Skip to contents

Model predictions based on a fitted grpreg object

Source: R/predict-cv.R, R/predict.R
predict.grpreg.Rd

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

Usage

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

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

# S3 method for class 'grpreg'
predict(
  object,
  X,
  type = c("link", "response", "class", "coefficients", "vars", "groups", "nvars",
    "ngroups", "norm"),
  lambda,
  which = 1:length(object$lambda),
  ...
)

# S3 method for class 'grpreg'
coef(object, lambda, which = 1:length(object$lambda), drop = TRUE, ...)

Arguments

object

Fitted "grpreg" or "cv.grpreg" model object.

X

Matrix of values at which predictions are to be made. Not used for type="coefficients"

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 the indices for the nonzero coefficients; "groups" returns the indices for the groups with at least one nonzero coefficient; "nvars" returns the number of nonzero coefficients; "ngroups" returns the number of groups with at least one nonzero coefficient; "norm" returns the L2 norm of the coefficients in each group.

...

Not used.

drop

By default, if a single value of lambda is supplied, a vector of coefficients is returned. Set drop=FALSE if you wish to have coef always return a matrix (see drop).

Value

The object returned depends on type.

Details

coef and predict methods are provided for "cv.grpreg" options as a convenience. They simply call coef.grpreg and predict.grpreg with lambda set to the value that minimizes the cross-validation error.

See also

grpreg

Author

Patrick Breheny

Examples

# Fit penalized logistic regression model to birthweight data
data(Birthwt)
X <- Birthwt$X
y <- Birthwt$low
group <- Birthwt$group
fit <- grpreg(X, y, group, penalty="grLasso", family="binomial")

# Coef and predict methods
coef(fit, lambda=.001)
#> (Intercept)        age1        age2        age3        lwt1        lwt2 
#>  -1.5594452 -11.3887695 -18.3241083 -13.5534407  -6.7400536  -2.0424956 
#>        lwt3       white       black       smoke        ptl1       ptl2m 
#>  -4.4466007  -0.6955048   0.5156693   0.8198631   1.6532574  -0.2816916 
#>          ht          ui        ftv1        ftv2       ftv3m 
#>   2.0050588   0.7726613  -0.3866688  -0.1593881   0.6674573 
predict(fit, X, type="link", lambda=.07)[1:10]
#>  [1] -0.8193011 -0.8702524 -0.8639390 -0.8129877 -0.8129877 -0.8702524
#>  [7] -0.8702524 -0.8702524 -0.8639390 -0.8639390
predict(fit, X, type="response", lambda=.07)[1:10]
#>  [1] 0.3059120 0.2952018 0.2965170 0.3072542 0.3072542 0.2952018 0.2952018
#>  [8] 0.2952018 0.2965170 0.2965170
predict(fit, X, type="class", lambda=.01)[1:15]
#>  [1] 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
predict(fit, type="vars", lambda=.07)
#> smoke  ptl1 ptl2m    ht    ui 
#>     9    10    11    12    13 
predict(fit, type="groups", lambda=.07)
#> [1] smoke ptl   ht    ui   
#> Levels: age lwt race smoke ptl ht ui ftv
predict(fit, type="norm", lambda=.07)
#>         age         lwt        race       smoke         ptl          ht 
#> 0.000000000 0.000000000 0.000000000 0.006313417 0.487194445 0.032052407 
#>          ui         ftv 
#> 0.050951339 0.000000000 

# Coef and predict methods for cross-validation
cvfit <- cv.grpreg(X, y, group, family="binomial", penalty="grMCP")
coef(cvfit)
#> (Intercept)        age1        age2        age3        lwt1        lwt2 
#> -0.99567389  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000 
#>        lwt3       white       black       smoke        ptl1       ptl2m 
#>  0.00000000  0.00000000  0.00000000  0.02697791  0.94778608  0.14979714 
#>          ht          ui        ftv1        ftv2       ftv3m 
#>  0.30980476  0.22330875  0.00000000  0.00000000  0.00000000 
predict(cvfit, X)[1:10]
#>  [1] -0.7723651 -0.9956739 -0.9686960 -0.7453872 -0.7453872 -0.9956739
#>  [7] -0.9956739 -0.9956739 -0.9686960 -0.9686960
predict(cvfit, X, type="response")[1:10]
#>  [1] 0.3159677 0.2697928 0.2751405 0.3218272 0.3218272 0.2697928 0.2697928
#>  [8] 0.2697928 0.2751405 0.2751405
predict(cvfit, type="groups")
#> [1] smoke ptl   ht    ui   
#> Levels: age lwt race smoke ptl ht ui ftv