documentation built on May 29, 2024, 2:08 a.m.