predict.svm {e1071} | R Documentation |
This function predicts values based upon a model trained by svm
.
predict.svm(model, x, type="class")
model |
object of class svm , created by svm . |
x |
A matrix containing the input data. |
type |
If raw is supplied, the estimated values are returned.
With class , predict returns the first level of the
training response for negative (non-zero) values, and the second
level otherwise. If class is used on numerical data, a factor
with labels "-1" and "1" is created. |
The predicted value.
David Meyer (based on C++-code by Chih-Chung Chang and Chih-Jen Lin)
david.meyer@ci.tuwien.ac.at
data(iris) # amputate data to two factors iris.sub <- subset(iris, Species != "virginica") # get independent vars x <- subset (iris.sub, select = -Species) # get responses y <- iris.sub[,"Species"] # coercion needed for correct factor levels y <- as.factor(as.character(y)) # default with factor response: classification mode model <- svm (x, y) print (model) summary (model) # test with train data pred <- predict (model, x) # should be TRUE: all.equal (pred, y) # try regression mode on two dimensions in linear mode model <- svm (x[,"Petal.Length"], x[,"Petal.Width"], svm.type="regression", kernel.type="linear") print (model) pred <- predict (model,x[,"Petal.Length"]) par (mfcol=c(1,2)) plot(x[,"Petal.Length"],x[,"Petal.Width"]) plot(x[,"Petal.Length"],pred)