Spatially-Clustered Data Analysis

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Documentation for package ‘SCDA’ version 0.0.2

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Data2010 Spatial dataset to replicate the results for 2010 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>
Data2020 Spatial dataset to replicate the results for 2020 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>)
Elbow_finder Automatically selects the optimal number of clusters based on elbow criterion.
listW List of 222 spatial weights (style = "W", zero.policy=TRUE) used in Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>)
SCSR_Estim Estimate spatially-clustered spatial regression models
SCSR_InfoCrit Automatically select the optimal number of clusters based on likelihood information criteria (i.e., AIC, BIC and HQC) for a given SCSR model.
SC_AMKM Spatial Clustering for sf data
SpatReg_Extract Extracts numerical values for the estimated regression parameters (i.e., spatial coefficients, regression coefficients, and residuals variance) for a given spatial regression model of class 'lm' or 'Sarlm'.
SpatReg_GoF Computes a set of goodness-of-fit indices (e.g., likelihood-based information criteria, Wald and LR test, Moran's I statistic) for a given spatial regression model of class 'lm' or 'Sarlm'.
SpatReg_Perf Computes a set of in-sample performance metrics (i.e., AIC, BIC, RMSE, Sigma, and Pseudo R^2) for a given spatial regression model of class 'lm' or 'Sarlm'.
SpatReg_PseudoR2 Computes the Pseudo R^2 metric for a given spatial regression model of class 'lm' or 'Sarlm'.