R/GeneralizedGWR.r

In GWmodel: Geographically-Weighted Models

##Generalized  GWR functions (basic non-parametric)
#Author: Binbin Lu
#Referenced to Tomoki Nakaya's c++ code and Chris' R code

ggwr.basic<-function(formula, data, regression.points, bw, family ="poisson", kernel="bisquare",
              adaptive=FALSE, cv=T, tol=1.0e-5, maxiter=20, p=2, theta=0, longlat=F, dMat,dMat1)
{
 ##Record the start time
    timings <- list()
    timings[["start"]] <- Sys.time()
    ###################################macth the variables
    this.call <- match.call()
    p4s <- as.character(NA)
    #####Check the given data frame and regression points
    #####Regression points
    if (missing(regression.points))
    {
        rp.given <- FALSE
        regression.points <- data
        hatmatrix<-T
    }
    else
    {
        rp.given <- TRUE
        hatmatrix<-F
    }
    ##Data points{
    if (is(data, "Spatial"))
    {
        p4s <- proj4string(data)
        dp.locat<-coordinates(data)
        data <- as(data, "data.frame")
    }
    else
    {
        stop("Given regression data must be Spatial*DataFrame")
    }

    ####################
    ######Extract the data frame
    ####Refer to the function lm
    mf <- match.call(expand.dots = FALSE)
    m <- match(c("formula", "data"), names(mf), 0L)

    mf <- mf[c(1L, m)]
    mf$drop.unused.levels <- TRUE
    mf[[1L]] <- as.name("model.frame")
    mf <- eval(mf, parent.frame())
    mt <- attr(mf, "terms")
    y <- model.extract(mf, "response")
    x <- model.matrix(mt, mf)
    ############################################
    var.n<-ncol(x)
    if(is(regression.points, "Spatial"))
        rp.locat<-coordinates(regression.points)
    else if(is.numeric(regression.points)&&dim(regression.points)[2]==2)
    {
        rp.locat <- regression.points
    }
    else
        stop("Please use the correct regression points for model calibration!")

    rp.n<-nrow(rp.locat)
    dp.n<-nrow(data)
    betas <-matrix(nrow=rp.n, ncol=var.n)
    betas1<- betas
    if(hatmatrix)
    {
        betas.SE <-matrix(nrow=rp.n, ncol=var.n)
        betas.TV <-matrix(nrow=rp.n, ncol=var.n)
        ##S: hatmatrix
        S<-matrix(nrow=dp.n,ncol=dp.n)
    }
    #C.M<-matrix(nrow=dp.n,ncol=dp.n)
    idx1 <- match("(Intercept)", colnames(x))
    if(!is.na(idx1))
        colnames(x)[idx1]<-"Intercept"
    colnames(betas) <- colnames(x)
    #colnames(betas)[1]<-"Intercept"
    ####################################################GWR
    #########Distance matrix is given or not

    if (missing(dMat))
    {
        DM.given<-F
        if(dp.n + rp.n <= 10000)
        {
            dMat <- gw.dist(dp.locat=dp.locat, rp.locat=rp.locat, p=p, theta=theta, longlat=longlat)
            DM.given<-T
        }
    }
    else
    {
        DM.given<-T
        dim.dMat<-dim(dMat)
        if (dim.dMat[1]!=dp.n||dim.dMat[2]!=rp.n)
            stop("Dimensions of dMat are not correct")
    }
    if(missing(dMat1))
    {
        DM1.given<-F
        if(hatmatrix&&DM.given)
        {
            dMat1 <- dMat
            DM1.given<-T
        }
        else
        {
            if(dp.n < 8000)
            {
                dMat1 <- gw.dist(dp.locat=dp.locat, rp.locat=dp.locat, p=p, theta=theta, longlat=longlat)
                DM1.given<-T
            }
        }
    }
    else
    {
        DM1.given<-T
        dim.dMat1<-dim(dMat1)
        if (dim.dMat1[1]!=dp.n||dim.dMat1[2]!=dp.n)
            stop("Dimensions of dMat are not correct")
    }
    ####Generate the weighting matrix
    #############Calibration the model
    W1.mat<-matrix(numeric(dp.n*dp.n),ncol=dp.n)
    W2.mat<-matrix(numeric(dp.n*rp.n),ncol=rp.n)
    for (i in 1:dp.n)
    {
        if (DM1.given)
            dist.vi<-dMat1[,i]
        else
        {
            dist.vi<-gw.dist(dp.locat=dp.locat, focus=i, p=p, theta=theta, longlat=longlat)
        }
        W.i<-gw.weight(dist.vi,bw,kernel,adaptive)
        W1.mat[,i]<-W.i
    }
    if (rp.given)
    {
        for (i in 1:rp.n)
        {
            if (DM.given)
                dist.vi<-dMat[,i]
            else
            {
                dist.vi<-gw.dist(dp.locat, rp.locat, focus=i, p, theta, longlat)
            }
            W.i<-gw.weight(dist.vi,bw,kernel,adaptive)
            W2.mat[,i]<-W.i
        }
    }
    else
        W2.mat<-W1.mat

