Description Usage Arguments Details Value Author(s) Examples
Interfaces to adabag
functions that can be used
in a pipeline implemented by magrittr
.
1 2 3 4 5 | ntbt_autoprune(data, ...)
# ntbt_bagging(data, ...) ## Already defined in ipred
ntbt_bagging.cv(data, ...)
ntbt_boosting(data, ...)
ntbt_boosting.cv(data, ...)
|
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Interfaces call their corresponding interfaced function.
Object returned by interfaced function.
Roberto Bertolusso
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ## Not run:
library(intubate)
library(magrittr)
library(adabag)
## ntbt_autoprune: Builds automatically a pruned tree of class rpart
## Original function to interface
autoprune(Species ~ ., data = iris)
## The interface puts data as first parameter
ntbt_autoprune(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_autoprune(Species ~ .)
## ntbt_bagging: Applies the Bagging algorithm to a data set
library(rpart)
data(iris)
## Original function to interface
bagging(Species ~ ., data = iris, mfinal = 10)
## The interface puts data as first parameter
ntbt_bagging(iris, Species ~ ., mfinal = 10)
## so it can be used easily in a pipeline.
iris %>%
ntbt_bagging(Species ~ ., mfinal = 10)
## Original function to interface
iris.baggingcv <- bagging.cv(Species ~ ., v = 2, data = iris, mfinal = 10,
control = rpart.control(cp = 0.01))
iris.baggingcv[-1]
## The interface puts data as first parameter
iris.baggingcv <- ntbt_bagging.cv(iris, Species ~ ., v = 2, mfinal = 10,
control = rpart.control(cp = 0.01))
iris.baggingcv[-1]
## so it can be used easily in a pipeline.
iris.baggingcv <- iris %>%
ntbt_bagging.cv(Species ~ ., v = 2, mfinal = 10,
control = rpart.control(cp = 0.01))
iris.baggingcv[-1]
## ntbt_boosting: Applies the AdaBoost.M1 and SAMME algorithms to a data set
## Original function to interface
boosting(Species ~ ., data = iris, boos = TRUE, mfinal = 5)
## The interface puts data as first parameter
ntbt_boosting(iris, Species ~ ., boos = TRUE, mfinal = 5)
## so it can be used easily in a pipeline.
iris %>%
ntbt_boosting(Species ~ ., boos = TRUE, mfinal = 5)
## ntbt_boosting.cv: Runs v-fold cross validation with AdaBoost.M1 or SAMME
## Original function to interface
iris.boostcv <- boosting.cv(Species ~ ., v = 2, data = iris, mfinal = 10,
control = rpart.control(cp = 0.01))
iris.boostcv[-1]
## The interface puts data as first parameter
iris.boostcv <- ntbt_boosting.cv(iris, Species ~ ., v = 2, mfinal = 10,
control = rpart.control(cp = 0.01))
iris.boostcv[-1]
## so it can be used easily in a pipeline.
iris.boostcv <- iris %>%
ntbt_boosting.cv(Species ~ ., v = 2, mfinal = 10,
control = rpart.control(cp = 0.01))
iris.boostcv[-1]
## End(Not run)
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