SpatioTemporal (version 1.1.7)

est.cv.mesa: Example of estCVSTmodel and predCVSTmodel structures

Description

Example of 10-fold cross-validated for the model in mesa.model using estimateCV.STmodel and predictCV.STmodel.

Format

A list with elements, see the return description in estimateCV.STmodel and predictCV.STmodel.

References

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.

See Also

estimateCV.STmodel and predictCV.STmodel for cross-validation. createSTmodel for creation of the originating STmodel object.

Other example data: est.mesa.model, MCMC.mesa.model, mesa.data.raw, mesa.model, pred.mesa.model

Examples

# NOT RUN {
##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)
# }
# 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)
# }
# 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 )
# }