Usage
## S3 method for class 'formula':
rvm(x, data=NULL, ..., subset, na.action = na.omit)## S3 method for class 'vector':
rvm(x, ...)
## S3 method for class 'matrix':
rvm(x, y, type="regression", kernel="rbfdot", kpar=list(sigma=0.1),
alpha=1, var=0.1, var.fix=FALSE, iterations=100, verbosity=0, tol=
.Machine$double.eps,minmaxdiff = 1e-3, cross = 0, fit =TRUE, subset,
na.action = na.omit,...)
Arguments
x
a symbolic description of the model to be fit. Note, 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
`rvm' 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
rvm can only be used for regression at the moment.
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
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 :
sigma(inverse kernel width for the Radial
alpha
The initial alpha vector. Can be either a vector of
length equal to the number of data points or a single number.
var
the initial noise variance
var.fix
Keep noise variance fix during iterations (default: FALSE)
iterations
Number of iterations allowed (default: 100)
tol
tolerance of termination criterion
minmaxdiff
termination criteria. Stop when max difference is
equall to this parameter (default:1e-3)
verbosity
print information on algorithm convergence (default
= FALSE)
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 NAs 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<