mesa.data.raw {SpatioTemporal} | R Documentation

## Data used in the examples

### Description

The raw data that was used to create the `mesa.model` structures.
The data structure contains raw data from the MESA Air project. The example below describes how to create the `mesa.model` structure from raw data.

### Format

The structure contains observations, temporal trends, locations, geographic covariates, and spatio-temporal covariates. The data is stored as a list with elements:

X

  A data.frame containing names, locations, and (geographic) covariates for all the (observation) locations.

obs

  A time-by-location matrix for the observed data, missing data marked as `NA`

lax.conc.1500

  A time-by-location matrix of a spatio-temporal covariate based on output from Caline3QHC.

### Source

Contains monitoring data from the MESA Air project, see Cohen et.al. (2009) 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

`createSTdata` for creation of `STdata` objects.

Other data matrix: `SVDmiss`, `SVDsmooth`, `createDataMatrix`, `estimateBetaFields`

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

### Examples

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

    ##extract matrix of observations (missing marked by NA)
    obs.mat <- mesa.data.raw$obs
    head(obs.mat)

    ##optionally observations can be given as a data.frame
    obs <- data.frame(obs=c(obs.mat),
                      date=rep(rownames(obs.mat), dim(obs.mat)[2]),
                      ID=rep(colnames(obs.mat), each=dim(obs.mat)[1]))
    ##force date-format
    obs$date <- as.Date(obs$date)

    ##drop unobserved
    obs <- obs[!is.na(obs$obs),,drop=FALSE]

    ##create a 3D-array for the spatio-temporal covariate
    ST <- array(mesa.data.raw$lax.conc.1500, dim =
                c(dim(mesa.data.raw$lax.conc.1500),1))
    dimnames(ST) <- list(rownames(mesa.data.raw$lax.conc),
                         colnames(mesa.data.raw$lax.conc),
                         "lax.conc.1500")
    ##or use a list of matrices
    ST.list <- list(lax.conc.1500=mesa.data.raw$lax.conc.1500)

    ###########################
    ## create STdata object ##
    ###########################
    ##Create the data-object
    mesa.data <- createSTdata(obs.mat, mesa.data.raw$X, n.basis=2,
                              SpatioTemporal=ST)
    mesa.data.2 <- createSTdata(obs, mesa.data.raw$X, n.basis=2,
                                SpatioTemporal=ST.list)

    ##This should yield equal structures,
    ##which are also the same as data(mesa.data)
    all.equal(mesa.data, mesa.data.2)

    ###########################
    ## create STmodel object ##
    ###########################
    ##define land-use covariates, for intercept and trends
    LUR <- list(~log10.m.to.a1+s2000.pop.div.10000+km.to.coast,
      ~km.to.coast, ~km.to.coast)
    ##and covariance model
    cov.beta <- list(covf="exp", nugget=FALSE)
    cov.nu <- list(covf="exp", nugget=~type, random.effect=FALSE)
    ##which locations to use
    locations <- list(coords=c("x","y"), long.lat=c("long","lat"), others="type")
    ##create object
    mesa.model <- createSTmodel(mesa.data, LUR=LUR, ST="lax.conc.1500",
                                cov.beta=cov.beta, cov.nu=cov.nu,
                                locations=locations)

    ##This should be the same as the data in data(mesa.model)

[Package SpatioTemporal version 1.1.9.1 Index]