Package: spsur 1.0.2.1

spsur: Spatial Seemingly Unrelated Regression Models

A collection of functions to test and estimate Seemingly Unrelated Regression (usually called SUR) models, with spatial structure, by maximum likelihood and three-stage least squares. The package estimates the most common spatial specifications, that is, SUR with Spatial Lag of X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM). The methodology of these models can be found in next references: Mur, J., Lopez, F., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443>; Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2> and Lopez, F.A., Minguez, R. and Mur, J. (2020) <doi:10.1007/s00168-019-00914-1>.

Authors:Ana Angulo [aut], Fernando A Lopez [aut], Roman Minguez [aut, cre], Jesus Mur [aut]

spsur_1.0.2.1.tar.gz
spsur_1.0.2.1.zip(r-4.5)spsur_1.0.2.1.zip(r-4.4)spsur_1.0.2.1.zip(r-4.3)
spsur_1.0.2.1.tgz(r-4.4-any)spsur_1.0.2.1.tgz(r-4.3-any)
spsur_1.0.2.1.tar.gz(r-4.5-noble)spsur_1.0.2.1.tar.gz(r-4.4-noble)
spsur_1.0.2.1.tgz(r-4.4-emscripten)spsur_1.0.2.1.tgz(r-4.3-emscripten)
spsur.pdf |spsur.html
spsur/json (API)

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

Peer review:

Bug tracker:https://github.com/rominsal/spsur/issues

Datasets:
  • NCOVR.sf - Homicides in U.S. counties
  • Wspc - Spatial weight matrix for South-West Ohio Counties to estimate Spatial Phillips-Curve
  • spain.covid - Within/Exit mobility index and incidence COVID-19 at Spain provinces
  • spain.covid.sf - Spain geometry
  • spc - A classical Spatial Phillips-Curve

On CRAN:

11 exports 11 stars 1.66 score 113 dependencies 32 scripts 494 downloads

Last updated 2 years agofrom:75fc1a8d6f. Checks:OK: 1 NOTE: 4 WARNING: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 21 2024
R-4.5-winNOTEAug 21 2024
R-4.5-linuxNOTEAug 21 2024
R-4.4-winNOTEAug 21 2024
R-4.4-macNOTEAug 21 2024
R-4.3-winWARNINGAug 21 2024
R-4.3-macWARNINGAug 21 2024

Exports:dgp_spsurimpactspsurlmtestspsurlr_betaslrtestspsurspsur3slsspsurgs3slsspsurmlspsurtimewald_betaswald_deltas

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDataclassclassIntclicodacodetoolscolorspacecowplotcpp11DBIdeldirDerivdigestdoBydplyre1071evaluatefansifarverfastmapfontawesomeFormulafsgdatagenericsggplot2gluegmodelsgridExtragtablegtoolshighrhtmltoolsisobandjquerylibjsonliteKernSmoothknitrlabelinglatticeLearnBayeslifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigproxypurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackrlangrmarkdowns2sandwichsassscalessfspSparseMsparseMVNspatialregspDataspdepsphetstringistringrsurvivalTH.datatibbletidyrtidyselecttinytexunitsutf8vctrsviridisLitewithrwkxfunyamlzoo

Maximum Likelihood estimation of Spatial Seemingly Unrelated Regression models. A short Monte Carlo exercise with spsur and spse

Rendered frommontecarlo_spsur_spse.Rmdusingknitr::rmarkdownon Aug 21 2024.

Last update: 2022-04-22
Started: 2021-04-10

Spatial seemingly unrelated regression models. A comparison of spsur, spse and PySAL

Rendered fromspsur_pysal.Rmdusingknitr::rmarkdownon Aug 21 2024.

Last update: 2022-04-22
Started: 2021-04-10

spsur user guide

Rendered fromVignette_User_Guide.Rmdusingknitr::rmarkdownon Aug 21 2024.

Last update: 2022-04-22
Started: 2020-04-09

spsur vs spatialreg

Rendered fromspsur-vs-spatialreg.Rmdusingknitr::rmarkdownon Aug 21 2024.

Last update: 2022-04-22
Started: 2020-04-09

Readme and manuals

Help Manual

Help pageTopics
Generation of a random dataset with a spatial SUR structure.dgp_spsur
Direct, indirect and total effects estimated for a spatial SUR modelimpactspsur
Testing for the presence of spatial effects in Seemingly Unrelated Regressionslmtestspsur lmtestspsur.default lmtestspsur.formula
Likelihood ratio for testing homogeneity constraints on beta coefficients of the SUR equations.lr_betas
Likelihood Ratio tests for the specification of spatial SUR models.lrtestspsur
Methods for class spsuranova anova.spsur coef coef.spsur fitted fitted.spsur logLik logLik.spsur methods_spsur plot plot.spsur print print.spsur residuals residuals.spsur vcov vcov.spsur
Homicides in U.S. countiesNCOVR.sf
Print method for objects of class summary.spsur.print.summary.spsur
Within/Exit mobility index and incidence COVID-19 at Spain provincesspain.covid
Spain geometryspain.covid.sf
A classical Spatial Phillips-Curvespc
Spatial Seemingly Unrelated Regression Models.spsur
Three Stages Least Squares estimation,3sls, of spatial SUR models.spsur3sls
General Spatial 3SLS for systems of spatial equations.spsurgs3sls
Maximum likelihood estimation of spatial SUR model.spsurml
Estimation of SUR models for simple spatial panels (G=1).spsurtime
Summary of estimated objects of class _spsur_.summary.spsur
Wald tests on the _beta_ coefficients of the equation of the SUR modelwald_betas
Wald tests for spatial parameters coefficients.wald_deltas
Spatial weight matrix for South-West Ohio Counties to estimate Spatial Phillips-CurveWspc