    ##model calibration
    if(family=="poisson")
        res1<-gwr.poisson(y,x,regression.points,W1.mat,W2.mat,hatmatrix,tol, maxiter)
    if(family=="binomial")
        res1<-gwr.binomial(y,x,regression.points,W1.mat,W2.mat,hatmatrix,tol, maxiter)
    ####################################
    CV <- numeric(dp.n)
    if(hatmatrix && cv)
    {
        CV <- ggwr.cv.contrib(bw, x, y,family, kernel,adaptive, dp.locat, p, theta, longlat,dMat)
    }
    ####encapsulate the GWR results
    GW.arguments<-list()
    GW.arguments<-list(formula=formula,rp.given=rp.given,hatmatrix=hatmatrix,bw=bw, family=family,
                       kernel=kernel,adaptive=adaptive, p=p, theta=theta, longlat=longlat,DM.given=DM1.given)

    timings[["stop"]] <- Sys.time()
    ##############
    res<-list(GW.arguments=GW.arguments,GW.diagnostic=res1$GW.diagnostic,glms=res1$glms,SDF=res1$SDF,CV=CV,timings=timings,this.call=this.call)
    class(res) <-"ggwrm"
    invisible(res) 
}

# This version of this function is kept to make the code work with the early versions of GWmodel (before 2.0-1)
gwr.generalised<-function(formula, data, regression.points, bw, family ="poisson", kernel="bisquare",
              adaptive=FALSE, cv=T, tol=1.0e-5, maxiter=20, p=2, theta=0, longlat=F, dMat,dMat1)
{
  ##Record the start time
    timings <- list()
    timings[["start"]] <- Sys.time()
    ###################################macth the variables
    this.call <- match.call()
    p4s <- as.character(NA)
    #####Check the given data frame and regression points
    #####Regression points
    if (missing(regression.points))
    {
        rp.given <- FALSE
        regression.points <- data
        hatmatrix<-T
    }
    else
    {
        rp.given <- TRUE
        hatmatrix<-F
    }
    ##Data points{
    if (is(data, "Spatial"))
    {
        p4s <- proj4string(data)
        dp.locat<-coordinates(data)
        data <- as(data, "data.frame")
    }
    else
    {
        stop("Given regression data must be Spatial*DataFrame")
    }

    ####################
    ######Extract the data frame
    ####Refer to the function lm
    mf <- match.call(expand.dots = FALSE)
    m <- match(c("formula", "data"), names(mf), 0L)

    mf <- mf[c(1L, m)]
    mf$drop.unused.levels <- TRUE
    mf[[1L]] <- as.name("model.frame")
    mf <- eval(mf, parent.frame())
    mt <- attr(mf, "terms")
    y <- model.extract(mf, "response")
    x <- model.matrix(mt, mf)
    ############################################
    var.n<-ncol(x)
    if(is(regression.points, "Spatial"))
        rp.locat<-coordinates(regression.points)
    else if(is.numeric(regression.points)&&dim(regression.points)[2]==2)
    {
        rp.locat <- regression.points
    }
    else
        stop("Please use the correct regression points for model calibration!")

