gausspr {kernlab} | R Documentation |
gausspr
is an implementation of Gaussian processes
for classification and regression.
## S4 method for signature 'formula': gausspr(x, data=NULL, ..., subset, na.action = na.omit) ## S4 method for signature 'vector': gausspr(x,...) ## S4 method for signature 'matrix': gausspr(x, y, type="classification", kernel="rbfdot", kpar=list(sigma = 0.1), var=1, tol=0.001, cross=0, fit=TRUE, ... , subset, na.action = na.omit)
x |
a symbolic description of the model to be fit or a matrix or vector when a formula interface is not used. Note, that an when using the formula interface, that an intercept is always included, whether given in the formula or not. When not using a formula x is a matrix or vector containg the variables in the model |
data |
an optional data frame containing the variables in the model. By default the variables are taken from the environment which `gausspr' is called from. |
y |
a response vector with one label for each row/component of x . Can be either
a factor (for classification tasks) or a numeric vector (for
regression). |
type |
Type of problem. Either "classification" or "regression" |
kernel |
the kernel function used in training and predicting.
This parameter can be set to any function, of class kernel, which computes a dot product between two
vector arguments. kernlab provides the most popular kernel functions
which can be used by setting the kernel parameter to the following
strings:
|
kpar |
the list of hyper-parameters (kernel parameters).
This is a list which contains the parameters to be used with the
kernel function. For valid parameters for existing kernels are :
Hyper-parameters for user defined kernels can be passed through the kpar parameter as well. |
var |
the initial noise variance |
tol |
tolerance of termination criterion (default: 0.001) |
fit |
indicates whether the fitted values should be computed and included in the model or not (default: 'TRUE') |
cross |
if a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model: the Mean Squared Error for regression |
subset |
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) |
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.) |
... |
additional parameters |
A Gaussian process is specified by a mean and a covariance function. The mean is a function of x (which is often the zero function), and the covariance is a function C(x,x') which expresses the expected covariance between the value of the function y at the points x and x'. The actual function y(x) in any data modelling problem is assumed to be a single sample from this Gaussian distribution. Model parameter estimation in classification is done by a gradient descent algorithm.
An S4 object of class "gausspr" containing the fitted model along with information. Accessor functions can be used to access the slots of the object which include :
alpha |
The resulting model parameters |
error |
Training error (if fit == TRUE) |
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
Christopher K.I. Williams, Carl Edward Rasmussen
Gaussian Processes for Regression
Advances in Neural Information Processing Systems, NIPS
http://books.nips.cc/papers/files/nips08/0514.pdf
# train model data(iris) test <- gausspr(Species~.,data=iris,var=2) test alpha(test) # predict on the training set predict(test,iris[,-5]) # 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")