Package: spup 1.4-0

spup: Spatial Uncertainty Propagation Analysis

Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally.

Authors:Kasia Sawicka [aut, cre], Gerard Heuvelink [aut], Dennis Walvoort [aut], Stefan van Dam [ctb], Damiano Luzzi [ctb]

spup_1.4-0.tar.gz
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spup_1.4-0.tgz(r-4.6-any)spup_1.4-0.tgz(r-4.5-any)
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spup_1.4-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spup/json (API)

# Install 'spup' in R:
install.packages('spup', repos = c('https://ksawicka.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ksawicka/spup/issues

Datasets:
  • dem30m - Digital Elevation Model of Zlatibor region in Serbia.
  • dem30m_sd - Standard deviation of Digital Elevation Model of Zlatibor region in Serbia.
  • OC - Soil organic carbon content in a south area (33 x 33km) of lake Alaotra in Madagascar.
  • OC_sd - Standard deviation of soil organic carbon content in a south area (33 x 33km) of lake Alaotra in Madagascar.
  • TN - Soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar.
  • TN_sd - Standard deviation of soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar.
  • woon - Neighbourhood in Rotterdam.

On CRAN:

Conda:

monte-carlospatialuncertainty-analysisuncertainty-propagation

6.41 score 10 stars 64 scripts 321 downloads 14 exports 33 dependencies

Last updated from:f6434a87a2. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK307
source / vignettesOK877
linux-releaseOK296
macos-releaseOK325
macos-oldrelOK217
windows-develOK290
windows-releaseOK265
windows-oldrelOK256
wasm-releaseOK135

Exports:defineMUMdefineUMexecutablegenSamplelist_depthmakeCRMmean_MC_sgdfpropagatequantile_MC_sgdfrendersd_MC_sgdftemplatevar_MC_sgdfvarcov

Dependencies:abindclassclassIntcliDBIe1071FNNgluegstatintervalsKernSmoothlatticelifecyclemagrittrmvtnormproxypurrrrasterRcpprlangs2sfsftimespspacetimestarsterraunitsvctrswhiskerwkxtszoo

Spatial Uncertainty Propagation Analysis
Case study with calling external model | Introduction | Monte Carlo methodology for uncertainty analysis | Uncertainty propagation analysis with 'spup' | Modify ASCII input files - rendering | Running external models | Acknowledgements
Rendered fromExternalModel_v2.Rmdusingknitr::rmarkdown

Last update: 2018-07-17
Started: 2017-04-03

Spatial Uncertainty Propagation Analysis
Case study with categorical data - calculating tax depending on a building function | Introduction | Adapted Monte Carlo methodology for spatial uncertainty analysis with a categorical variable | Building function uncertainty propagation analysis with 'spup' | Preliminaries - load and view the data | Define uncertainty model (UM) for the building function | Generate possible realities of the building function | Uncertainty propagation through the model that calculates tax revenue for the neighbourhood | Acknowledgements
Rendered fromRotterdam.Rmdusingknitr::rmarkdown

Last update: 2018-07-17
Started: 2017-01-30

Spatial Uncertainty Propagation Analysis
Case study with spatially variable standard deviation - slope calculations | Introduction/Problem definition | Monte Carlo methodology for spatial uncertainty analysis with spatially variable sd | DEM uncertainty propagation analysis with 'spup' | Preliminaries - load and view the data | Define uncertainty model (UM) for elevation | Generate possible realities of DEM | Uncertainty propagation through the model that calculates slope using elevation as input | Visualization of results | Acknowledgements | References
Rendered fromDEM_v3.Rmdusingknitr::rmarkdown

Last update: 2018-07-17
Started: 2017-02-08

Spatial Uncertainty Propagation Analysis
Case study with cross-correlated variables | Introduction/Problem definition | Monte Carlo methodology for spatial uncertainty analysis with spatially cross-correlated variables | C/N uncertainty propagation analysis with 'spup' | Preliminaries - load and view the data | Define uncertainty model (UM) for the cross-correlated OC and TN | Generate possible realities of OC and TN | Uncertainty propagation through the model that calculates C/N ratio | Visualization of results | Acknowledgements | References
Rendered fromCN_v2.Rmdusingknitr::rmarkdown

Last update: 2018-07-17
Started: 2017-06-15

Readme and manuals

Help Manual

Help pageTopics
Simple check if distribution provided in defineUM() belongs to a list of supported distributions.check_distribution
Simple check if class of provided object is Spatialcheck_if_Spatial
Converting a spatial correlogram model to a variogram modelcrm2vgm
Define Mulivariate Uncertainty ModeldefineMUM
Define an uncertainty model for a single variabledefineUM
Digital Elevation Model of Zlatibor region in Serbia.dem30m
Standard deviation of Digital Elevation Model of Zlatibor region in Serbia.dem30m_sd
Sampling from a given distributiondistribution_sampling
Sampling from a given distributiondistribution_sampling_raster
Wrapper function for calling executables in Rexecutable
Sampling from a given distributionfind_strata
Methods for generating Monte Carlo realizations from uncertain inputs.genSample
Generating Monte Carlo sample from a list of uncertain objects that are cross-correlated.genSample.JointNumericSpatial
Generating sample from cross-correlated variables described by a scalar.genSample.JointScalar
Generating Monte Carlo sample from an uncertain object of a class 'MarginalCategoricalSpatial'genSample.MarginalCategoricalSpatial
Generating Monte Carlo sample from an uncertain object of a class 'MarginalNumericSpatial'genSample.MarginalNumericSpatial
Generating Monte Carlo sample from an uncertain object of a class 'MarginalScalar'genSample.MarginalScalar
Function to find the level of list nestinglist_depth
Defining a spatial correlogram modelmakeCRM makecrm
mean() function for MC sample saved in a SpatialGridDataFramemean_MC_sgdf
Soil organic carbon content in a south area (33 x 33km) of lake Alaotra in Madagascar.OC
Standard deviation of soil organic carbon content in a south area (33 x 33km) of lake Alaotra in Madagascar.OC_sd
Plots correlogram modelplot.SpatialCorrelogramModel
Print method for class "template."print.template
Propagation functionpropagate
quantile() function for MC sample saved in a SpatialGridDataFramequantile_MC_sgdf
Rendering templaterender
Render method for "character" class.render.character
Render method for "template" class.render.template
sd() function for MC sample saved in a SpatialGridDataFramesd_MC_sgdf
spup - Package for spatial uncertainty propagationspup--pkg _PACKAGE
Stratified sampling for spatial variablesstratsamp
Constructor for class "template".template
Soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar.TN
Standard deviation of soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar.TN_sd
var() function for MC sample saved in a SpatialGridDataFramevar_MC_sgdf
Calculate variance covariance matrixvarcov
Convert vgm to crmvgm2crm
Neighbourhood in Rotterdam.woon