Description Usage Arguments Details Value Author(s) Examples
Interfaces to klaR
functions that can be used
in a pipeline implemented by magrittr
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ntbt_classscatter(data, ...)
ntbt_cond.index(data, ...)
ntbt_greedy.wilks(data, ...)
ntbt_loclda(data, ...)
ntbt_meclight(data, ...)
ntbt_NaiveBayes(data, ...)
ntbt_nm(data, ...)
ntbt_partimat(data, ...)
ntbt_plineplot(data, ...)
ntbt_pvs(data, ...)
ntbt_rda(data, ...)
ntbt_sknn(data, ...)
ntbt_stepclass(data, ...)
ntbt_woe(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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | ## Not run:
library(intubate)
library(magrittr)
library(klaR)
## ntbt_classscatter: Classification scatterplot matrix
data(B3)
library(MASS)
## Original function to interface
classscatter(PHASEN ~ BSP91JW + EWAJW + LSTKJW, data = B3, method = "lda")
## The interface puts data as first parameter
ntbt_classscatter(B3, PHASEN ~ BSP91JW + EWAJW + LSTKJW, method = "lda")
## so it can be used easily in a pipeline.
B3 %>%
ntbt_classscatter(PHASEN ~ BSP91JW + EWAJW + LSTKJW, method = "lda")
## ntbt_cond.index: Calculation of Condition Indices for Linear Regression
data(Boston)
## Original function to interface
cond.index(medv ~ ., data = Boston)
## The interface puts data as first parameter
ntbt_cond.index(Boston, medv ~ .)
## so it can be used easily in a pipeline.
Boston %>%
ntbt_cond.index(medv ~ .)
## ntbt_greedy.wilks: Stepwise forward variable selection for classification
data(B3)
## Original function to interface
greedy.wilks(PHASEN ~ ., data = B3, niveau = 0.1)
## The interface puts data as first parameter
ntbt_greedy.wilks(B3, PHASEN ~ ., niveau = 0.1)
## so it can be used easily in a pipeline.
B3 %>%
ntbt_greedy.wilks(PHASEN ~ ., niveau = 0.1)
## ntbt_loclda: Localized Linear Discriminant Analysis (LocLDA)
## Original function to interface
loclda(PHASEN ~ ., data = B3)
## The interface puts data as first parameter
ntbt_loclda(B3, PHASEN ~ .)
## so it can be used easily in a pipeline.
B3 %>%
ntbt_loclda(PHASEN ~ .)
## ntbt_meclight: Minimal Error Classification
data(iris)
## Original function to interface
meclight(Species ~ ., data = iris)
## The interface puts data as first parameter
ntbt_meclight(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_meclight(Species ~ .)
## ntbt_NaiveBayes: Naive Bayes Classifier
data(iris)
## Original function to interface
NaiveBayes(Species ~ ., data = iris)
## The interface puts data as first parameter
ntbt_NaiveBayes(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_NaiveBayes(Species ~ .)
## ntbt_nm: Nearest Mean Classification
## Original function to interface
nm(PHASEN ~ ., data = B3)
## The interface puts data as first parameter
ntbt_nm(B3, PHASEN ~ .)
## so it can be used easily in a pipeline.
B3 %>%
ntbt_nm(PHASEN ~ .)
## ntbt_partimat: Plotting the 2-d partitions of classification methods
## Original function to interface
partimat(Species ~ ., data = iris, method = "lda")
## The interface puts data as first parameter
ntbt_partimat(iris, Species ~ ., method = "lda")
## so it can be used easily in a pipeline.
iris %>%
ntbt_partimat(Species ~ ., method = "lda")
## ntbt_plineplot: Plotting marginal posterior class probabilities
## Original function to interface
plineplot(PHASEN ~ ., data = B3, method = "lda", x = "EWAJW", xlab = "EWAJW")
## The interface puts data as first parameter
ntbt_plineplot(B3, PHASEN ~ ., method = "lda", x = "EWAJW", xlab = "EWAJW")
## so it can be used easily in a pipeline.
B3 %>%
ntbt_plineplot(PHASEN ~ ., method = "lda", x = "EWAJW", xlab = "EWAJW")
## ntbt_pvs: Pairwise variable selection for classification
library("mlbench")
data("Satellite")
## Original function to interface
pvs(classes ~ ., Satellite[1:3218,], method="qda", vs.method="ks.test")
## The interface puts data as first parameter
ntbt_pvs(Satellite[1:3218,], classes ~ ., method="qda", vs.method="ks.test")
## so it can be used easily in a pipeline.
Satellite[1:3218,] %>%
ntbt_pvs(classes ~ ., method="qda", vs.method="ks.test")
## ntbt_rda: Regularized Discriminant Analysis (RDA)
## Original function to interface
rda(Species ~ ., data = iris, gamma = 0.05, lambda = 0.2)
## The interface puts data as first parameter
ntbt_rda(iris, Species ~ ., gamma = 0.05, lambda = 0.2)
## so it can be used easily in a pipeline.
iris %>%
ntbt_rda(Species ~ ., gamma = 0.05, lambda = 0.2)
## ntbt_sknn: Simple k nearest Neighbours
## Original function to interface
sknn(Species ~ ., data = iris)
## The interface puts data as first parameter
ntbt_sknn(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_sknn(Species ~ .)
## ntbt_stepclass: Stepwise variable selection for classification
## Original function to interface
stepclass(Species ~ ., data = iris, method = "qda",
start.vars = "Sepal.Width", criterion = "AS") # same as above
## The interface puts data as first parameter
ntbt_stepclass(iris, Species ~ ., method = "qda",
start.vars = "Sepal.Width", criterion = "AS") # same as above
## so it can be used easily in a pipeline.
iris %>%
ntbt_stepclass(Species ~ ., method = "qda",
start.vars = "Sepal.Width", criterion = "AS") # same as above
## ntbt_woe: Weights of evidence
data("GermanCredit")
set.seed(6)
train <- sample(nrow(GermanCredit), round(0.6*nrow(GermanCredit)))
## Original function to interface
woe(credit_risk ~ ., data = GermanCredit[train,], zeroadj = 0.5, applyontrain = TRUE)
## The interface puts data as first parameter
ntbt_woe(GermanCredit[train,], credit_risk ~ ., zeroadj = 0.5, applyontrain = TRUE)
## so it can be used easily in a pipeline.
GermanCredit[train,] %>%
ntbt_woe(credit_risk ~ ., zeroadj = 0.5, applyontrain = TRUE)
## End(Not run)
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