| predict.svm {e1071} | R Documentation |
This function predicts values based upon a model trained by svm.
## S3 method for class 'svm': predict(object, newdata, decision.values = FALSE, probability = FALSE, ..., na.action = na.omit)
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 ‘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 an error if NA cases
are found. (NOTE: If given, this argument must be named.) |
... |
Currently not used. |
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.
If the training set was scaled by svm (done by default), the
new data is scaled accordingly using scale and center of
the training data.
David Meyer (based on C++-code by Chih-Chung Chang and Chih-Jen Lin)
David.Meyer@R-project.org
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)