Calibration for multiclassication methods
calibration(
pr,
cl,
weights = NULL,
threshold = 10,
method = "bin",
breaks = nclass.Sturges,
df = 3,
...
)matrix with probabilities for each class
class variable
counts
do not calibrate if less then 'threshold' events
either 'isotonic' (pava), 'logistic', 'mspline' (monotone spline), 'bin' (local constant)
optional number of bins (only for method 'bin')
degrees of freedom (only for spline methods)
additional arguments to lower level functions
An object of class 'calibration' is returned. See calibration-class
for more details about this class and its generic functions.
...
sim1 <- function(n, beta=c(-3, rep(.5,10)), rho=.5) {
p <- length(beta)-1
xx <- lava::rmvn0(n,sigma=diag(nrow=p)*(1-rho)+rho)
y <- rbinom(n, 1, lava::expit(cbind(1,xx)%*%beta))
d <- data.frame(y=y, xx)
names(d) <- c("y",paste0("x",1:p))
return(d)
}
set.seed(1)
beta <- c(-2,rep(1,10))
d <- sim1(1e4, beta=beta)
a1 <- NB(y ~ ., data=d)
a2 <- glm(y ~ ., data=d, family=binomial)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## a3 <- randomForest(factor(y) ~ ., data=d, family=binomial)
d0 <- sim1(1e4, beta=beta)
p1 <- predict(a1, newdata=d0)
p2 <- predict(a2, newdata=d0, type="response")
## p3 <- predict(a3, newdata=d0, type="prob")
c2 <- calibration(p2, d0$y, method="isotonic")
c1 <- calibration(p1, d0$y, breaks=100)
if (interactive()) {
plot(c1)
plot(c2,col="red",add=TRUE)
abline(a=0,b=1)##'
with(c1$xy[[1]], points(pred,freq,type="b", col="red"))
}
set.seed(1)
beta <- c(-2,rep(1,10))
dd <- lava::csplit(sim1(1e4, beta=beta), k=3)
mod <- NB(y ~ ., data=dd[[1]])
p1 <- predict(mod, newdata=dd[[2]])
cal <- calibration(p1, dd[[2]]$y)
p2 <- predict(mod, newdata=dd[[3]])
pp <- predict(c1, p2)
cc <- calibration(pp, dd[[3]]$y)
if (interactive()) {##'
plot(cal)
plot(cc, add=TRUE, col="blue")
}