Description Usage Arguments Details Value Author(s) Examples
Interfaces to ipred
functions that can be used
in a pipeline implemented by magrittr
.
1 2 3 4 5 | ntbt_bagging(data, ...)
ntbt_errorest(data, ...)
ntbt_inbagg(data, ...)
ntbt_inclass(data, ...)
ntbt_slda(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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | ## Not run:
library(intubate)
library(magrittr)
library(ipred)
## ntbt_bagging: Bagging Classification, Regression and Survival Trees
data("BreastCancer", package = "mlbench")
## Original function to interface
bagging(Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + Epith.c.size
+ Bare.nuclei + Bl.cromatin + Normal.nucleoli + Mitoses, data=BreastCancer, coob=TRUE)
## The interface puts data as first parameter
ntbt_bagging(BreastCancer,
Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + Epith.c.size
+ Bare.nuclei + Bl.cromatin + Normal.nucleoli + Mitoses, coob=TRUE)
## so it can be used easily in a pipeline.
BreastCancer %>%
ntbt_bagging(Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + Epith.c.size
+ Bare.nuclei + Bl.cromatin + Normal.nucleoli + Mitoses, coob=TRUE)
## ntbt_errorest: Estimators of Prediction Error
data("iris")
library("MASS")
mypredict.lda <- function(object, newdata)
predict(object, newdata = newdata)$class
## Original function to interface
errorest(Species ~ ., data = iris, model = lda, estimator = "cv", predict = mypredict.lda)
## The interface puts data as first parameter
ntbt_errorest(iris, Species ~ ., model = lda, estimator = "cv", predict = mypredict.lda)
## so it can be used easily in a pipeline.
iris %>%
ntbt_errorest(Species ~ ., model = lda, estimator = "cv", predict = mypredict.lda)
## ntbt_inbagg: Indirect Bagging
library("MASS")
library("rpart")
y <- as.factor(sample(1:2, 100, replace = TRUE))
W <- mvrnorm(n = 200, mu = rep(0, 3), Sigma = diag(3))
X <- mvrnorm(n = 200, mu = rep(2, 3), Sigma = diag(3))
colnames(W) <- c("w1", "w2", "w3")
colnames(X) <- c("x1", "x2", "x3")
DATA <- data.frame(y, W, X)
pFUN <- list(list(formula = w1~x1+x2, model = lm, predict = mypredict.lm),
list(model = rpart))
## Original function to interface
inbagg(y ~ w1 + w2 + w3 ~ x1 + x2 + x3, data = DATA, pFUN = pFUN)
## The interface puts data as first parameter
ntbt_inbagg(DATA, y ~ w1 + w2 + w3 ~ x1 + x2 + x3, pFUN = pFUN)
## so it can be used easily in a pipeline.
DATA %>%
ntbt_inbagg(y ~ w1 + w2 + w3 ~ x1 + x2 + x3, pFUN = pFUN)
## ntbt_inclass: Indirect Classification
data("Smoking", package = "ipred")
# Set three groups of variables:
# 1) explanatory variables are: TarY, NicY, COY, Sex, Age
# 2) intermediate variables are: TVPS, BPNL, COHB
# 3) response (resp) is defined by:
classify <- function(data) {
data <- data[,c("TVPS", "BPNL", "COHB")]
res <- t(t(data) > c(4438, 232.5, 58))
res <- as.factor(ifelse(apply(res, 1, sum) > 2, 1, 0))
res
}
response <- classify(Smoking[ ,c("TVPS", "BPNL", "COHB")])
smoking <- data.frame(Smoking, response)
## Original function to interface
inclass(response ~ TVPS + BPNL + COHB ~ TarY + NicY + COY + Sex + Age, data = smoking,
pFUN = list(list(model = lm, predict = mypredict.lm)), cFUN = classify)
## The interface puts data as first parameter
ntbt_inclass(smoking, response ~ TVPS + BPNL + COHB ~ TarY + NicY + COY + Sex + Age,
pFUN = list(list(model = lm, predict = mypredict.lm)), cFUN = classify)
## so it can be used easily in a pipeline.
smoking %>%
ntbt_inclass(response ~ TVPS + BPNL + COHB ~ TarY + NicY + COY + Sex + Age,
pFUN = list(list(model = lm, predict = mypredict.lm)), cFUN = classify)
## ntbt_slda: Stabilised Linear Discriminant Analysis
library("mlbench")
library("MASS")
learn <- as.data.frame(mlbench.twonorm(100))
## Original function to interface
slda(classes ~ ., data=learn)
## The interface puts data as first parameter
ntbt_slda(learn, classes ~ .)
## so it can be used easily in a pipeline.
learn %>%
ntbt_slda(classes ~ .)
## End(Not run)
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