# train model
data(iris)
test <- gausspr(Species~.,data=iris,var=2)
test
alpha(test)
# predict on the training set
predict(test,iris[,-5])
# class probabilities
predict(test, iris[,-5], type="probabilities")
# create regression data
x <- seq(-20,20,0.1)
y <- sin(x)/x + rnorm(401,sd=0.03)
# regression with gaussian processes
foo <- gausspr(x, y)
foo
# predict and plot
ytest <- predict(foo, x)
plot(x, y, type ="l")
lines(x, ytest, col="red")
#predict and variance
x = c(-4, -3, -2, -1, 0, 0.5, 1, 2)
y = c(-2, 0, -0.5,1, 2, 1, 0, -1)
plot(x,y)
foo2 <- gausspr(x, y, variance.model = TRUE)
xtest <- seq(-4,2,0.2)
lines(xtest, predict(foo2, xtest))
lines(xtest,
predict(foo2, xtest)+2*predict(foo2,xtest, type="sdeviation"),
col="red")
lines(xtest,
predict(foo2, xtest)-2*predict(foo2,xtest, type="sdeviation"),
col="red")
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