    rp.n<-nrow(rp.locat)
    dp.n<-nrow(data)
    betas <-matrix(nrow=rp.n, ncol=var.n)
    betas1<- betas
    if(hatmatrix)
    {
        betas.SE <-matrix(nrow=rp.n, ncol=var.n)
        betas.TV <-matrix(nrow=rp.n, ncol=var.n)
        ##S: hatmatrix
        S<-matrix(nrow=dp.n,ncol=dp.n)
    }
    #C.M<-matrix(nrow=dp.n,ncol=dp.n)
    idx1 <- match("(Intercept)", colnames(x))
    if(!is.na(idx1))
        colnames(x)[idx1]<-"Intercept"
    colnames(betas) <- colnames(x)
    #colnames(betas)[1]<-"Intercept"
    ####################################################GWR
    #########Distance matrix is given or not

    if (missing(dMat))
    {
        DM.given<-F
        if(dp.n + rp.n <= 10000)
        {
            dMat <- gw.dist(dp.locat=dp.locat, rp.locat=rp.locat, p=p, theta=theta, longlat=longlat)
            DM.given<-T
        }
    }
    else
    {
        DM.given<-T
        dim.dMat<-dim(dMat)
        if (dim.dMat[1]!=dp.n||dim.dMat[2]!=rp.n)
            stop("Dimensions of dMat are not correct")
    }
    if(missing(dMat1))
    {
        DM1.given<-F
        if(hatmatrix&&DM.given)
        {
            dMat1 <- dMat
            DM1.given<-T
        }
        else
        {
            if(dp.n < 8000)
            {
                dMat1 <- gw.dist(dp.locat=dp.locat, rp.locat=dp.locat, p=p, theta=theta, longlat=longlat)
                DM1.given<-T
            }
        }
    }
    else
    {
        DM1.given<-T
        dim.dMat1<-dim(dMat1)
        if (dim.dMat1[1]!=dp.n||dim.dMat1[2]!=dp.n)
            stop("Dimensions of dMat are not correct")
    }
    ####Generate the weighting matrix
    #############Calibration the model
    W1.mat<-matrix(numeric(dp.n*dp.n),ncol=dp.n)
    W2.mat<-matrix(numeric(dp.n*rp.n),ncol=rp.n)
    for (i in 1:dp.n)
    {
        if (DM1.given)
            dist.vi<-dMat1[,i]
        else
        {
            dist.vi<-gw.dist(dp.locat=dp.locat, focus=i, p=p, theta=theta, longlat=longlat)
        }
        W.i<-gw.weight(dist.vi,bw,kernel,adaptive)
        W1.mat[,i]<-W.i
    }
    if (rp.given)
    {
        for (i in 1:rp.n)
        {
            if (DM.given)
                dist.vi<-dMat[,i]
            else
            {
                dist.vi<-gw.dist(dp.locat, rp.locat, focus=i, p, theta, longlat)
            }
            W.i<-gw.weight(dist.vi,bw,kernel,adaptive)
            W2.mat[,i]<-W.i
        }
    }
    else
        W2.mat<-W1.mat

    ##model calibration
    if(family=="poisson")
        res1<-gwr.poisson(y,x,regression.points,W1.mat,W2.mat,hatmatrix,tol, maxiter)
    if(family=="binomial")
        res1<-gwr.binomial(y,x,regression.points,W1.mat,W2.mat,hatmatrix,tol, maxiter)
    ####################################
    CV <- numeric(dp.n)
    if(hatmatrix && cv)
    {
        CV <- ggwr.cv.contrib(bw, x, y,family, kernel,adaptive, dp.locat, p, theta, longlat,dMat)
    }
    ####encapsulate the GWR results
    GW.arguments<-list()
    GW.arguments<-list(formula=formula,rp.given=rp.given,hatmatrix=hatmatrix,bw=bw, family=family,
                       kernel=kernel,adaptive=adaptive, p=p, theta=theta, longlat=longlat,DM.given=DM1.given)

    timings[["stop"]] <- Sys.time()
    ##############
    res<-list(GW.arguments=GW.arguments,GW.diagnostic=res1$GW.diagnostic,glms=res1$glms,SDF=res1$SDF,CV=CV,timings=timings,this.call=this.call)
    class(res) <-"ggwrm"
    invisible(res)
}



