Function to obtain the naive subgroup treatment effects of an object fitted
with the elastic_net function.
Usage
# S3 method for elastic_net
summary(object, gamma = 1, l = NULL, lambda = NULL, ...)Arguments
- object
(
elastic_net)
the elastic_net object.- gamma
(
scalar)
numeric value defining the weights to obtain the average hazard ratio. Default is 1 (in this case the average hazard ratio obtained can be interpreted as the odds of concordance). Just needed when using survival data.- l
(
scalar)
the maximum value of time that wants to be studied to obtain the average hazard ratio. Default is the maximum value of time when there was an event. Just needed when using survival data.- lambda
(
scalar)
the penalization constant in the elastic net. Default is the value that leads to minimal cross validation error.- ...
Arguments of summary
Value
Object of class summary.elastic_net which is a list with the
estimated subgroup treatment effects, the resptype, the confidence level
and the value of alpha.
Examples
summary(elastic_net_fit_surv)
#> subgroup trt.estimate
#> 1 x_1a 0.6500483
#> 2 x_1b 0.6493824
#> 3 x_2a 0.6493749
#> 4 x_2b 0.6501893
#> 5 x_3a 0.6497598
#> 6 x_3b 0.6489794
#> 7 x_4a 0.6474298
#> 8 x_4b 0.6467309
#> 9 x_4c 0.6467375
#> 10 x_5a 0.6497693
#> 11 x_5b 0.6495787
#> 12 x_5c 0.6493530
#> 13 x_5d 0.6489952
#> 14 x_6a 0.6485965
#> 15 x_6b 0.6494347
#> 16 x_7a 0.6489064
#> 17 x_7b 0.6498076
#> 18 x_8a 0.6496933
#> 19 x_8b 0.6495582
#> 20 x_8c 0.6499395
#> 21 x_9a 0.6487417
#> 22 x_9b 0.6497883
#> 23 x_10a 0.6498012
#> 24 x_10b 0.6495769
#> 25 x_10c 0.6492702