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.
Usage
lcorrel(DFrame, bw, Coords)
Arguments
DFrame
A numeric Data Frame of at least two variables
bw
A positive value between 0 and 1 to define the proportion of the total observations for the local sample for which each time the local coefficients are calculated for. This can be also the result of bandwidth selection algorithms of local regression techniques such as the Geographically Weighted Regression (GWR)
Coords
a numeric matrix or vector or data frame of two columns giving the X,Y coordinates of the observations (data points or geometric centroids)
Details
The degrees of freedom for the local t-student test is Round(bw * Number of Observations) - 2.
Value
lcorrel returns a list of 7 Data Frames
LPCC
A numeric data frame with the Local Pearson Correlation Coefficients (LPCCs) for each possible pair of the input variables in DFrame
LPCC_t
A numeric data frame with the t-student test statistics for all LPCCs
LPCC_sig
A numeric data frame with level of significance (p-value) for all LPCCs
LPCC_sig_BF
A numeric data frame with level of significance (p-value) for all LPCCs adjusted using the conservative Bonferroni correction to account for false positives under the multiple hypothesis testing theory
GWPCC
A numeric data frame with the Geographically Weighted Pearson Correlation Coefficients (GWPCCs) for each possible pair of the input variables in DFrame
GWPCC_sig
A numeric data frame with level of significance (p-value) for all GWPCCs
GWPCC_sig_BF
A numeric data frame with level of significance (p-value) for all GWPCCs adjusted using the conservative Bonferroni correction to account for false positives under the multiple hypothesis testing theory
Author(s)
Stamatis Kalogirou <skalo@hua.gr>
References
Kalogirou, S., 2012, Testing local versions of correlation coefficients, Review of Regional Research - Jahrbuch fur Regionalwissenschaft, 32, 1, pp. 45 - 61, doi: 10.1007/s10037-011-0061-y.
Kalogirou, S., 2013, Testing geographically weighted multicollinearity diagnostics, GISRUK 2013, Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UK, 3-5 April 2013.http://gisc.gr/docs/sk_papers/2_7_Kalogirou_2013.pdf
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