SpatioTemporal-package {SpatioTemporal} | R Documentation

## Spatio-Temporal Modelling

### Description

Package for spatio-temporal modelling. Contains functions that estimate, simulate and predict from the model described in (Szpiro et.al., 2010; Sampson et.al., 2011; Lindstrom et.al., 2010). The package also contains functions that handle missing data SVD in accordance with (Fuentes et.al. 2006).

Package: | SpatioTemporal
Type: | Package
Version: | 1.1.9
Date: | 2018-06-20
License: | GPL version 2 or newer
LazyLoad: | yes

Examples in the package uses data from the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), (Cohen et.al.,2009).

### Changelog

1.1.9

  Upates: R 3.5.0 Compatibility and Matrix

- Minor updates to fullfill R 3.5.0 and changes to Matrix-package.

1.1.8

  Upates: R 3.2.1 Compatibility

- Minor updates to fullfill R 3.2.1 changes.

1.1.7

  Upates: Handling of log-Gaussian fields

- Updated several functions to allow for prediction and CV of log-Gaussian fields. Updated functions: `predict.STmodel`, `print.predictSTmodel`, `plot.predictSTmodel`, `predictCV.STmodel`, `print.predCVSTmodel`, `summary.predCVSTmodel`, `plot.predCVSTmodel`, `qqnorm.predCVSTmodel`, and `scatterPlot.predCVSTmodel`.

- Updated `predict.STmodel` to compute temporal averages, and return both prediction and variance of the averages. Both for Gaussian and log-Gaussian data.

1.1.6

  Upates: sparse-Matrices and temporal basis functions

- Allows for sparse matrices in `makeSigmaB` and `makeSigmaNu`; this reduces the memory footprint and execution time for `loglikeST`, `predict.STmodel`, and `estimate.STmodel`.

- Added function that does regression estimates of the beta-coefficients: `estimateBetaFields`.

- Altered computation of CV-statistics in `SVDsmoothCV`.

- Added `boxplot.SVDcv` for illustration of CV-statistics from `SVDsmoothCV`.

- Replaced `updateSTdataTrend` with `updateTrend.STdata` and `updateTrend.STmodel` that also allows for temporal trends defined using functions.

- Updated `SVDsmooth`, `SVDsmoothCV`, and `calcSmoothTrends` to return both the trend and the smoothing function used to compute the trends, simplifying interpolation at unobserved time-points.

- Updated example data-sets.

- Added options for computation of temporal averages (incl. variances) to `predict.STmodel` and `predictCV.STmodel`.

1.1.5

  Major bug fixes:

- In `predict.STmodel`, predictions now always uses the trend given in `object`, ignoring the trend object in `STdata`. Prediction at dates in `STdata` are computed using the smoothing function that defines the trend; see `updateTrend.STmodel` for details.

- In `summary.predCVSTmodel`, code previously divided by the wrong variance when computing adjusted R2 using the `pred.naive` option.

- In `summary.predCVSTmodel`, code previously returned statistics even for dates without observations when using `by.date=TRUE`.

- In `plot.STdata` and `plot.STmodel` code now accounts for missing time-points when computing acf and pacf.

1.1.4

  Added plot funcions/Minor fixes:

- Added `scatterPlot.STdata`, `scatterPlot.STmodel`, and `scatterPlot.predCVSTmodel` for plotting observations/residuals against covariates.

- Added `plot.mcmcSTmodel`, `density.mcmcSTmodel`, and `plot.density.mcmcSTmodel` for plotting of MCMC results.

- Added `qqnorm.STdata`, `qqnorm.STmodel`, and `qqnorm.predCVSTmodel` for plotting of data and CV-prediction results.

- Added a `restart` option to `estimate.STmodel` allowing for restarts of optimisation in cases on bad optimisation.

1.1.3

  Minor changes/Bug fixes:

- Fixed stupid misstake in `predictNaive` that caused computations to take unnecessarily long.

1.1.2

  Minor changes/Bug fixes:

- Fixed a bug in `SVDsmooth`, that caused the values in the temporal smooths to depend on the number of unobserved time points.. This also affects `calcSmoothTrends` and `updateSTdataTrend` when the option `extra.dates` is in use.

- Fixed bug in `simulate.STmodel` that caused `NA` values when simulating at unobserved sites.

