Produces a summary of a fitted SLOPE model, including information about the regularization path, model family, and fitted values.
Usage
# S3 method for class 'SLOPE'
summary(object, ...)Arguments
- object
an object of class
'SLOPE', typically from a call toSLOPE()- ...
other arguments (currently ignored)
Value
An object of class 'summary_SLOPE' with the following components:
- call
the call that produced the model
- family
the model family
- n_obs
number of observations
- n_predictors
number of predictors
- has_intercept
whether an intercept was fit
- path_length
number of steps in the regularization path
- alpha_range
range of alpha values in the path
- deviance_ratio_range
range of deviance ratios in the path
- null_deviance
null deviance
- path_summary
data frame summarizing the regularization path
See also
SLOPE(), print.summary_SLOPE()
Other SLOPE-methods:
coef.SLOPE(),
deviance.SLOPE(),
plot.SLOPE(),
predict.SLOPE(),
print.SLOPE(),
score()
Examples
fit <- SLOPE(heart$x, heart$y)
summary(fit)
#>
#> Call:
#> SLOPE(x = heart$x, y = heart$y)
#>
#> Family: gaussian
#> Observations: 270
#> Predictors: 18
#> Intercept: Yes
#>
#> Regularization path:
#> Length: 64 steps
#> Alpha range: 0.000257 to 0.0902
#> Deviance ratio range: 0 to 0.563
#> Null deviance: 0.247
#>
#> Path summary (first and last 5 steps):
#> alpha deviance_ratio n_nonzero
#> 0.090200 0.0000 0
#> 0.082200 0.0802 5
#> 0.074900 0.1480 5
#> 0.068200 0.2050 5
#> 0.062200 0.2520 6
#> . . .
#> 0.000373 0.5630 18
#> 0.000340 0.5630 18
#> 0.000309 0.5630 18
#> 0.000282 0.5630 18
#> 0.000257 0.5630 18
# Multinomial example
fit_multi <- SLOPE(wine$x, wine$y, family = "multinomial")
summary(fit_multi)
#>
#> Call:
#> SLOPE(x = wine$x, y = wine$y, family = "multinomial")
#>
#> Family: multinomial
#> Observations: 178
#> Predictors: 13
#> Intercept: Yes
#>
#> Regularization path:
#> Length: 88 steps
#> Alpha range: 4.11e-05 to 0.135
#> Deviance ratio range: 0 to 0.999
#> Null deviance: 2.17
#>
#> Path summary (first and last 5 steps):
#> alpha deviance_ratio n_nonzero
#> 0.1350000 0.0000 0
#> 0.1230000 0.0818 4
#> 0.1120000 0.1590 4
#> 0.1020000 0.2240 4
#> 0.0928000 0.2820 4
#> . . .
#> 0.0000597 0.9990 19
#> 0.0000544 0.9990 19
#> 0.0000495 0.9990 19
#> 0.0000451 0.9990 19
#> 0.0000411 0.9990 19