Description Usage Arguments Details Value Author(s) See Also Examples
Functions that perform cross-validated parameter estimation and prediction for the spatio-temporal model.
1 2 3 4 5 6 7 8 9 10 11 |
object |
|
x |
Either a vector or matrix of starting point(s) for the optimisation,
see |
Ind.cv |
|
control |
A list of control parameters for the optimisation.
See |
verbose.res |
A |
... |
All additional parameters for |
silent |
Show status after each iteration? |
LTA |
|
For predictCV.STmodel
the parameters used to compute predictions for the left
out observations can be either a single vector or a matrix.
For a single vector the same parameter values will be used for all
cross-validation predictions; for a matrix the parameters in x[,i]
will be used for the predictions of the i:th cross-validation set (i.e. for
Ind.cv[,i]
). Suitable matrices are provided in the output from
estimateCV.STmodel
.
The cross-validation groups are given by Ind.cv
. Ind.cv
should
be either a (number of observations) - by - (groups) logical matrix or an
integer valued vector with length equal to (number of observations).
If a matrix then each column defines a cross-validation set with the
TRUE
values marking the observations to be left out. If a vector then
1
:s denote observations to be dropped in the first cross-validation
set, 2
:s observations to be dropped in the second set, etc.
Observations marked by values <=0
are never dropped. See
createCV
for details.
Either a estCVSTmodel
object with elements:
status |
Data.frame with convergence information and best function value for each cross-validation group. |
Ind.cv |
The cross-validation grouping. |
x.fixed |
Fixed parameters in the estimation, see
|
x.init |
Matrix of inital values used, i.e. |
par.all, par.cov |
Matrices with estimated parameters for each cross-validation group. |
par.all.sd, par.cov.sd |
Standard deviations computed from the Hessian/information matrix for set of estimated parameters. |
res.all |
Estimation results for each cross-validation group,
contains the output from the |
Or a predCVSTmodel
object with elements:
opts |
Copy of the |
Ind.cv |
The cross-validation grouping. |
pred.obs |
A data.frame with a copy of observations from
|
pred.all |
A list with time-by-location data.frames containing
predictions and variances for all space-time locations as
well as predictions and variances for the
beta-fields. Unobserved points are |
Johan Lindstrom
Other STmodel methods: MCMC.STmodel
,
c.STmodel
, createSTmodel
,
estimate.STmodel
,
plot.STdata
, predict.STmodel
,
print.STmodel
,
print.summary.STmodel
,
qqnorm.predCVSTmodel
,
scatterPlot.predCVSTmodel
,
simulate.STmodel
,
summary.STmodel
Other cross-validation functions: computeLTA
,
createCV
, dropObservations
,
predictNaive
Other estCVSTmodel methods: boxplot.estCVSTmodel
,
coef.estCVSTmodel
,
print.estCVSTmodel
,
print.summary.estCVSTmodel
,
summary.estCVSTmodel
Other predCVSTmodel functions: computeLTA
Other predCVSTmodel methods: plot.predCVSTmodel
,
print.predCVSTmodel
,
print.summary.predCVSTmodel
,
qqnorm.predCVSTmodel
,
scatterPlot.predCVSTmodel
,
summary.predCVSTmodel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ##load data
data(mesa.model)
data(est.mesa.model)
################
## estimateCV ##
################
##create the CV structure defining 10 different CV-groups
Ind.cv <- createCV(mesa.model, groups=10, min.dist=.1)
##use the best parameters and there starting values as
x.init <- coef(est.mesa.model, pars="cov")[,c("par","init")]
## Not run:
##estimate different parameters for each CV-group
est.cv.mesa <- estimateCV(mesa.model, x.init, Ind.cv)
## End(Not run)
##lets load precomputed results instead
data(est.cv.mesa)
##examine the estimation results
print( est.cv.mesa )
##estimated parameters for each CV-group
coef(est.cv.mesa, pars="cov")
###############
## predictCV ##
###############
## Not run:
##Do cross-validated predictions using the just estimated parameters
##Ind.cv is infered from est.cv.mesa as est.cv.mesa$Ind.cv
pred.cv.mesa <- predictCV(mesa.model, est.cv.mesa, LTA=TRUE)
## End(Not run)
##lets load precomputed results instead
data(pred.cv.mesa)
##prediction results
print( pred.cv.mesa )
##and CV-statistics
print( summary( pred.cv.mesa, LTA=TRUE) )
## Not run:
##A faster option is to only consider the observations and not to compute
##variances
pred.cv.fast <- predictCV(mesa.model, est.cv.mesa, only.obs=TRUE,
pred.var=FALSE)
print( pred.cv.fast )
summary( pred.cv.fast )
## End(Not run)
|
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