SuperLearner_tidiers | R Documentation |
This method extends tidy
to tidy the results from a
SuperLearner
fit (screening, prediction, or
both) into a summary.
## S3 method for class 'SuperLearner'
tidy(
x,
algorithm = c("prediction", "screening", "both"),
stringsAsFactors = FALSE,
...
)
x |
object of class |
algorithm |
one of "prediction" (the default), "screening", or "both" (where "both" indicates that information on both "prediction" and "screening" should be reported). |
stringsAsFactors |
Set to |
... |
passed through to internal functions handling summaries of
feature selection and/or prediction models (depending on the supplied value
for |
This method can be used to summarize information related to the screening algorithm(s), prediction algorithm(s), or both.
A data.frame
without rownames. Column names included depend
on the supplied value for algorithm
.
If algorithm
is set to "screening" or "both", the optional argument
includeAll
can be set to TRUE
to include results from the
pass-thru screening algorithm, "All"
. By default, includeAll
is set to FALSE
and these results are excluded.
data.frame
Columns in resulting data.frame
depend upon selected algorithm
:
One row per element in SL.library
. Five columns:
Coefficient estimate
Estimate of cross-validated risk.
Screening algorithm
Prediction algorithm
Logical. Is this the discrete SuperLearner?
Number of rows equal to the product of
[number columns in X
] and
[number of unique screening algorithms in SL.library
, not
including "All"
(by default)].
Three columns:
Screening algorithm
Column of X
Logical. Did the screening algorithm select
this column of X
?
Number of rows equal to the product of
[number columns in X
] and
[number of elements in SL.library
, not including any elements
where the screening algorithm was set to "All"
(by default)].
Seven columns constituting the union of the columns returned
for "prediction" and "screening" (described above).
tidy.CV.SuperLearner
# based on an example in the SuperLearner package
set.seed(1)
n <- 100
p <- 10
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
X <- data.frame(X)
Y <- rbinom(n, 1, plogis(.2*X[, 1] + .1*X[, 2] - .2*X[, 3] + .1*X[, 3]*X[, 4] - .2*abs(X[, 4])))
library(SuperLearner)
sl = SuperLearner(Y, X, family = binomial(), cvControl = list(V = 2),
SL.library = list(c("SL.mean", "screen.wgtd.corP"),
c("SL.mean", "screen.wgtd.ttest"),
c("SL.glm", "screen.wgtd.corP"),
c("SL.glm", "screen.wgtd.ttest")))
library(broom)
tidy(sl)
tidy(sl, algorithm = "screening")
tidy(sl, algorithm = "both")
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