Return predictions from models fit by `SLOPE()`

.

```
# S3 method for SLOPE
predict(object, x, alpha = NULL, type = "link", simplify = TRUE, sigma, ...)
```# S3 method for GaussianSLOPE
predict(
object,
x,
sigma = NULL,
type = c("link", "response"),
simplify = TRUE,
...
)

# S3 method for BinomialSLOPE
predict(
object,
x,
sigma = NULL,
type = c("link", "response", "class"),
simplify = TRUE,
...
)

# S3 method for PoissonSLOPE
predict(
object,
x,
sigma = NULL,
type = c("link", "response"),
exact = FALSE,
simplify = TRUE,
...
)

# S3 method for MultinomialSLOPE
predict(
object,
x,
sigma = NULL,
type = c("link", "response", "class"),
exact = FALSE,
simplify = TRUE,
...
)

object

an object of class `"SLOPE"`

, typically the result of
a call to `SLOPE()`

x

new data

alpha

penalty parameter for SLOPE models; if `NULL`

, the
values used in the original fit will be used

type

type of prediction; `"link"`

returns the linear predictors,
`"response"`

returns the result of applying the link function,
and `"class"`

returns class predictions.

simplify

if `TRUE`

, `base::drop()`

will be called before returning
the coefficients to drop extraneous dimensions

sigma

deprecated. Please use `alpha`

instead.

...

ignored and only here for method consistency

exact

if `TRUE`

and the given parameter values differ from those in
the original fit, the model will be refit by calling `stats::update()`

on
the object with the new parameters. If `FALSE`

, the predicted values
will be based on interpolated coefficients from the original
penalty path.

Predictions from the model with scale determined by `type`

.

`stats::predict()`

, `stats::predict.glm()`

, `coef.SLOPE()`

Other SLOPE-methods:
`coef.SLOPE()`

,
`deviance.SLOPE()`

,
`plot.SLOPE()`

,
`print.SLOPE()`

,
`score()`

# NOT RUN { fit <- with(mtcars, SLOPE(cbind(mpg, hp), vs, family = "binomial")) predict(fit, with(mtcars, cbind(mpg, hp)), type = "class") # }