multiscale_gwr This function adapts the multiscale Geographically Weighted Regression (GWR) methodology proposed by Fotheringam et al. in 2017, employing a backward fitting procedure within the MGWRSAR subroutines. The consecutive bandwidth optimizations are performed by minimizing the corrected Akaike criteria.
multiscale_gwr(formula,data,coords,kernels='bisq',init='GWR',
maxiter=20,nstable=6,tolerance=0.000001,doMC=FALSE,ncore=1,HF=NULL,
H0=NULL,H2=NULL,Model=NULL,model=NULL,get_AICg=FALSE,verbose=FALSE,
control=list(SE=FALSE,adaptive=TRUE,NN=800,isgcv=FALSE,family=gaussian()))Return an object of class mgwrsar
A formula.
A dataframe.
default NULL, a dataframe or a matrix with coordinates.
A vector containing the kernel types. Possible types: rectangle ("rectangle"), bisquare ("bisq"), tricube ("tcub"), epanechnikov ("epane")
starting model (lm or GWR)
maximum number of iterations in the back-fitting procedure.
required number of consecutive unchanged optimal bandwidth (by covariate) before leaving optimisation of bandwidth size, default 3.
value to terminate the back-fitting iterations (ratio of change in RMSE)
A boolean for Parallel computation, default FALSE.
number of CPU cores for parallel computation, default 1.
if available, a vector containing the optimal bandwidth parameters for each covariate, default NULL.
A bandwidth value for the starting GWR model, default NULL.
A bandwidth temporal value for the starting GWR model, default NULL.
Type of Model.
A previous model estimated using multiscale_gwr function, default NULL
Boolean, should Global AICc be estimated.
Boolean, verbose mode.
a list of extra control arguments, see MGWRSAR help.