Learn R Programming

ncvreg (version 3.4-0)

predict.ncvreg: Model predictions based on a fitted "ncvreg" object.

Description

Similar to other predict methods, this function returns predictions from a fitted "ncvreg" object.

Usage

## S3 method for class 'ncvreg':
predict(object, X, type=c("link", "response", "class",
"coefficients", "vars", "nvars"), lambda, which=1:length(object$lambda),
...)
## S3 method for class 'ncvreg':
coef(object, lambda, which=1:length(object$lambda),
drop=TRUE, ...)

Arguments

object
Fitted "ncvreg" model object.
X
Matrix of values at which predictions are to be made. Not used for type="coefficients" or for some of the type settings in predict.
lambda
Values of the regularization parameter lambda at which predictions are requested. For values of lambda not in the sequence of fitted models, linear interpolation is used.
which
Indices of the penalty parameter lambda at which predictions are required. By default, all indices are returned. If lambda is specified, this will override which.
type
Type of prediction: "link" returns the linear predictors; "response" gives the fitted values; "class" returns the binomial outcome with the highest probability; "coefficients" returns the coe
drop
If coefficients for a single value of lambda are to be returned, reduce dimensions to a vector? Setting drop=FALSE returns a 1-column matrix.
...
Not used.

Value

  • The object returned depends on type.

References

Breheny, P. and Huang, J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann. Appl. Statist., 5: 232-253.

See Also

ncvreg

Examples

Run this code
data(heart)
X <- as.matrix(heart[,1:9])
y <- heart$chd

fit <- ncvreg(X,y,family="binomial")
coef(fit, lambda=0.05)
head(predict(fit, X, type="link", lambda=0.05))
head(predict(fit, X, type="response", lambda=0.05))
head(predict(fit, X, type="class", lambda=0.05))
predict(fit, type="vars", lambda=c(0.05, 0.01))
predict(fit, type="nvars", lambda=c(0.05, 0.01))

Run the code above in your browser using DataLab