############ Possipon GWGLM
gwr.poisson<-function(y,x,regression.points,W1.mat,W2.mat,hatmatrix,tol=1.0e-5, maxiter=500)
{
    p4s <- as.character(NA)
    if (is(regression.points, "Spatial"))
    {
      p4s <- proj4string(regression.points)
    }
    ############################################
    ##Generalized linear regression
    glms<-glm.fit(x, y, family = poisson()) 
    null.dev <- glms$null.deviance
    glm.dev <-glms$deviance
    glm.pseudo.r2 <- 1- glm.dev/null.dev 
    glms$pseudo.r2 <- glm.pseudo.r2
    var.n<-ncol(x)
    dp.n<-nrow(x)
    ########change the aic
    glms$aic <- glm.dev + 2*var.n
    glms$aicc <- glm.dev + 2*var.n + 2*var.n*(var.n+1)/(dp.n-var.n-1)
    ############################################
    if(is(regression.points, "Spatial"))
    	 rp.locat<-coordinates(regression.points)
    else
       rp.locat <- regression.points
    rp.n<-nrow(rp.locat)
    betas <- matrix(nrow=rp.n, ncol=var.n)
    betas1<- matrix(nrow=dp.n, ncol=var.n)
    betas.SE <-matrix(nrow=dp.n, ncol=var.n)
    betas.TV <-matrix(nrow=dp.n, ncol=var.n)
    ##S: hatmatrix
    S<-matrix(nrow=dp.n,ncol=dp.n)
    #C.M<-matrix(nrow=dp.n,ncol=dp.n)
    colnames(betas) <- colnames(x)
   # colnames(betas)[1]<-"Intercept" 
    ####################################
    ##model calibration
    
    it.count <- 0
    llik <- 0.0
    mu <- y + 0.1
    nu <- log(mu)
    cat(" Iteration    Log-Likelihood\n=========================\n")
    wt2 <- rep(1,dp.n)
    repeat {
     y.adj <- nu + (y - mu)/mu
     for (i in 1:dp.n)
     {
        W.i<-W1.mat[,i]
        gwsi<-gw_reg(x,y.adj,W.i*wt2,hatmatrix=F,i)
        betas1[i,]<-gwsi[[1]]
     }
     nu <- gw_fitted(x,betas1)
     mu <- exp(nu)
     old.llik <- llik
     #llik <- sum(y*nu - mu - log(gamma(y+1)))
	   llik <- sum(dpois(y, mu, log = TRUE))
     cat(paste("   ",formatC(it.count,digits=4,width=4),"    ",formatC(llik,digits=4,width=7),"\n"))
     if (abs((old.llik - llik)/llik) < tol) break
     wt2 <- as.numeric(mu)
     it.count <- it.count+1
     if (it.count == maxiter) break}
     GW.diagnostic <- NULL
     gw.dev <- 0
     for(i in 1:dp.n)
     {
       if(y[i]!=0)
           gw.dev <- gw.dev + 2*(y[i]*(log(y[i]/mu[i])-1)+mu[i])
       else
           gw.dev <- gw.dev + 2* mu[i]
     }
     
