Description Usage Arguments Value
Safe predictions for glmnet objects
1 2 3 | ## S3 method for class 'glmnet'
safe_predict(object, new_data, type = c("response",
"class", "prob", "link"), ..., penalty = NULL, threshold = 0.5)
|
object |
TODO |
new_data |
TODO |
type |
TODO |
... |
Unused. |
penalty |
Unlike |
threshold |
A number between |
A tibble::tibble()
with one row for each row of new_data
.
Predictions for observations with missing data will be NA
. Returned
tibble has different columns depending on type
:
"response"
:
univariate outcome: .pred
(numeric)
multivariate outcomes: .pred_{outcome name}
(numeric) for each
outcome
"class"
: .pred_class
(factor)
"prob"
: .pred_{level}
columns (numerics between 0 and 1)
"link"
: .pred
(numeric)
"conf_int"
: .pred
, .pred_lower
, .pred_upper
(all numeric)
"pred_int"
: .pred
, .pred_lower
, .pred_upper
(all numeric)
If you request standard errors with std_error = TRUE
, an additional
column .std_error
.
For interval predictions, the tibble has additional attributes level
and interval
. The level
is the same as the level
argument and is
between 0 and 1. interval
is either "confidence"
or "prediction"
.
Some models may also set a method
attribute to detail the method
used to calculate the intervals.
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