Description Usage Arguments Author(s) References See Also Examples
This function computes the linear predictors, probability estimates,
or the class labels for new data, using a stepplr
object.
1 2 3 |
object |
|
x |
matrix of features used for fitting |
newx |
matrix of features at which the predictions are made. If
|
type |
If |
... |
other options for prediction |
Mee Young Park and Trevor Hastie
Mee Young Park and Trevor Hastie (2008) Penalized Logistic Regression for Detecting Gene Interactions
stepplr
1 2 3 4 5 6 7 8 9 10 11 12 13 | n <- 100
p <- 5
x0 <- matrix(sample(seq(3), n * p, replace=TRUE), nrow=n)
x0 <- cbind(rnorm(n), x0)
y <- sample(c(0, 1), n, replace=TRUE)
level <- vector("list", length=6)
for (i in 2:6) level[[i]] <- seq(3)
fit <- step.plr(x0, y, level=level)
x1 <- matrix(sample(seq(3), n * p, replace=TRUE), nrow=n)
x1 <- cbind(rnorm(n), x1)
pred1 <- predict(fit, x0, x1, type="link")
pred2 <- predict(fit, x0, x1, type="response")
pred3 <- predict(fit, x0, x1, type="class")
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