e1071 (version 1.5-20)

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,
probability = 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.

probability

Logical indicating whether class probabilities should be computed and returned. Only possible if the model was fitted with the probability option enabled.

na.action

A function to specify the action to be taken if NAs 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,

...

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 c matrix (n number of predicted values, c number of classifiers) of all c binary classifiers' decision values. There are k * (k - 1) / 2 classifiers (k number of classes). The colnames of the matrix indicate the labels of the two classes. If probability is TRUE, the vector gets a "probabilities" attribute containing a n x k matrix (n number of predicted values, k number of classes) of the class probabilities.

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, probability = TRUE) 

print(model)
summary(model)

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

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

# visualize:
plot(X, col = 1:1000 %in% m$index + 1, xlim = c(-5,5), ylim=c(-5,5))
points(newdata, pch = "+", col = 2, cex = 5)