ncvreg (version 3.8-0)

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

"predict"(object, X, type=c("link", "response", "class",
"coefficients", "vars", "nvars"), lambda, which=1:length(object$lambda),
...)
"coef"(object, lambda, which=1:length(object$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 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.

Value

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

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))