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).
|License:||GPL version 2 or newer|
Examples in the package uses data from the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), (Cohen et.al.,2009).
Upates: R 3.5.0 Compatibility and Matrix
Minor updates to fullfill R 3.5.0 and changes to Matrix-package.
Upates: R 3.2.1 Compatibility
Minor updates to fullfill R 3.2.1 changes.
Upates: Handling of log-Gaussian fields
Updated several functions to allow for prediction and CV of
log-Gaussian fields. Updated functions:
predict.STmodel to compute temporal
averages, and return both prediction and variance of the
averages. Both for Gaussian and log-Gaussian data.
Upates: sparse-Matrices and temporal basis functions
Allows for sparse matrices in
makeSigmaNu; this reduces the memory footprint and
execution time for
Added function that does regression estimates of the
Altered computation of CV-statistics in
boxplot.SVDcv for illustration of CV-statistics
updateTrend.STmodel that also allows for temporal
trends defined using functions.
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
Major bug fixes:
predict.STmodel, predictions now always
uses the trend given in
object, ignoring the trend object
STdata. Prediction at dates in
computed using the smoothing function that defines the trend; see
updateTrend.STmodel for details.
summary.predCVSTmodel, code previously divided by
the wrong variance when computing adjusted R2 using the
summary.predCVSTmodel, code previously
returned statistics even for dates without observations when
now accounts for missing time-points when computing acf and pacf.
Added plot funcions/Minor fixes:
scatterPlot.predCVSTmodel for plotting
observations/residuals against covariates.
plot.density.mcmcSTmodel for plotting of MCMC
qqnorm.predCVSTmodel for plotting of data and
restart option to
allowing for restarts of optimisation in cases on bad
Minor changes/Bug fixes:
Fixed stupid misstake in
predictNaive that caused
computations to take unnecessarily long.
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
when the option
extra.dates is in use.
Fixed bug in
simulate.STmodel that caused
values when simulating at unobserved sites.
Fixed bug in
predict.STmodel that could cause
errors when predicting at unobserved sites.
Fixed bug in
predict.STmodel; these will now handle predictions
at locations with incomplete nugget covariates.
to avoid errors/warnings due to more complex nugget models.
Replaced warning in
n.basis=NULL with a message.
c.STmodel will now combine
objects with identical covariate scaling.
Changed the return of the variances for
Reduced the memory footprint of
Error checks in
predict.STmodel, combination of
objects with different covariate scaling is NOT
New plot function:
coef.estCVSTmodel functions that extract estimated
predictCV.STmodel can be specified using
lwd option to
A short introductory vignette as complement to the full tutorial.
predictNaive now works for only one locations.
detrendSTdata now works for different regions.
plotrix to suggested packages.
prediction for leave-one-out CV.
stop updateCovf crashing in Rscript/R CMD BATCH.
Minor bug fixes
Updated documentation and vignette
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
Minor updates - no user visible changes
First released version, short course at TIES-2010
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).
Johan Lindstrom, Adam Szpiro, Paul D. Sampson, Silas Bergen, Assaf P. Oron
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.
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