est.cv.mesa {SpatioTemporal} | R Documentation |
estCVSTmodel
and predCVSTmodel
structuresExample of 10-fold cross-validated for the model in mesa.model
using estimateCV.STmodel
and predictCV.STmodel
.
A list with elements, see the return description in
estimateCV.STmodel
and predictCV.STmodel
.
Contains parametere estimates for the Spatio-Temporal model applied
to monitoring data from the MESA Air project, see
Cohen et.al. (2009) and mesa.data.raw
for details.
M. A. Cohen, S. D. Adar, R. W. Allen, E. Avol, C. L. Curl, T. Gould, D. Hardie, A. Ho, P. Kinney, T. V. Larson, P. D. Sampson, L. Sheppard, K. D. Stukovsky, S. S. Swan, L. S. Liu, J. D. Kaufman. (2009) Approach to Estimating Participant Pollutant Exposures in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Environmental Science & Technology: 43(13), 4687-4693.
estimateCV.STmodel
and
predictCV.STmodel
for cross-validation.
createSTmodel
for creation of the originating STmodel
object.
Other example data: MCMC.mesa.model
,
est.mesa.model
,
mesa.data.raw
, mesa.model
,
pred.mesa.model
##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)