spGini.w
(Package: lctools) :
Spatial Gini coefficient with a given weights matrix
This is the implementation of the spatial decomposition of the Gini coefficient introduced by Rey and Smith (2013) as in the function spGini. In this function, the calculation of the global Gini and the two components of the spatial Gini is performed using matrix algebra and a ready made weights matrix. Thus, it is possible to use weighting schemes other than those currently supported in spGini.
● Data Source:
CranContrib
● Keywords: Gini, Spatial Gini, Spatial Inequality, Spatial autocorrelation
● Alias: spGini.w
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gw.zi.cv
(Package: lctools) :
A specific version of the function gw.zi
A specific version of the function gw.zi returning only the leave-one-out Cross Validation (CV) score. gw.zi.cv exludes the observation for which a sub-model fits.
● Data Source:
CranContrib
● Keywords: GWZIPR, local regression
● Alias: gw.zi.cv
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moransI
(Package: lctools) :
Moran's I classic statistic for assessing spatial autocorrelation
Moran's I is one of the oldest statistics used to examine spatial autocorrelation. This global statistic was first proposed by Moran (1948, 1950). Later, Cliff and Ord (1973, 1981) present a comprehensive work on spatial autocorrelation and suggested a formula to calculate the I which is now used in most textbooks and software:
● Data Source:
CranContrib
● Keywords: Moran's I, Moran's I significance test, Spatial autocorrelation
● Alias: moransI
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gwr.cv
(Package: lctools) :
A specific version of the function gwr
A specific version of the function gwr returning only the leave-one-out Cross Validation (CV) score. gwr.cv exludes the observation for which a sub-model fits.
● Data Source:
CranContrib
● Keywords: GWR, local regression
● Alias: gwr.cv
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spGini
(Package: lctools) :
Spatial Gini coefficient
This is the implementation of the spatial decomposition of the Gini coefficient introduced by Rey and Smith (2013). The function calculates the global Gini and the two components of the spatial Gini: the inequality among nearest (geographically) neighbours and the inequality of non-neighbours. Three weighted schemes are currently supported: binary, bi-square and row standardised bi-square.
● Data Source:
CranContrib
● Keywords: Gini, Spatial Gini, Spatial Inequality, Spatial autocorrelation
● Alias: spGini
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gw.zi.light
(Package: lctools) :
A light version of the Geographically Weighted Zero Inflated Poisson Regression (GWZIPR)
This function allows for the calibration of a local model using the Geographically Weighted Zero Inflated Poisson Regression (GWZIPR) but reports and returns fewer results compared to the function gw.zi.
● Data Source:
CranContrib
● Keywords: GWZIPR, local regression
● Alias: gw.zi.light
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lcorrel
(Package: lctools) :
Local Pearson and GW Pearson Correlation
This function computes Local Pearson and Geographically Weighted Pearson Correlation Coefficients and tests for their statistical significance. Because the local significant tests are not independent, under the multiple hypotheses testing theory, a Bonferroni correction of the local coefficients takes place. The function results in tables with results for all possible pairs of the input variables.
● Data Source:
CranContrib
● Keywords: GWPCC, LPCC, local multi-collinearity
● Alias: lcorrel
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gw.zi
(Package: lctools) :
Geographically Weighted Zero Inflated Poisson Regression (GWZIPR)
This function allows for the calibration of a local model using the Geographically Weighted Zero Inflated Poisson Regression (GWZIPR).
● Data Source:
CranContrib
● Keywords: GWZIPR, local regression
● Alias: gw.zi
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gw.zi.bw
(Package: lctools) :
Optimal bandwidth estimation for Geographically Weighted Zero Inflated Poisson Regression (GWZIPR)
This function helps choosing the optimal bandwidth for the Geographically Weighted Zero Inflated Poisson Regression (GWZIPR).
● Data Source:
CranContrib
● Keywords: GWR, optimal bandwidth
● Alias: gw.zi.bw
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FLQ
(Package: lctools) :
Focal Location Quotient
This is the implementation of the Focal Location Quotients proposed by Cromley and Hanink (2012). The function calculates the standard LQ and the Focal LQ based on a kernel of nearest neighbours. Two weighted schemes are currently supported: binary and bi-square weights for a fixed number of nearest neighbours set by the user.
● Data Source:
CranContrib
● Keywords: FLQ, Focal Location Quotient, LQ, Location Quotient
● Alias: FLQ
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