| multiscale_gwr {mgwrsar} | R Documentation |
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.
Description
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.
Usage
multiscale_gwr(formula,data,coords,Model = 'GWR',kernels='bisq',
control=list(SE=FALSE,adaptive=TRUE,NN=800,isgcv=FALSE),init='GWR',maxiter=100,
nstable=6,crit=0.000001,doMC=FALSE,ncore=1,HF=NULL,H0=NULL,model=NULL)
Arguments
formula |
A formula. |
data |
A dataframe. |
coords |
default NULL, a dataframe or a matrix with coordinates. |
Model |
The type of model: Possible values are "GWR" (default), and "MGWRSAR_1_0_kv". See Details for more explanation. |
kernels |
A vector containing the kernel types. Possible types: rectangle ("rectangle"), bisquare ("bisq"), tricube ("tcub"), epanechnikov ("epane"), gaussian("gauss")). |
control |
a list of extra control arguments, see MGWRSAR help. |
init |
starting model (lm or GWR) |
maxiter |
maximum number of iterations in the back-fitting procedure. |
nstable |
required number of consecutive unchanged optimal bandwidth (by covariate) before leaving optimisation of bandwidth size, default 3. |
crit |
value to terminate the back-fitting iterations (ratio of change in RMSE) |
doMC |
A boolean for Parallel computation, default FALSE. |
ncore |
number of CPU cores for parallel computation, default 1. |
HF |
if available, a vector containing the optimal bandwidth parameters for each covariate, default NULL. |
H0 |
A bandwidth value for the starting GWR model, default NULL. |
model |
A previous model estimated using multiscale_gwr function, default NULL. |
Value
Return an object of class mgwrsar with at least the following components:
- Betav
matrix of coefficients of dim(n,kv) x kv.
- Betac
vector of coefficients of length kc.
- Model
The sum of square residuals.
- Y
The dependent variable.
- XC
The explanatory variables with constant coefficients.
- XV
The explanatory variables with varying coefficients.
- X
The explanatory variables.
- W
The spatial weight matrix for spatial dependence.
- isgcv
if gcv has been computed.
- edf
The estimated degrees of freedom.
- formula
The formula.
- data
The dataframe used for computation.
- Method
The type of model.
- coords
The spatial coordinates of observations.
- H
A vector of bandwidths.
- fixed_vars
The names of constant coefficients.
- kernels
The kernel vector.
- SSR
The sum of square residuals.
- residuals
The vector of residuals.
- fit
the vector of fitted values.
- sev
local standard error of parameters.
- get_ts
Boolean, if trace of hat matrix Tr(S) should be stored.
- NN
Maximum number of neighbors for weights computation
See Also
tds_mgwr, bandwidths_mgwrsar, summary_mgwrsar, plot_mgwrsar, predict_mgwrsar
Examples
library(mgwrsar)
mysimu<-simu_multiscale(n=1000)
mydata=mysimu$mydata
coords=mysimu$coords
model_multiscale<-multiscale_gwr(formula=as.formula('Y~X1+X2+X3'),data=mydata,
coords=coords,Model = 'GWR',kernels='bisq',control=list(SE=FALSE,
adaptive=TRUE,NN=900,isgcv=FALSE),init='GWR',nstable=6,crit=0.000001)
summary_mgwrsar(model_multiscale)