kqr-class function

Class "kqr"

The Kernel Quantile Regression object class class

Objects from the Class

Objects can be created by calls of the form new("kqr", ...). or by calling the kqr function

Slots

• kernelf:: Object of class "kfunction" contains the kernel function used
• kpar:: Object of class "list" contains the kernel parameter used
• coef:: Object of class "ANY" containing the model parameters
• param:: Object of class "list" contains the cost parameter C and tau parameter used
• kcall:: Object of class "list" contains the used function call
• terms:: Object of class "ANY" contains the terms representation of the symbolic model used (when using a formula)
• xmatrix:: Object of class "input" containing the data matrix used
• ymatrix:: Object of class "output" containing the response matrix
• fitted:: Object of class "output" containing the fitted values
• alpha:: Object of class "listI" containing the computes alpha values
• b:: Object of class "numeric" containing the offset of the model.
• scaling: Object of class "ANY" containing the scaling coefficients of the data (when case scaled = TRUE is used).
• error:: Object of class "numeric" containing the training error
• cross:: Object of class "numeric" containing the cross validation error
• n.action:: Object of class "ANY" containing the action performed in NA
• nclass:: Inherited from class vm, not used in kqr
• lev:: Inherited from class vm, not used in kqr
• type:: Inherited from class vm, not used in kqr

Methods

• coef: signature(object = "kqr"): returns the coefficients (alpha) of the model
• alpha: signature(object = "kqr"): returns the alpha vector (identical to coef)
• b: signature(object = "kqr"): returns the offset beta of the model.
• cross: signature(object = "kqr"): returns the cross validation error
• error: signature(object = "kqr"): returns the training error
• fitted: signature(object = "vm"): returns the fitted values
• kcall: signature(object = "kqr"): returns the call performed
• kernelf: signature(object = "kqr"): returns the kernel function used
• kpar: signature(object = "kqr"): returns the kernel parameter used
• param: signature(object = "kqr"): returns the cost regularization parameter C and tau used
• xmatrix: signature(object = "kqr"): returns the data matrix used
• ymatrix: signature(object = "kqr"): returns the response matrix used
• scaling: signature(object = "kqr"): returns the scaling coefficients of the data (when scaled = TRUE is used)

Author(s)

Alexandros Karatzoglou

alexandros.karatzoglou@ci.tuwien.ac.at

See Also

kqr, vm-class, ksvm-class

Examples

# create data
x <- sort(runif(300))
y <- sin(pi*x) + rnorm(300,0,sd=exp(sin(2*pi*x)))

# first calculate the median
qrm <- kqr(x, y, tau = 0.5, C=0.15)

# predict and plot
plot(x, y)
ytest <- predict(qrm, x)
lines(x, ytest, col="blue")

# calculate 0.9 quantile
qrm <- kqr(x, y, tau = 0.9, kernel = "rbfdot",
           kpar = list(sigma = 10), C = 0.15)
ytest <- predict(qrm, x)
lines(x, ytest, col="red")

# print model coefficients and other information
coef(qrm)
b(qrm)
error(qrm)
kernelf(qrm)