Description Usage Arguments Details Value Author(s) Examples
Aggregate the data for quasi-likelihood estimation
1 2 |
runs |
list or matrix of simulation results from |
X |
list or matrix of model parameters |
var.type |
character, " |
Nb |
numeric, number of bootstrap samples for kriging the variance matrix,
only if ' |
na.rm |
if |
verbose |
if |
The function aggregates all neccessary data for quasi-likelihood estimation storing the
sample locations and the corresponding simulation results of the statistics.
If 'X
' equals NULL
, then the sample points are taken from the object 'runs
'.
The most critical part is the decomposition of variance matrices for each sample location unless 'var.type
'
equals "const
" in which case a constant variance matrix approximation is expected later by function qle
.
The Cholesky decompositions are used for average approximations of the variance matrix of the statistics when calculating the
quasi-score vector or any type of function criterion based on the Mahalanobis distance or quasi-deviance.
If these fail for any reason we try to ignore, if possible, the corresponding sample points and exclude them
from all following calculations. Unless a constant estimate of the variance matrix, the default is to approximate the
variance at any model parameter by either a kriging interpolation of the Cholesky terms or as an average over
all sampled variance matrices also based on the decomposed Cholesky terms (see vignette).
An object of class QLdata
as a data frame with columns:
X |
Model parameters ( |
mean |
Results of simulations ( |
var |
Simulation variances of statistics ( |
L |
if applicable, Cholesky decomposed terms of variance matrices of statistics (k=1,...,(m*(m+1)/2)) |
where 'p
' denotes the number of user defined statistics and 'q
' the problem dimension, that is,
the number of parameters to be estimated.
The following items are stored as attributes:
type |
see above |
nsim |
number of simulations at each point |
xdim |
parameter dimension |
nWarnings |
Number of warnings during simulations |
nErrors |
Number of errors during simulations |
nIgnored |
List of parameters ignored (because of failures) |
M. Baaske
1 2 3 4 5 6 7 8 9 10 11 12 13 | # simulate model statistics at LHS design
sim <- simQLdata(sim =
function(x,cond) {
X <- rlnorm(cond$n,x[1],x[2])
c("MED"=median(X),"MAD"=mad(X))
},
cond=list("n"=10),
nsim=10, N=10, method="maximinLHS",
lb=c("mu"=-1.5,"sd"=0), ub=c("mu"=2,"sd"=1))
# setup the QL data model using defaults
qldata <- setQLdata(sim,verbose=TRUE)
|
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