predict.ncvsurv: Model predictions based on a fitted 'ncvsurv' object.

In ncvreg: Regularization Paths for SCAD and MCP Penalized Regression Models

Model predictions based on a fitted ncvsurv object.

Description

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

Usage

## S3 method for class 'ncvsurv'
predict(
  object,
  X,
  type = c("link", "response", "survival", "hazard", "median", "coefficients", "vars",
    "nvars"),
  lambda,
  which = 1:length(object$lambda),
  ...
)

Arguments

object	Fitted "ncvsurv" 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.
type	Type of prediction: link returns the linear predictors response gives the risk (i.e., exp(link)) survival returns the estimated survival function median estimates median survival times The other options are all identical to their ncvreg() counterparts: 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.
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.
...	Not used.

Details

Estimation of baseline survival function conditional on the estimated values of beta is carried out according to the method described in Chapter 4.3 of Kalbfleish and Prentice. In particular, it agrees exactly the results returned by survfit.coxph(..., type='kalbfleisch-prentice') in the survival package.

Value

The object returned depends on type.

Author(s)

Patrick Breheny [e-mail]

References

• Breheny P and Huang J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/10-AOAS388")}

• Kalbfleish JD and Prentice RL (2002). The Statistical Analysis of Failure Time Data, 2nd edition. Wiley.

See Also

ncvsurv()

Examples

data(Lung)
X <- Lung$X
y <- Lung$y

fit <- ncvsurv(X,y)
coef(fit, lambda=0.05)
head(predict(fit, X, type="link", lambda=0.05))
head(predict(fit, X, type="response", lambda=0.05))

# Survival function
S <- predict(fit, X[1,], type="survival", lambda=0.05)
S(100)
S <- predict(fit, X, type="survival", lambda=0.05)
plot(S, xlim=c(0,200))

# Medians
predict(fit, X[1,], type="median", lambda=0.05)
M <- predict(fit, X, type="median")
M[1:10, 1:10]

# Nonzero coefficients
predict(fit, type="vars", lambda=c(0.1, 0.01))
predict(fit, type="nvars", lambda=c(0.1, 0.01))

ncvreg documentation built on Sept. 11, 2024, 8:31 p.m.