gausspr {kernlab} | R Documentation |
gausspr
is an implementation of Gaussian processes.
Gaussian processes can be used 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.
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")