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 plr
object.
1 2 3 |
object |
|
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
plr
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | n <- 100
p <- 10
x0 <- matrix(rnorm(n * p), nrow=n)
y <- sample(c(0, 1), n, replace=TRUE)
fit <- plr(x0, y, lambda=1)
x1 <- matrix(rnorm(n * p), nrow=n)
pred1 <- predict(fit, x1, type="link")
pred2 <- predict(fit, x1, type="response")
pred3 <- predict(fit, x1, type="class")
p <- 3
z <- matrix(sample(seq(3), n * p, replace=TRUE), nrow=n)
x0 <- data.frame(x1=factor(z[, 1]), x2=factor(z[, 2]), x3=factor(z[, 3]))
y <- sample(c(0, 1), n, replace=TRUE)
fit <- plr(x0, y, lambda=1)
z <- matrix(sample(seq(3), n * p, replace=TRUE), nrow=n)
x1 <- data.frame(x1=factor(z[, 1]), x2=factor(z[, 2]), x3=factor(z[, 3]))
pred1 <- predict(fit, x1, type="link")
pred2 <- predict(fit, x1, type="response")
pred3 <- predict(fit, x1, type="class")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.