Description Usage Arguments Details Value Author(s) Examples
Compute the Mahalanobis distance (MD) based on the kriging models of statistics
1 2 3 |
points |
either matrix or list of points or a vector of parameters (but then considered as a single (multidimensional) point) |
qsd |
object of class |
Sigma |
either a constant variance matrix estimate or an prespecified value |
... |
further arguments passed to |
cvm |
list of fitted cross-validation models (see |
obs |
numeric vector of observed statistics (overwrites ' |
inverted |
logical, |
check |
logical, |
w |
numeric value, scalar weight, |
value.only |
integer, |
na.rm |
logical, if |
cl |
cluster object, |
verbose |
if |
The function computes the Mahalanobis distance of the given statistics T(X)\in R^p with different options
for the approximation type of the variance matrix. The Mahalanobis distance can be used as an alternative criterion function
for estimating the unknown model parameter during the main estimation function qle
.
There are several options how to estimate or choose the variance matrix of the statistics Σ.
First, in case of a given constant variance matrix estimate 'Sigma
', the Mahalanobis distance reads
(T(x)-E_{θ}[T(X)])^tΣ^{-1}(T(x)-E_{θ}[T(X)])
and 'Sigma
' is used as given.
As a second option, the variance matrix Σ can be estimated by the average approximation
\bar{V}=\frac{1}{n}∑_{i=1}^n V_i
based on the simulated variance matrices V_i=V(θ_i) of statistics over all sample points
θ_1,...,θ_n (see vignette).
Unless 'qsd$var.type
' equals "const
" additional prediction variances are added as diagonal terms in order
to account for the kriging approximation error of the statistics using kriging. A weighted version of these average approximation
types is also available (see covarTx
).
As a continuous variance approximation we use a kriging approach (see [1]). Then
Σ(θ) = Var_{θ}(T(X))
denotes the variance matrix which depends on the parameter θ\in R^q and corresponds to the
formal function argument 'points
'. Each time a value of the criterion function is calculated at any parameter
'points
' the variance matrix is estimated by the correpsonding approach with added prediction variances as explained above.
Note that in this case the argument 'Sigma
' is ignored.
Either a vector of MD values or a list of lists, where each contains the following elements:
value |
Mahalanobis distance value |
par |
parameter estimate |
I |
approximate variance matrix of the parameter estimate |
score |
gradient of MD (for constant ' |
jac |
Jacobian of sample mean values of statistics |
varS |
estimated variance matrix of ' |
and the following attributes:
Sigma |
estimate of variance matrix |
inverted |
whether ' |
M. Baaske
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