gausspr
is an implementation of Gaussian processes
for classification and regression.## S3 method for class 'formula':
gausspr(x, data=NULL, ..., subset, na.action = na.omit, scaled = TRUE)## S3 method for class 'vector':
gausspr(x,...)
## S3 method for class 'matrix':
gausspr(x, y, scaled = TRUE, type= NULL, kernel="rbfdot", kpar="automatic",
var=1, tol=0.0005, cross=0, fit=TRUE, ... , subset, na.action = na.omit)
x
. Can be either
a factor (for classification tasks) or a numeric vector (for
regression).y
is a factor or not, the default
setting for type
is classification
or regression
,
respectively, but can be ovescaled
is of length 1, the value is recycled as
many times as needed and all non-binary variables are scaled.
Per default, data are scaled internally (both x
sigma
inverse kernel width for the Radial BasisNA
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
, whitype
parameter to "probabilities".predict.gausspr
, rvm
, ksvm
, gausspr-class
, lssvm
# 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")
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