Description Usage Arguments Author(s) Examples
Heatmap plotting function for gwrselect package
1 |
formula |
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data |
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coords |
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gweight |
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bw |
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D |
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verbose |
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longlat |
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adapt |
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s |
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family |
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weights |
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nearest.neighbors |
Wesley Brooks
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ##---- 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 (formula, data, coords, gweight, bw, D = NULL, verbose = FALSE,
longlat = FALSE, adapt = FALSE, s, family, weights = NULL,
nearest.neighbors = FALSE)
{
if (!is.logical(adapt))
stop("adapt must be logical")
if (is(data, "Spatial")) {
if (!missing(coords))
warning("data is Spatial* object, ignoring coords argument")
coords <- coordinates(data)
if ((is.null(longlat) || !is.logical(longlat)) && !is.na(is.projected(data)) &&
!is.projected(data)) {
longlat <- TRUE
}
else longlat <- FALSE
data <- as(data, "data.frame")
}
if (is.null(longlat) || !is.logical(longlat))
longlat <- FALSE
if (missing(coords))
stop("Observation coordinates have to be given")
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data"), names(mf), 0)
mf <- mf[c(1, m)]
mf$drop.unused.levels <- TRUE
mf[[1]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
mt <- attr(mf, "terms")
dp.n <- length(model.extract(mf, "response"))
if (!is.null(weights) && !is.numeric(weights))
stop("'weights' must be a numeric vector")
if (is.null(weights))
weights <- rep(as.numeric(1), dp.n)
if (any(is.na(weights)))
stop("NAs in weights")
if (any(weights < 0))
stop("negative weights")
y <- model.extract(mf, "response")
x <- model.matrix(mt, mf)
if (is.null(D)) {
n = dim(coords)[1]
if (longlat) {
D = as.matrix(earth.dist(coords), n, n)
}
else {
Xmat = matrix(rep(coords[, 1], times = n), n, n)
Ymat = matrix(rep(coords[, 2], times = n), n, n)
D = sqrt((Xmat - t(Xmat))^2 + (Ymat - t(Ymat))^2)
}
}
if (!nearest.neighbors) {
weight.matrix = gweight(D, bw)
}
else {
n = dim(D)[1]
bandwidths = sapply(1:n, function(x) {
neighbor.weight(q = bw, D = D[x, ], weight.function = gweight,
verbose = verbose, tol = 0.001)
})
weight.matrix = as.matrix(rbind(sapply(1:n, function(k) {
gweight(as.vector(D[k, ]), as.numeric(bandwidths[1,
k]))
})), n, n)
}
if (!adapt) {
res = gwglmnet.fit(x, y, coords, weight.matrix, s, verbose,
family, weights)
}
else {
res = gwglmnet.adaptive.fit(x, y, coords, weight.matrix,
s, verbose, family, weights)
}
res[["data"]] = data
res[["response"]] = as.character(formula[[2]])
res
}
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