mesa.model {SpatioTemporal} | R Documentation

## Example of a `STmodel` structure

### 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

    ##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)

[Package SpatioTemporal version 1.1.9.1 Index]