e1071 (version 1.3-16)

predict.svm: Predict method for Support Vector Machines

Description

This function predicts values based upon a model trained by svm.

Usage

## S3 method for class 'svm':
predict(object, newdata, decision.values = FALSE, ..., na.action = na.omit)

Arguments

object

Object of class "svm", created by svm.

newdata

A matrix containing the new input data. A vector will be transformed to a n x 1 matrix.

decision.values

Logical controlling whether the decision values of all binary classifiers computed in multiclass classification shall be computed and returned.

na.action

A function to specify the action to be taken if `NA's are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes a

...

Currently not used.

Value

• A vector of predicted values (for classification: a vector of labels, for density estimation: a logical vector). If decision.value is TRUE, the vector gets a "decision.values" attribute containing a n x k matrix (n number of predicted values, k number of classes) of all k binary classifiers' decision values. The colnames of the matrix indicate the labels of the two classes.

References

• Chang, Chih-Chung and Lin, Chih-Jen: LIBSVM 2.0: Solving Different Support Vector Formulations. http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm2.ps.gz
• Chang, Chih-Chung and Lin, Chih-Jen: Libsvm: Introduction and Benchmarks http://www.csie.ntu.edu.tw/~cjlin/papers/q2.ps.gz

See Also

svm

Examples

data(iris)
attach(iris)

## classification mode
# default with factor response:
model <- svm(Species ~ ., data = iris)

# alternatively the traditional interface:
x <- subset(iris, select = -Species)
y <- Species
model <- svm(x, y) 

print(model)
summary(model)

# test with train data
pred <- predict(model, x)
# (same as:)
pred <- fitted(model)

# Check accuracy:
table(pred, y)

# compute decision values:
pred <- predict(model, x, decision.values = TRUE)
attr(pred, "decision.values")[1:4,]

## try regression mode on two dimensions

# create data
x <- seq(0.1, 5, by = 0.05)
y <- log(x) + rnorm(x, sd = 0.2)

# estimate model and predict input values
m   <- svm(x, y)
new <- predict(m, x)

# visualize
plot   (x, y)
points (x, log(x), col = 2)
points (x, new, col = 4)

## density-estimation

# create 2-dim. normal with rho=0:
X <- data.frame(a = rnorm(1000), b = rnorm(1000))
attach(X)

# traditional way:
m <- svm(X, gamma = 0.1)

# formula interface:
m <- svm(~., data = X, gamma = 0.1)
# or:
m <- svm(~ a + b, gamma = 0.1)

# test:
newdata <- data.frame(a = c(0, 4), b = c(0, 4))
predict (m, newdata)