Nothing
.getcorrCovmACM <- function (S1, K, W=diag(ncol(K)))
{
###########################################
##
## R-function: .getcorrCovmACM - computes filtering error covarince matrix
## (internal function)
## author: Bernhard Spangl
## version: 0.1 (2008-03-23)
##
###########################################
## Paramters:
## S1 ... prediction error covariance matrix (formerly: M)
## K ... dummy matrix, i.e., S1 %*% t(Z) %*% st
## W ... weight matrix
S1 - K %*% W %*% t(K)
}
.mACMcorrstep <- function (y, x1, S1, Z, V, rob1=NULL,
psi, apsi, bpsi, cpsi, flag, ...)
{
###########################################
##
## R-function: .mACMcorrstep - correction step (internal function)
## author: Bernhard Spangl
## version: 0.2 (2008-03-31)
##
###########################################
## Paramters:
## y ... observed vector-valued time series
## x1 ... state vector (one-step-ahead predictor)
## S1 ... prediction error covariance matrix (formerly: M)
## Z ... observation matrix (formerly: H)
## V ... covariance matrix of observation noise (formerly: R)
## psi ... influence function to be used
## a, b, c ... tuning constants for Hampel's psi-function
## (default: a=b=2.5, c=5.0)
## flag ... character, weight matrix to be used in correction step,
## if "deriv" (default), Jacobian matrix of multivariate analogue
## of Hampel's psi-function is used (only default is available
## at the moment)
D <- S1 %*% t(Z)
Rt <- Z %*% D + V
sqrtR <- rootMatrix(Rt)
st <- sqrtR$X.sqrt.inv
K <- D %*% st
yDelta <- drop(st %*% (y - Z %*% x1))
yPsi <- psi(yDelta, apsi, bpsi, cpsi)
xDelta <- K %*% yPsi
x0 <- x1 + xDelta
ind <- sqrt(yDelta%*%yDelta) > apsi
jacobian.psi <- .weighting(flag)
W <- jacobian.psi(yDelta, a=apsi, b=bpsi, c=cpsi)
S0 <- .getcorrCovmACM(S1, K, W = W)
return(list(x0 = x0, K = K, S0 = S0, Ind=ind, rob0=st, Delta =NULL, DeltaY = yDelta))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.