make predictions from a

This function makes predictions from a cross-validated glmnet model, using the stored "glmnet.fit" object, and the optimal value chosen for lambda (and gamma for a 'relaxed' fit.

Usage

# S3 method for class 'cv.glmnet'
predict(object, newx, s = c("lambda.1se", "lambda.min"), ...)

# S3 method for class 'cv.relaxed'
predict(
  object,
  newx,
  s = c("lambda.1se", "lambda.min"),
  gamma = c("gamma.1se", "gamma.min"),
  ...
)

Arguments

object: Fitted "cv.glmnet" or "cv.relaxed" object.
newx: Matrix of new values for x at which predictions are to be made. Must be a matrix; can be sparse as in Matrix package. See documentation for predict.glmnet.
s: Value(s) of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.1se" stored on the CV object. Alternatively s="lambda.min" can be used. If s is numeric, it is taken as the value(s) of lambda to be used. (For historical reasons we use the symbol 's' rather than 'lambda' to reference this parameter)
...: Not used. Other arguments to predict.
gamma: Value (single) of 'gamma' at which predictions are to be made

Value

The object returned depends on the ... argument which is passed on to the predict method for glmnet objects.

Details

This function makes it easier to use the results of cross-validation to make a prediction.

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent (2010), Journal of Statistical Software, Vol. 33(1), 1-22, doi:10.18637/jss.v033.i01 .
Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, doi:10.18637/jss.v039.i05 .
Hastie, T., Tibshirani, Robert and Tibshirani, Ryan (2020) Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons, Statist. Sc. Vol. 35(4), 579-592, https://arxiv.org/abs/1707.08692.
Glmnet webpage with four vignettes, https://glmnet.stanford.edu.

Author

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie [email protected]

Examples


x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
cv.fit = cv.glmnet(x, y)
predict(cv.fit, newx = x[1:5, ])
#>      lambda.1se
#> [1,] 0.07501387
#> [2,] 0.07501387
#> [3,] 0.07501387
#> [4,] 0.07501387
#> [5,] 0.07501387
coef(cv.fit)
#> 21 x 1 sparse Matrix of class "dgCMatrix"
#>             lambda.1se
#> (Intercept) 0.07501387
#> V1          .         
#> V2          .         
#> V3          .         
#> V4          .         
#> V5          .         
#> V6          .         
#> V7          .         
#> V8          .         
#> V9          .         
#> V10         .         
#> V11         .         
#> V12         .         
#> V13         .         
#> V14         .         
#> V15         .         
#> V16         .         
#> V17         .         
#> V18         .         
#> V19         .         
#> V20         .         
coef(cv.fit, s = "lambda.min")
#> 21 x 1 sparse Matrix of class "dgCMatrix"
#>              lambda.min
#> (Intercept)  0.06451924
#> V1          -0.03536507
#> V2           .         
#> V3           0.05242244
#> V4           .         
#> V5           .         
#> V6           .         
#> V7           0.06397749
#> V8           .         
#> V9           .         
#> V10          .         
#> V11          .         
#> V12          .         
#> V13          .         
#> V14          .         
#> V15          .         
#> V16         -0.02403396
#> V17          .         
#> V18          .         
#> V19          .         
#> V20          .         
predict(cv.fit, newx = x[1:5, ], s = c(0.001, 0.002))
#>          s=0.001     s=0.002
#> [1,]  0.18365095  0.18549497
#> [2,] -0.29444884 -0.28133067
#> [3,] -0.07069425 -0.07378082
#> [4,]  0.28268286  0.28912207
#> [5,]  0.32817600  0.32201042
cv.fitr = cv.glmnet(x, y, relax = TRUE)
predict(cv.fit, newx = x[1:5, ])
#>      lambda.1se
#> [1,] 0.07501387
#> [2,] 0.07501387
#> [3,] 0.07501387
#> [4,] 0.07501387
#> [5,] 0.07501387
coef(cv.fit)
#> 21 x 1 sparse Matrix of class "dgCMatrix"
#>             lambda.1se
#> (Intercept) 0.07501387
#> V1          .         
#> V2          .         
#> V3          .         
#> V4          .         
#> V5          .         
#> V6          .         
#> V7          .         
#> V8          .         
#> V9          .         
#> V10         .         
#> V11         .         
#> V12         .         
#> V13         .         
#> V14         .         
#> V15         .         
#> V16         .         
#> V17         .         
#> V18         .         
#> V19         .         
#> V20         .         
coef(cv.fit, s = "lambda.min", gamma = "gamma.min")
#> 21 x 1 sparse Matrix of class "dgCMatrix"
#>              lambda.min
#> (Intercept)  0.06451924
#> V1          -0.03536507
#> V2           .         
#> V3           0.05242244
#> V4           .         
#> V5           .         
#> V6           .         
#> V7           0.06397749
#> V8           .         
#> V9           .         
#> V10          .         
#> V11          .         
#> V12          .         
#> V13          .         
#> V14          .         
#> V15          .         
#> V16         -0.02403396
#> V17          .         
#> V18          .         
#> V19          .         
#> V20          .         
predict(cv.fit, newx = x[1:5, ], s = c(0.001, 0.002), gamma = "gamma.min")
#>          s=0.001     s=0.002
#> [1,]  0.18365095  0.18549497
#> [2,] -0.29444884 -0.28133067
#> [3,] -0.07069425 -0.07378082
#> [4,]  0.28268286  0.28912207
#> [5,]  0.32817600  0.32201042

make predictions from a "cv.glmnet" object.