predict.svm {e1071}R Documentation

Predict method for Support Vector Machines

Description

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

Usage

predict(object, newdata, ...)

Arguments

object object of class "svm", created by svm.
newdata a matrix containing the new input data.
... currently not used.

Value

The predicted value (for classification: the label, for density estimation: TRUE or FALSE).

Author(s)

David Meyer (based on C++-code by Chih-Chung Chang and Chih-Jen Lin)
david.meyer@ci.tuwien.ac.at

References

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)

# Check accuracy:
table (pred,y)

## 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)

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

# visualization:
plot (X)
points (X[m$index,], col=2)

[Package Contents]