Computes or updates some auxiliary variables used for kriging (see below). This is useful in several situations : when all parameters are known (as for one basic step in Bayesian analysis), or when some new data is added but one does not want to re-estimate the model coefficients. On the other hand, `computeAuxVariables`

is not used during the estimation of covariance parameters, since this function requires to compute the trend coefficients at each optimization step; the alternative given by (Park, Baek, 2001) is preferred.

`computeAuxVariables(model)`

model

an object of class `km`

with missing (or non updated) items.

An updated `km`

objet, where the changes concern the following items:

a matrix equal to the upper triangular factor of the Choleski decomposition of `C`

, such that `t(T)*T = C`

(where C is the covariance matrix).

a vector equal to `inv(t(T))*(y - F*beta)`

, with `y`

, `F`

, `beta`

are respectively the response, the experimental matrix and the trend coefficients specified in `model@trend.coef`

. If `model@trend.coef`

is empty, `z`

is not computed.

a matrix equal to `inv(t(T))*F`

.

J.-S. Park and J. Baek (2001), Efficient computation of maximum likelihood estimators in a spatial linear model with power exponential covariogram, *Computer Geosciences*, **27** no. 1, 1-7.