     #gw.dev <- 2*sum(y*log(y/mu)-(y-mu))     
     #local.dev <- numeric(dp.n)     
     #local.null.dev <- numeric(dp.n)
     #local.pseudo.r2 <- numeric(dp.n) 
     if(hatmatrix)
     { 
        for (i in 1:dp.n)
        { 
          W.i<-W2.mat[,i]
          gwsi<-gw_reg(x,y.adj,W.i*wt2,hatmatrix,i)
          betas[i,]<-gwsi[[1]]
          ##Add the smoother y.adjust, see equation (30) in Nakaya(2005)
          #S[i,]<-gwsi[[2]]
          S[i,]<-gwsi[[2]]
          Ci<-gwsi[[3]]      
          #betas.SE[i,]<-diag(Ci%*%t(Ci)) 
          invwt2 <- 1.0 /as.numeric(wt2)
          betas.SE[i,] <- diag((Ci*invwt2) %*% t(Ci))# diag(Ci/wt2%*%t(Ci))  #see Nakaya et al. (2005)
        }
        tr.S<-sum(diag(S))
        ####trace(SWS'W^-1) is used here instead of tr.StS
        #tr.StS<-sum(S^2)
        tr.StS<- sum(diag(S%*%diag(wt2)%*%t(S)%*% diag(1/wt2)))
        ###edf is different from the definition in Chris' code
        edf<-dp.n-2*tr.S+tr.StS
        yhat<-gw_fitted(x, betas)
        residual<-y-exp(yhat)
        ########rss <- sum((y - gwr.fitted(x,b))^2)
        #rss <- sum((y-exp(yhat))^2)
        #sigma.hat <- rss/edf
        #sigma.aic <- rss/dp.n
        for(i in 1:dp.n)
        {
           #betas.SE[i,]<-sqrt(sigma.hat*betas.SE[i,])
           betas.SE[i,]<-sqrt(betas.SE[i,])
           betas.TV[i,]<-betas[i,]/betas.SE[i,]  
        }
        #AICc <- -2*llik + 2*tr.S*dp.n/(dp.n-tr.S-2) 
        #AICc <- -2*llik + 2*tr.S + 2*tr.S*(tr.S+1)/(dp.n-tr.S-1)  # This is generic form of AICc (TN)
        AIC <- gw.dev + 2*tr.S
        AICc <- gw.dev + 2*tr.S + 2*tr.S*(tr.S+1)/(dp.n-tr.S-1) 
        #yss.g <- sum((y - mean(y))^2)
        #gw.R2<-1-rss/yss.g; ##R Square valeu
        #gwR2.adj<-1-(1-gw.R2)*(dp.n-1)/(edf-1) #Adjusted R squared valu
        
        pseudo.R2 <- 1- gw.dev/null.dev
        GW.diagnostic<-list(gw.deviance=gw.dev,AICc=AICc,AIC=AIC,pseudo.R2 =pseudo.R2,edf=edf)        
     }
     else
     {
        for (i in 1:rp.n)
        { 
          W.i<-W2.mat[,i]
          gwsi<-gw_reg(x,y.adj,W.i*wt2,hatmatrix,i)
          betas[i,]<-gwsi[[1]] ######See function by IG
        }
     }
    if (hatmatrix)                                         
    {
      gwres.df<-data.frame(betas,y,exp(yhat),residual,betas.SE,betas.TV)
      colnames(gwres.df)<-c(c(c(colnames(betas),c("y","yhat","residual")),paste(colnames(betas), "SE", sep="_")),paste(colnames(betas), "TV", sep="_"))
    }
    else
    {
      gwres.df<-data.frame(betas)
    }
    rownames(rp.locat)<-rownames(gwres.df)
    griddedObj <- F
    if (is(regression.points, "Spatial"))
    { 
        if (is(regression.points, "SpatialPolygonsDataFrame"))
        {
           polygons<-polygons(regression.points)
           #SpatialPolygons(regression.points)
           #rownames(gwres.df) <- sapply(slot(polygons, "polygons"),
                              #  function(i) slot(i, "ID"))
           SDF <-SpatialPolygonsDataFrame(Sr=polygons, data=gwres.df, match.ID=F)
        }
        else
        {
           griddedObj <- gridded(regression.points)
           SDF <- SpatialPointsDataFrame(coords=rp.locat, data=gwres.df, proj4string=CRS(p4s), match.ID=F)
           gridded(SDF) <- griddedObj 
        }
    }
    else
        SDF <- SpatialPointsDataFrame(coords=rp.locat, data=gwres.df, proj4string=CRS(p4s), match.ID=F)
   ##############
    if(hatmatrix)
      res<-list(GW.diagnostic=GW.diagnostic,glms=glms,SDF=SDF)
    else
      res <- list(glms=glms,SDF=SDF)
}

