SpatioTemporal (version 0.9.2)

mesa.data.model: Example of a mesa.data.model Structure

Description

Example of an auxiliary data structure holding information regarding which geographic and spatio-temporal covariates to use when fitting the model. Should always be created by calling create.data.model on a mesa.data structure.

Usage

data(mesa.data.model)

encoding

latin1

Warning

This structure should always be created by calling create.data.model on a mesa.data structure. The user should never attempt to create the structure from scratch.

source

Contains monitoring data from the MESA Air project, see Cohen et.al. (2009) and mesa.data.raw for details. Obtained from mesa.data as: mesa.data.model <- create.data.model(mesa.data, LUR = list(c("log10.m.to.a1", "s2000.pop.div.10000", "km.to.coast"),"km.to.coast", "km.to.coast"), ST.Ind=NULL) See mesa.data and create.data.model for details.

Details

This data structure is required as input to most of the functions in this package, see loglike, fit.mesa.model, cond.expectation, simulateMesaData, run.MCMC, etc.

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

See mesa.data and create.data.model.

For other data functions, see mesa.data.raw, create.data.matrix, construct.LUR.basis and default.LUR.list.

Examples

##load the data
data(mesa.data.model)

##examine components
names(mesa.data.model)
printMesaDataNbrObs(mesa.data.model)

##requested geographic and spatio-temporal covariates
mesa.data.model$LUR.list
mesa.data.model$ST.Ind

##covariates for the temporal intercept
head(mesa.data.model$X$const)
##...and the two smooth temporal trends
head(mesa.data.model$X$V1)
head(mesa.data.model$X$V2)

##Some important dimensions of the model
loglike.dim(mesa.data.model)