mesa.model: Example of a 'STmodel' structure

Description Format Details Source References See Also Examples

Description

Example of a model structure holding observations, geographic covariates, observation locations, smooth temporal trends, spatio-temporal covariates, and covariance specifications for the model.

Format

A list with elements, a detailed description of each elements is given in details below

Details

A STmodel object consists of a list with, some or all of, the following elements:

obs

A data.frame with columns:

obs

The value of each observation.

date

The observations time, preferably of class Date.

ID

A character-class giving observation locations; should match elements in locations$ID.

idx

match between obs$ID and locations$ID for faster computations.

The data.frame is sorted by date and idx.

locations.list,locations

Specification of locations and data.frame with locations for observations (and predictions), see processLocation.

D.nu,D.beta

Distance matrices for the locations in the, possibly different coordinate systems for beta- and nu-fields. See processLocation.

cov.beta,cov.nu

Covariance structure for beta- and nu-fields, see updateCovf.

LUR.list,LUR

Specification of covariates for the beta-fields and a list with covariates for each of the beta-fields, see processLUR and createLUR.

trend,trend.fnc

The temporal trends with one of the columns being named date, preferably of class Date providing the time alignment for the temporal trends.

F

A matrix contaning smooth temporal trends for each observation; elements taken from trend.

ST.list,ST,ST.all

Spatio-termporal covariates, NULL if no covariates. For the observations and all space-time locations respectively, see processST and createST.

old.trend,fit.trend

Additional components added if the observations have been detrended, see detrendSTdata.

Source

Contains monitoring data from the MESA Air project, see Cohen et.al. (2009) and mesa.data.raw for details.

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

createSTmodel for creation of STmodel objects.
createSTdata for creation of the originating STdata object.

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

Examples

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##load the data
data(mesa.model)

##examine components
names(mesa.model)
print(mesa.model)
summary(mesa.model)

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

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

##Some important dimensions of the model
loglikeSTdim(mesa.model)

SpatioTemporal documentation built on June 25, 2018, 9:03 a.m.