############ Binomial GWGLM

gwr.binomial <- function(y,x,regression.points,W1.mat,W2.mat,hatmatrix,tol=1.0e-5, maxiter=20)
{
    p4s <- as.character(NA)
    if (is(regression.points, "Spatial"))
    {
        p4s <- proj4string(regression.points)
    }

    ############################################
    ##Generalized linear regression
    glms<-glm.fit(x, y, family = binomial())
    null.dev <- glms$null.deviance
    glm.dev <-glms$deviance
    glm.pseudo.r2 <- 1- glm.dev/null.dev
    glms$pseudo.r2 <- glm.pseudo.r2
    var.n<-ncol(x)
    dp.n<-nrow(x)
    glms$aic <- glm.dev + 2*var.n
    glms$aicc <- glm.dev + 2*var.n + 2*var.n*(var.n+1)/(dp.n-var.n-1)
    ############################################
    rp.locat<-coordinates(regression.points)
    rp.n<-nrow(rp.locat)
    betas <-matrix(nrow=rp.n, ncol=var.n)
    betas1<- matrix(nrow=dp.n, ncol=var.n)
    betas.SE <-matrix(nrow=rp.n, ncol=var.n)
    betas.TV <-matrix(nrow=rp.n, ncol=var.n)
    ##S: hatmatrix
    S<-matrix(nrow=dp.n,ncol=dp.n)
    #C.M<-matrix(nrow=dp.n,ncol=dp.n)
    colnames(betas) <- colnames(x)
    #colnames(betas)[1]<-"Intercept"
    ####################################
    ##model calibration
    n=rep(1,length(y))
    it.count <- 0
    llik <- 0.0
    mu <- 0.5
    nu <- 0
    cat(" Iteration    Log-Likelihood\n=========================\n")
    wt2 <- rep(1,dp.n)
    repeat {
        y.adj <- nu + (y - n*mu)/(n*mu*(1 - mu))
        for (i in 1:dp.n)
        {
            W.i<-W1.mat[,i]
            gwsi<-gw_reg(x,y.adj,W.i*wt2,hatmatrix=F,i)
            betas1[i,]<-gwsi[[1]]
        }
        nu <- gw_fitted(x,betas1)
        mu <- exp(nu)/(1 + exp(nu))
        old.llik <- llik
        llik <- sum(lchoose(n,y) + (n-y)*log(1 - mu/n) + y*log(mu/n))
        if(is.na(llik)) llik <-old.llik
        cat(paste("   ",formatC(it.count,digits=4,width=4),"    ",formatC(llik,digits=4,width=7),"\n"))
        if (abs((old.llik - llik)/llik) < tol) break
        wt2 <- n*mu*(1-mu)
        #print(length(wt2))
        it.count <- it.count+1
        if (it.count == maxiter) break}

    if(hatmatrix)
    {
        for (i in 1:rp.n)
        {
            W.i<-W1.mat[,i]
            gwsi<-gw_reg(x,y.adj,W.i*wt2,hatmatrix,i)
            betas[i,]<-gwsi[[1]] ######See function by IG
            #S[i,]<-gwsi[[2]]
            S[i,]<-gwsi[[2]][1,]
            Ci<-gwsi[[3]]
            #betas.SE[i,]<-diag(Ci%*%t(Ci))
            invwt2 <- 1.0 /as.numeric(wt2)
            betas.SE[i,] <- diag((Ci*invwt2) %*% t(Ci))   #see Nakaya et al. (2005)
        }
        tr.S<-sum(diag(S))
        #tr.StS<-sum(S^2)

