Description Usage Arguments Details Value Author(s) Examples
Interfaces to kernlab
functions that can be used
in a pipeline implemented by magrittr
.
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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
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library(intubate)
library(magrittr)
library(kernlab)
## ntbt_gausspr: Gaussian processes for regression and classification
data(iris)
## Original function to interface
gausspr(Species ~ ., data = iris, var = 2)
## The interface puts data as first parameter
ntbt_gausspr(iris, Species ~ ., var = 2)
## so it can be used easily in a pipeline.
iris %>%
ntbt_gausspr(Species ~ ., var = 2)
## ntbt_kfa: Kernel Feature Analysis
data(promotergene)
## Original function to interface
kfa(~ ., data = promotergene)
## The interface puts data as first parameter
ntbt_kfa(promotergene, ~ .)
## so it can be used easily in a pipeline.
promotergene %>%
ntbt_kfa(~ .)
## ntbt_kha: Kernel Principal Components Analysis
data(iris)
test <- sample(1:150,70)
## Original function to interface
kpc <- kha(~ ., data = iris[-test, -5], kernel = "rbfdot", kpar = list(sigma=0.2),
features = 2, eta = 0.001, maxiter = 65)
pcv(kpc)
## The interface puts data as first parameter
kpc <- ntbt_kha(iris[-test, -5], ~ ., kernel = "rbfdot", kpar = list(sigma=0.2),
features = 2, eta = 0.001, maxiter = 65)
pcv(kpc)
## so it can be used easily in a pipeline.
iris[-test, -5] %>%
ntbt_kha(~ ., kernel = "rbfdot", kpar = list(sigma=0.2),
features = 2, eta = 0.001, maxiter = 65) %>%
pcv()
## ntbt_kkmeans: Kernel k-means
## Original function to interface
sc <- kkmeans(~ ., data = iris[-test, -5], centers = 3)
centers(sc)
## The interface puts data as first parameter
sc <- ntbt_kkmeans(iris[-test, -5], ~ ., centers = 3)
centers(sc)
## so it can be used easily in a pipeline.
iris[-test, -5] %>%
ntbt_kkmeans(~ ., centers = 3) %>%
centers()
## ntbt_kpca: Kernel Principal Components Analysis
data(iris)
test <- sample(1:150,20)
## Original function to interface
kpc <- kpca(~ ., data = iris[-test, -5], kernel = "rbfdot",
kpar = list(sigma = 0.2), features = 2)
pcv(kpc)
## The interface puts data as first parameter
kpc <- ntbt_kpca(iris[-test, -5], ~ ., kernel = "rbfdot",
kpar = list(sigma = 0.2), features = 2)
pcv(kpc)
## so it can be used easily in a pipeline.
iris[-test, -5] %>%
ntbt_kpca(~ ., kernel = "rbfdot",
kpar = list(sigma = 0.2), features = 2) %>%
pcv()
## ntbt_kqr: Kernel Quantile Regression
## Not found example using formula interface, and I am
## completely ignorant to construct one.
x <- sort(runif(300))
y <- sin(pi*x) + rnorm(300,0,sd=exp(sin(2*pi*x)))
dkqr <- data.frame(x, y)
## Original function to interface
set.seed(1)
kqr(x, y, tau = 0.5, C = 0.15)
## The interface puts data as first parameter
set.seed(1)
ntbt_kqr(dkqr, x, y, tau = 0.5, C = 0.15)
## so it can be used easily in a pipeline.
set.seed(1)
dkqr %>%
ntbt_kqr(x, y, tau = 0.5, C = 0.15)
## ntbt_ksvm: Support Vector Machines
data(spam)
index <- sample(1:dim(spam)[1])
spamtrain <- spam[index[1:floor(dim(spam)[1]/2)], ]
spamtest <- spam[index[((ceiling(dim(spam)[1]/2)) + 1):dim(spam)[1]], ]
## Original function to interface
set.seed(1)
ksvm(type ~ ., data = spamtrain, kernel = "rbfdot",
kpar = list(sigma = 0.05), C = 5, cross = 3)
## The interface puts data as first parameter
set.seed(1)
ntbt_ksvm(spamtrain, type ~ ., kernel = "rbfdot",
kpar = list(sigma = 0.05), C = 5, cross = 3)
## so it can be used easily in a pipeline.
set.seed(1)
spamtrain %>%
ntbt_ksvm(type ~ ., kernel = "rbfdot",
kpar = list(sigma = 0.05), C = 5, cross = 3)
## ntbt_lssvm: Least Squares Support Vector Machine
data(iris)
## Original function to interface
set.seed(1)
lssvm(Species ~ ., data = iris)
## The interface puts data as first parameter
set.seed(1)
ntbt_lssvm(iris, Species ~ .)
## so it can be used easily in a pipeline.
set.seed(1)
iris %>%
ntbt_lssvm(Species ~ .)
## ntbt_rvm: Relevance Vector Machine
## Not found example using formula interface, and I am
## completely ignorant to construct one.
x <- seq(-20,20,0.1)
y <- sin(x)/x + rnorm(401,sd=0.05)
drvm <- data.frame(x, y)
## Original function to interface
set.seed(1)
rvm(x, y, tau = 0.5, C = 0.15)
## The interface puts data as first parameter
set.seed(1)
ntbt_rvm(drvm, x, y, tau = 0.5, C = 0.15)
## so it can be used easily in a pipeline.
set.seed(1)
drvm %>%
ntbt_rvm(x, y, tau = 0.5, C = 0.15)
## ntbt_sigest: Hyperparameter estimation for the Gaussian Radial Basis kernel
data(promotergene)
## Original function to interface
set.seed(1)
sigest(Class ~ ., data = promotergene)
## The interface puts data as first parameter
set.seed(1)
ntbt_sigest(promotergene, Class ~ .)
## so it can be used easily in a pipeline.
set.seed(1)
promotergene %>%
ntbt_sigest(Class ~ .)
## ntbt_specc: Spectral Clustering
## Not found example using formula interface, and I am
## completely ignorant to construct one.
## End(Not run)
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