- Fixed bug in `predict.STmodel` that could cause errors when predicting at unobserved sites.

- Fixed bug in `predictCV.STmodel` and `predict.STmodel`; these will now handle predictions at locations with incomplete nugget covariates.

- Updated `c.STmodel` and `predict.STmodel` to avoid errors/warnings due to more complex nugget models.

- Replaced warning in `createSTdata` when `extra.dates!=NULL` and `n.basis=NULL` with a message.

1.1.1

  Bug fixes:

- `c.STmodel` will now combine `STmodel` objects with identical covariate scaling.

1.1.0

  Major Changes:

- Changed the return of the variances for `beta` in `predict.STmodel`.

- Reduced the memory footprint of `predict.STmodel`.

- Error checks in `c.STmodel` and `predict.STmodel`, combination of `STmodel` objects with different covariate scaling is NOT possible.

1.0.7

  Added:

- New plot function: `plot.predCVSTmodel`.

- `coef.estimateSTmodel` and `coef.estCVSTmodel` functions that extract estimated parameters.

- Parameters for `predict.STmodel` and `predictCV.STmodel` can be specified using `estimateSTmodel` or `estCVSTmodel` objects.

- An `lwd` option to `plot.predictSTmodel`.

- A short introductory vignette as complement to the full tutorial.

1.0.6

  Bug fixes:

- `predictNaive` now works for only one locations.

- `detrendSTdata` now works for different regions.

1.0.5

  Added packages `maps` and `plotrix` to suggested packages.

1.0.4

  Bug fixes:

- prediction for leave-one-out CV.

- stop updateCovf crashing in Rscript/R CMD BATCH.

1.0.3

  Minor bug fixes

1.0.2

  Updated documentation and vignette

1.0.0

  Major change, most old functions are now deprecated. New features:

- Different covariance functions

- Nuggets in the beta-fields

- Different nuggets for different locations in the nu-field.

- Different coordinates for beta and nu-fields, allowing for precomputed deformations

- Covariates can be specifed using formula-objects

0.9.2

  Minor updates - no user visible changes

0.9.0

  First CRAN-release

0.1.0

  First released version, short course at TIES-2010

### Note

Data used in the examples has been provided by the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Details regarding the data can be found in Cohen et.al. (2009).
Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through assistance agreement CR-834077101-0 and grant RD831697 to the University of Washington, it has not been subjected to the Agency's required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.
Travel for J. Lindstrom has been paid by STINT (The Swedish Foundation for International Cooperation in Research and Higher Education) Grant IG2005-2047.
Additional funding was provided by grants to the University of Washington from the Health Effects Institute (4749-RFA05-1A/06-10) and the National Institute of Environmental Health Sciences (P50 ES015915).

### Author(s)

Johan Lindstrom, Adam Szpiro, Paul D. Sampson, Silas Bergen, Assaf P. Oron

### 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.

M. Fuentes, P. Guttorp, and P. D. Sampson. (2006) Using Transforms to Analyze Space-Time Processes in Statistical methods for spatio-temporal systems (B. Finkenstadt, L. Held, V. Isham eds.) 77-150

J. Lindstrom, A. Szpiro, P. D. Sampson, L. Sheppard, A. Oron, M. Richards, and T. Larson T. (2010) A flexible spatio-temmporal model for air pollution: allowing for spatio-temporal covariates. Berkeley Electronic Press, University of Washington Biostatistics Working Paper Series, No. 370. http://www.bepress.com/uwbiostat/paper370

A. Szpiro, P. D. Sampson, L. Sheppard, T. Lumley, S. D. Adar, and J. D. Kaufman. (2010) Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics: 21, 606-631.

P. D. Sampson, A. Szpiro, L. Sheppard, J. Lindstrom, J. D. Kaufman. (2011) Pragmatic Estimation of a Spatio-temporal Air Quality Model with Irregular Monitoring Data. Atmospheric Environment: 45(36), 6593-6606.

### Examples

    ##For a short introduction see:
    ## Not run:
      vignette("ST_intro",package="SpatioTemporal")

    ## End(Not run)

    ##For a worked out data-analysis exmaple see the tutorial.
    ##NOTE: This vignette is still work in progress
    ## Not run:
      vignette("Tutorial",package="SpatioTemporal")

    ## End(Not run)

[Package SpatioTemporal version 1.1.9.1 Index]