Description Usage Arguments Details Value Author(s) Examples
Interfaces to party
functions that can be used
in a pipeline implemented by magrittr
.
1 2 3 | ntbt_cforest(data, ...)
ntbt_ctree(data, ...)
ntbt_mob(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 | ## Not run:
library(intubate)
library(magrittr)
library(party)
## ntbt_cforest: Random Forest
### honest (i.e., out-of-bag) cross-classification of
### true vs. predicted classes
data("mammoexp", package = "TH.data")
#table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp,
# control = cforest_unbiased(ntree = 50)),
# OOB = TRUE))
## Original function to interface
set.seed(290875)
cforest(ME ~ ., data = mammoexp, control = cforest_unbiased(ntree = 50))
## The interface puts data as first parameter
set.seed(290875)
ntbt_cforest(mammoexp, ME ~ ., control = cforest_unbiased(ntree = 50))
## so it can be used easily in a pipeline.
set.seed(290875)
mammoexp %>%
ntbt_cforest(ME ~ ., control = cforest_unbiased(ntree = 50))
## ntbt_ctree: Conditional Inference Trees
airq <- subset(airquality, !is.na(Ozone))
## Original function to interface
set.seed(290875)
ctree(Ozone ~ ., data = airq, controls = ctree_control(maxsurrogate = 3))
## The interface puts data as first parameter
set.seed(290875)
ntbt_ctree(airq, Ozone ~ ., controls = ctree_control(maxsurrogate = 3))
## so it can be used easily in a pipeline.
set.seed(290875)
airq %>%
ntbt_ctree(Ozone ~ ., controls = ctree_control(maxsurrogate = 3))
## ntbt_mob: Model-based Recursive Partitioning
data("BostonHousing", package = "mlbench")
## and transform variables appropriately (for a linear regression)
BostonHousing$lstat <- log(BostonHousing$lstat)
BostonHousing$rm <- BostonHousing$rm^2
## as well as partitioning variables (for fluctuation testing)
BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1,
labels = c("no", "yes"))
BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE)
## Original function to interface
set.seed(290875)
mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,
control = mob_control(minsplit = 40), data = BostonHousing,
model = linearModel)
## The interface puts data as first parameter
set.seed(290875)
ntbt_mob(BostonHousing,
medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,
control = mob_control(minsplit = 40), model = linearModel)
## so it can be used easily in a pipeline.
set.seed(290875)
BostonHousing %>%
ntbt_mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,
control = mob_control(minsplit = 40), model = linearModel)
## End(Not run)
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