NEWS/ChangeLog
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1.0.1 2020-05-04
All models of mgwrsar package based on local linear regression can now be estimated using a target points set. Several functions that allows to choose an optimal set of target points to obtain a faster approximation of GWR coefficients has been added.
Predictions on new data can now be done using the jacknife estimation method instead of spatial extrapolation of local coefficients from a preliminarily estimated model. Only the optimal value of the bandwidth modeled from the initial data is then used. In the function 'predict_mgwrsar', if the parameter method_pred = 'TP' (default), the prediction is done by recalculating a MGWRSAR model with the new data as target points keeping the bandwidth at the optimal value chosen with the training data, otherwise if method_pred= ('tWtp_model', 'model', 'sheppard') then a matrix is used for the spatial extrapolation of the estimated coefficients, and prediction are done using these extrapolated coefficients (as in the previous version of mgwrsar package).
'KNN' function is deprecated and replaced by 'kernel_matW' function that allows to build spatial weight matrix and interaction matrix based on General Kernel Product. In kernel_matW function it's possible to specify the maximum number of neighbors to consider in gaussian kernel (rough gaussian kernel) to increase speed and sparsity of weights matrix.
Fast computation of local OLS coefficients in the previous version of mgwrsar package (0.1) uses non pivotal computation that may provides undesirable results in presence of strong colinearity. The RCCP 'fastlmLLT_C' function has been replaced by R native lm.fit function in this realease.
A new ploting function has been added: plot_effect is a function that plots the effect of a variable X_k with spatially varying coefficient, i.e X_k * Beta_k(u_i,v_i) for comparing the magnitude of effects of between variables
0.1 2018-05-11