Description Usage Arguments Details Value See Also Examples

View source: R/predict.cv.rgam.R

This function returns the predictions for a new data matrix from a
cross-validated `rgam`

model by using the stored "`glmfit`

"
object and the optimal value chosen for `lambda`

.

1 2 3 |

`object` |
Fitted " |

`xnew` |
Matrix of new values for |

`s` |
Value of the penalty parameter |

`...` |
Other arguments to be passed to |

This function makes it easier to use the results of cross-validation to make a prediction.

Predictions which the cross-validated model makes for `xnew`

at
the optimal value of `lambda`

. Note that the default is the "lambda.1se"
for lambda, to make this function consistent with `cv.glmnet`

in the
`glmnet`

package.

The output depends on the `...`

argument which is passed on to the predict
method for `rgam`

objects.

`cv.rgam`

and `predict.rgam`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
set.seed(1)
n <- 100; p <- 20
x <- matrix(rnorm(n * p), n, p)
beta <- matrix(c(rep(2, 5), rep(0, 15)), ncol = 1)
y <- x %*% beta + rnorm(n)
cvfit <- cv.rgam(x, y)
# predictions at the lambda.1se value
predict(cvfit, xnew = x[1:5, ])
# predictions at the lambda.min value
predict(cvfit, xnew = x[1:5, ], s = "lambda.min")
# predictions at specific lambda value
predict(cvfit, xnew = x[1:5, ], s = 0.1)
# probability predictions for binomial family
bin_y <- ifelse(y > 0, 1, 0)
cvfit2 <- cv.rgam(x, bin_y, family = "binomial")
predict(cvfit2, xnew = x[1:5, ], type = "response", s = "lambda.min")
``` |

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