gwglmnet.nen.fit: Fit a GW-GLM model using Nearest Effective Neighbors for...

In wrbrooks/gwselect: Variable selection in Geographically Weighted Regression models

Description

Fit a GW-GLM model using Nearest Effective Neighbors for bandwidth selection.

Usage

gwglmnet.nen.fit(x, y, coords, D, s = NULL, verbose, family, prior.weights, gweight, bw, beta1, beta2, type = "pearson", tol = 1e-25, longlat = FALSE)

Arguments

x
y
coords
D
s
verbose
family
prior.weights
gweight
bw
beta1
beta2
type
tol
longlat

Author(s)

Wesley Brooks

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, y, coords, D, s = NULL, verbose, family, prior.weights, 
    gweight, bw, beta1, beta2, type = "pearson", tol = 1e-25, 
    longlat = FALSE) 
{
    coords.unique = unique(coords)
    model = list()
    s.optimal = vector()
    gwglmnet.object = list()
    cv.error = list()
    for (i in 1:dim(coords.unique)[1]) {
        colocated = which(coords[, 1] == coords.unique[i, 1] & 
            coords[, 2] == coords.unique[i, 2])
        dist = D[i, ]
        bandwidth = optimize(gwglmnet.ssr, lower = beta1, upper = beta2, 
            maximum = FALSE, tol = bw/10, x = x, y = y, colocated = colocated, 
            s = s, gweight = gweight, verbose = verbose, dist = dist, 
            prior.weights = prior.weights, family = family, target = bw, 
            type = type)$minimum
        cat(paste("For i=", i, ", bw=", bandwidth, ".\n", sep = ""))
        weight.matrix = gweight(D, bandwidth)
        loow = weight.matrix[i, -colocated]
        prior.loow = prior.weights[-colocated]
        reps = length(colocated)
        w <- prior.loow * loow
        if (sum(loow) == 0) {
            return(list(cv.error = Inf))
        }
        reps = length(colocated)
        xx = as.matrix(x[-colocated, ])
        yy = as.matrix(y[-colocated])
        if (family == "binomial" && (abs(sum(yy * w) - sum(w)) < 
            1e-04 || sum(yy * w) < 1e-04)) {
            cat(paste("Abort. i=", i, ", weighted sum=", sum(yy * 
                w), ", sum of weights=", sum(w), "\n", sep = ""))
            model[[i]] = NULL
            cv.error[[i]] = 0
            s.optimal = c(s.optimal, max(s))
        }
        else if (family == "binomial") {
            model[[i]] = glmnet(x = xx, y = cbind(1 - yy, yy), 
                weights = w, family = family, lambda = s)
            predictions = predict(model[[i]], newx = matrix(x[colocated, 
                ], nrow = reps, ncol = dim(xx)[2]), s = s, type = "response")
            cv.error[[i]] = colSums(abs(matrix(predictions - 
                matrix(y[colocated], nrow = reps, ncol = length(s)), 
                nrow = reps, ncol = length(s))))
            s.optimal = c(s.optimal, s[which.min(cv.error[[i]])])
        }
        else {
            model[[i]] = glmnet(x = xx, y = yy, weights = w, 
                family = family, lambda = s)
            predictions = predict(model[[i]], newx = matrix(x[colocated, 
                ], nrow = reps, ncol = dim(xx)[2]), s = s, type = "response")
            cv.error[[i]] = colSums(abs(matrix(predictions - 
                matrix(y[colocated], nrow = reps, ncol = length(s)), 
                nrow = reps, ncol = length(s))))
            s.optimal = c(s.optimal, s[which.min(cv.error[[i]])])
        }
        if (verbose) {
            cat(paste(i, "\n", sep = ""))
        }
    }
    gwglmnet.object[["coef.scale"]] = NULL
    gwglmnet.object[["model"]] = model
    gwglmnet.object[["s"]] = s.optimal
    gwglmnet.object[["mode"]] = mode
    gwglmnet.object[["coords"]] = coords.unique
    gwglmnet.object[["cv.error"]] = cv.error
    gwglmnet.object[["s.range"]] = s
    class(gwglmnet.object) = "gwglmnet.object"
    return(gwglmnet.object)
  }

wrbrooks/gwselect documentation built on May 4, 2019, 11:59 a.m.