MCMC.mesa.model {SpatioTemporal} | R Documentation |
mcmcSTmodel
structureThe output from a Metropolis-Hastings algorithm, implemented in
MCMC.STmodel
), run for the model in mesa.model
A list with elements, see the return description in
MCMC.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.
createSTmodel
for creation of the originating
STmodel
object.
Other example data: est.cv.mesa
,
est.mesa.model
,
mesa.data.raw
, mesa.model
,
pred.mesa.model
##load data data(mesa.model) ##and results of estimation data(est.mesa.model) ##strating point x <- coef(est.mesa.model) ##Hessian, for use as proposal matrix H <- est.mesa.model$res.best$hessian.all ## Not run: ##run MCMC MCMC.mesa.model <- MCMC(mesa.model, x$par, N = 2500, Hessian.prop = H) ## End(Not run) ##lets load precomputed results instead data(MCMC.mesa.model) ##Examine the results print(MCMC.mesa.model) ##and contens of result vector names(MCMC.mesa.model) ##Summary summary(MCMC.mesa.model) ##MCMC tracks for four of the parameters par(mfrow=c(5,1),mar=c(2,2,2.5,.5)) plot(MCMC.mesa.model, ylab="", xlab="", type="l") for(i in c(4,9,13,15)){ plot(MCMC.mesa.model, i, ylab="", xlab="", type="l") }