View source: R/get_what-methods.R
get_what.lasso_screenr | R Documentation |
lasso_screenr
Objectsget_what.lasso_screenr
extracts components from lasso_screenr
-class
objects.
## S3 method for class 'lasso_screenr' get_what( from = NULL, what = c("glmpathObj", "ROCci", "cvROC", "isROC"), ..., model = c("minAIC", "minBIC"), conf_level = 0.95, bootreps = 4000, se_min = 0.8 )
from |
the |
what |
the character-valued name of the component to extract. Valid values are
|
... |
optional arguments to |
model |
the character-valued name of the model for which the component is
desired. Valid values are |
conf_level |
confidence level for |
bootreps |
the number of bootstrap replications for estimation of
confidence intervals for |
se_min |
minimum value of sensitivity printed for
|
get_what
is provided to enable easy extraction of components that are
not provided by the coef
, plot
, predict
, print
or summary
methods.
The following values of what
return:
"glmpathObj"
the entire glmpath
-class object produced by
by glmpath
.
ROCci
a data frame containing cross-validated sensitivities, specificities and their confidence limits, and thresholds
.
"cvROC"
the roc
-class object produced by roc
containing the k-fold cross-validated receiver-operating characteristic.
"isROC"
the roc
-class object produced by roc
containing the in-sample (overly optimistic) receiver-operating characteristic.
get_what.lasso_screenr
returns (invisibly) the object specified
by what
.
## Not run: attach(uniobj1) ## Plot the coefficient paths pathobj <- get_what(from = uniobj1, what = "glmpathObj", model = "minAIC") plot(pathobj, xvar = "lambda") ## Get and print cross-validated sensitivities and specificities at ## thresholds for the local maxima of the ROC curve cvROCci <- get_what(from = uniobj1, what = "ROCci", model = "minBIC") print(cvROCci) ## End(Not run)
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