        #tr.StS<- sum(diag(S%*%diag(wt2)%*%t(S)%*% diag(1/wt2)))
        ###edf is different from the definition in Chris' code
        #edf<-dp.n-2*tr.S+tr.StS
        yhat<-gw_fitted(x, betas)
        residual<-y-exp(yhat)/(1+exp(yhat))
        ########rss <- sum((y - gwr.fitted(x,b))^2)
        rss <- sum(residual^2)
        #sigma.hat <- rss/edf
        #sigma.aic <- rss/dp.n   ### can be omitted? (TN)
        gw.dev <- sum(log(1/((y-n+exp(yhat)/(1+exp(yhat))))^2))
        for(i in 1:dp.n)
        {
            #betas.SE[i,]<-sqrt(sigma.hat*betas.SE[i,])
            betas.SE[i,]<-sqrt(betas.SE[i,])
            betas.TV[i,]<-betas[i,]/betas.SE[i,]
        }
        #AICc <- -2*llik + 2*tr.S*dp.n/(dp.n-tr.S-2)
        #AICc <- -2*llik + 2*tr.S + 2*tr.S*(tr.S+1)/(dp.n-tr.S-1)
        AICc <- gw.dev + 2*tr.S + 2*tr.S*(tr.S+1)/(dp.n-tr.S-1)
        AIC <- gw.dev + 2*tr.S
        #yss.g <- sum((y - mean(y))^2)
        #gw.R2<-1-rss/yss.g; ##R Square valeu  ### is R2 needed? (TN)
        #gwR2.adj<-1-(1-gw.R2)*(dp.n-1)/(edf-1) #Adjusted R squared value
        pseudo.R2 <- 1 - gw.dev/null.dev
        #GW.diagnostic<-list(rss=rss,AICc=AICc,edf=edf,gw.R2=gw.R2,gwR2.adj=gwR2.adj)
        GW.diagnostic<-list(gw.deviance=gw.dev,AICc=AICc,AIC=AIC,pseudo.R2 =pseudo.R2)
    }
    else
    {
        for (i in 1:rp.n)
        {
            W.i<-W2.mat[,i]
            gwsi<-gw_reg(x,y.adj,W.i*wt2,hatmatrix,i)
            betas[i,]<-gwsi[[1]]
        }
    }
    if(hatmatrix)
    {
        gwres.df<-data.frame(betas,y,exp(yhat)/(1+exp(yhat)),residual,betas.SE,betas.TV)
        colnames(gwres.df)<-c(c(c(colnames(betas),c("y","yhat","residual")),paste(colnames(betas), "SE", sep="_")),paste(colnames(betas), "TV", sep="_"))
    }
    else
    {
        gwres.df<-data.frame(betas)
    }
    rownames(rp.locat)<-rownames(gwres.df)

    griddedObj <- F
    if (is(regression.points, "Spatial"))
    {
        if (is(regression.points, "SpatialPolygonsDataFrame"))
        {
            polygons<-polygons(regression.points)
            #SpatialPolygons(regression.points)
            #rownames(gwres.df) <- sapply(slot(polygons, "polygons"),
            #  function(i) slot(i, "ID"))
            SDF <-SpatialPolygonsDataFrame(Sr=polygons, data=gwres.df,match.ID =F)
        }
        else
        {
            griddedObj <- gridded(regression.points)
            SDF <- SpatialPointsDataFrame(coords=rp.locat, data=gwres.df, proj4string=CRS(p4s), match.ID=F)
            gridded(SDF) <- griddedObj
        }
    }
    else
        SDF <- SpatialPointsDataFrame(coords=rp.locat, data=gwres.df, proj4string=CRS(p4s), match.ID=F)
    ##############
    if(hatmatrix)
        res<-list(GW.diagnostic=GW.diagnostic,glms=glms,SDF=SDF)
    else
        res <- list(glms=glms,SDF=SDF)
}

