svm: SVM Regression and Discrimination

In mlesnoff/rchemo: Dimension reduction, Regression and Discrimination for Chemometrics

SVM Regression and Discrimination

Description

SVM models with Gaussian (RBF) kernel

svmr: SVM regression (SVMR).

svmda: SVM discrimination (SVMC).

The functions uses function svm of package e1071 (Meyer et al. 2021) available on CRAN (e1071 uses the tool box LIVSIM; Chang & Lin, http://www.csie.ntu.edu.tw/~cjlin/libsvm).

The SVM models are fitted with parameterization 'C', not the 'nu' parameterization.

The RBF kernel is defined by: exp(-gamma * |x - y|^2).

For tuning the model, usual preliminary ranges are for instance:

- cost = 10^(-5:15)

- epsilon = seq(.1, .3, by = .1)

- gamma = 10^(-6:3)

Usage

svmr(X, y, cost = 1, epsilon = .1, gamma = 1, scale = FALSE)

svmda(X, y, cost = 1, epsilon = .1, gamma = 1, scale = FALSE)

## S3 method for class 'Svm'
predict(object, X, ...)  

## S3 method for class 'Svm'
summary(object, ...)  

Arguments

X	For the main function: Training X-data (n, p). — For the auxiliary functions: New X-data (m, p) to consider.
y	Training Y-data (n).
cost	The cost of constraints violation cost parameter. See svm.
epsilon	The epsilon parameter in the insensitive-loss function. See svm.
gamma	The gamma parameter in the RBF kernel.
scale	Logical. If TRUE, X and Y are scaled internally.
object	A fitted model, output of a call to the main function.
...	Optional arguments.

Value

See the examples.

References

Meyer, M. 2021 Support Vector Machines - The Interface to libsvm in package e1071. FH Technikum Wien, Austria, [e-mail]. https://cran.r-project.org/web/packages/e1071/vignettes/svmdoc.pdf

Chang, cost.-cost. & Lin, cost.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Detailed documentation (algorithms, formulae, . . . ) can be found in http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz

Examples

#################################### SVMR

n <- 50 ; p <- 4
Xtrain <- matrix(rnorm(n * p), ncol = p)
ytrain <- rnorm(n)
m <- 3
Xtest <- Xtrain[1:m, , drop = FALSE] 
ytest <- ytrain[1:m]

fm <- svmr(Xtrain, ytrain)
predict(fm, Xtest)

pred <- predict(fm, Xtest)$pred
msep(pred, ytest)

summary(fm)

####### Example of fitting the function sinc(x)
####### described in Rosipal & Trejo 2001 p. 105-106 

x <- seq(-10, 10, by = .2)
x[x == 0] <- 1e-5
n <- length(x)
zy <- sin(abs(x)) / abs(x)
y <- zy + rnorm(n, 0, .2)
plot(x, y, type = "p")
lines(x, zy, lty = 2)
X <- matrix(x, ncol = 1)

fm <- svmr(X, y, gamma = .5)
pred <- predict(fm, X)$pred
plot(X, y, type = "p")
lines(X, zy, lty = 2)
lines(X, pred, col = "red")

#################################### SVMC

n <- 50 ; p <- 8
Xtrain <- matrix(rnorm(n * p), ncol = p)
ytrain <- sample(c(1, 4, 10), size = n, replace = TRUE)
#ytrain <- sample(c("a", "10", "d"), size = n, replace = TRUE)
#ytrain <- as.factor(sample(c(1, 4, 10), size = n, replace = TRUE))
#ytrain <- as.factor(sample(c("a", "10", "d"), size = n, replace = TRUE))
m <- 5
Xtest <- Xtrain[1:m, ] ; ytest <- ytrain[1:m]

cost <- 100 ; epsilon <- .1 ; gamma <- 1 
fm <- svmda(Xtrain, ytrain,
    cost = cost, epsilon = epsilon, gamma = gamma)
predict(fm, Xtest)

pred <- predict(fm, Xtest)$pred
err(pred, ytest)

summary(fm)

mlesnoff/rchemo documentation built on April 15, 2023, 1:25 p.m.