Model predictions based on a fitted ncvreg object.

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

Usage

# S3 method for class 'cv.ncvreg'
predict(
  object,
  X,
  type = c("link", "response", "class", "coefficients", "vars", "nvars"),
  which = object$min,
  ...
)

# S3 method for class 'cv.ncvreg'
coef(object, which = object$min, ...)

# S3 method for class 'cv.ncvsurv'
predict(
  object,
  X,
  type = c("link", "response", "survival", "median", "coefficients", "vars", "nvars"),
  which = object$min,
  ...
)

# 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.

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.

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.

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.

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.

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. Annals of Applied Statistics, 5: 232-253. doi:10.1214/10-AOAS388

Author

Patrick Breheny

Examples

data(Heart)

fit <- ncvreg(Heart$X, Heart$y, family="binomial")
coef(fit, lambda=0.05)
#>  (Intercept)          sbp      tobacco          ldl    adiposity      famhist 
#> -4.079298688  0.000000000  0.037271649  0.075045442  0.000000000  0.611522063 
#>        typea      obesity      alcohol          age 
#>  0.009798506  0.000000000  0.000000000  0.047479496 
head(predict(fit, Heart$X, type="link", lambda=0.05))
#> [1]  0.358554123 -0.217849509 -0.510057641  0.546336589  0.216182502
#> [6] -0.007063715
head(predict(fit, Heart$X, type="response", lambda=0.05))
#> [1] 0.5886904 0.4457520 0.3751800 0.6332852 0.5538361 0.4982341
head(predict(fit, Heart$X, type="class", lambda=0.05))
#> [1] 1 0 0 1 1 0
predict(fit, type="vars", lambda=c(0.05, 0.01))
#> $`0.0500`
#> tobacco     ldl famhist   typea     age 
#>       2       3       5       6       9 
#> 
#> $`0.0100`
#>     sbp tobacco     ldl famhist   typea obesity     age 
#>       1       2       3       5       6       7       9 
#> 
predict(fit, type="nvars", lambda=c(0.05, 0.01))
#> 0.0500 0.0100 
#>      5      7