############################Layout function for outputing the GWR results
##Author: BL	
print.ggwrm<-function(x, ...)
{
  if(!inherits(x, "ggwrm")) stop("It's not a gwm object")
  cat("   ***********************************************************************\n")
  cat("   *                       Package   GWmodel                             *\n")
  cat("   ***********************************************************************\n")
  cat("   Program starts at:", as.character(x$timings$start), "\n")
  cat("   Call:\n")
  cat("   ")
  print(x$this.call)
  vars<-all.vars(x$GW.arguments$formula)
  var.n<-length(x$glms$coefficients)
	cat("\n   Dependent (y) variable: ",vars[1])
	cat("\n   Independent variables: ",vars[-1])
	dp.n<-length(x$glms$residuals)
	cat("\n   Number of data points:",dp.n)
	cat("\n   Used family:",x$GW.arguments$family)
	################################################################ Print Linear
 	cat("\n   ***********************************************************************\n")
	  cat("   *              Results of Generalized linear Regression               *\n")
	  cat("   ***********************************************************************\n")
	print(summary.glm(x$glms))
  cat("\n AICc: ", x$glms$aicc)
  cat("\n Pseudo R-square value: ", x$glms$pseudo.r2)
	#########################################################################
	cat("\n   ***********************************************************************\n")
	  cat("   *          Results of Geographically Weighted Regression              *\n")
	cat("   ***********************************************************************\n")
	cat("\n   *********************Model calibration information*********************\n")
	cat("   Kernel function:", x$GW.arguments$kernel, "\n")
	if(x$GW.arguments$adaptive)
	   cat("   Adaptive bandwidth: ", x$GW.arguments$bw, " (number of nearest neighbours)\n", sep="") 
  else
     cat("   Fixed bandwidth:", x$GW.arguments$bw, "\n")
	if(x$GW.arguments$rp.given) 
     cat("   Regression points: A seperate set of regression points is used.\n")
  else
     cat("   Regression points: the same locations as observations are used.\n")
	if (x$GW.arguments$DM.given) 
     cat("   Distance metric: A distance matrix is specified for this model calibration.\n")
  else
     {
     if (x$GW.arguments$longlat)
        cat("   Distance metric: Great Circle distance metric is used.\n")
     else if (x$GW.arguments$p==2)
        cat("   Distance metric: Euclidean distance metric is used.\n")
     else if (x$GW.arguments$p==1)
        cat("   Distance metric: Manhattan distance metric is used.\n") 
     else if (is.infinite(x$GW.arguments$p))
        cat("   Distance metric: Chebyshev distance metric is used.\n")
     else 
        cat("   Distance metric: A generalized Minkowski distance metric is used with p=",x$GW.arguments$p,".\n")
     if (x$GW.arguments$theta!=0&&x$GW.arguments$p!=2&&!x$GW.arguments$longlat)
        cat("   Coordinate rotation: The coordinate system is rotated by an angle", x$GW.arguments$theta, "in radian.\n")   
     } 
	
	cat("\n   ************Summary of Generalized GWR coefficient estimates:**********\n")      
		df0 <- as(x$SDF, "data.frame")[,1:var.n, drop=FALSE]
        if (any(is.na(df0))) {
            df0 <- na.omit(df0)
            warning("NAs in coefficients dropped")
        }
	CM <- t(apply(df0, 2, summary))[,c(1:3,5,6)]
	if(var.n==1) 
    { 
      CM <- matrix(CM, nrow=1)
      colnames(CM) <- c("Min.", "1st Qu.", "Median", "3rd Qu.", "Max.")
      rownames(CM) <- names(x$SDF)[1]
    }
	rnames<-rownames(CM)
		for (i in 1:length(rnames))
			 rnames[i]<-paste("   ",rnames[i],sep="")
	rownames(CM) <-rnames 
	printCoefmat(CM)
	cat("   ************************Diagnostic information*************************\n")

	if (x$GW.arguments$hatmatrix) 
  {	
		cat("   Number of data points:", dp.n, "\n")
		cat("   GW Deviance:", x$GW.diagnostic$gw.deviance, "\n")
		cat("   AIC :",
                    x$GW.diagnostic$AIC, "\n")
    cat("   AICc :",
                    x$GW.diagnostic$AICc, "\n")
    cat("   Pseudo R-square value: ",x$GW.diagnostic$pseudo.R2,"\n")
  }
	cat("\n   ***********************************************************************\n")
	cat("   Program stops at:", as.character(x$timings$stop), "\n")
	invisible(x)
}