ncvreg (version 3.13.0)

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

Value

The object returned depends on type.

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 coefficients; "vars" returns a list containing the indices and names of the nonzero variables at each value of lambda; "nvars" returns the number of nonzero coefficients at each value of lambda.

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.

Author

Patrick Breheny

References

Breheny P and Huang J. (2011) Coordinate descentalgorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. tools:::Rd_expr_doi("10.1214/10-AOAS388")

See Also

ncvreg

Examples

data(Heart)

fit <- ncvreg(Heart$X, Heart$y, family="binomial")
coef(fit, lambda=0.05)
head(predict(fit, Heart$X, type="link", lambda=0.05))
head(predict(fit, Heart$X, type="response", lambda=0.05))
head(predict(fit, Heart$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))