Description Usage Arguments Author(s) Examples
Cross-validation for selection of tuning parameter in a GW-GLM model using Nearest Effective Neighbors for bandwidth selection.
1 |
formula |
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data |
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bw |
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coords |
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gweight |
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verbose |
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adapt |
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longlat |
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s |
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beta1 |
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beta2 |
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family |
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weights |
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D |
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tolerance |
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type |
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parallel |
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... |
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 | ##---- 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, bw, coords, gweight, verbose, adapt,
longlat, s = NULL, beta1, beta2, family, weights = NULL,
D = NULL, tolerance = .Machine$double.eps^0.25, type = "pearson",
parallel = FALSE, ...)
{
cat(paste("Beginning with target SSR: ", bw, ", tolerance: ",
tolerance, "\n", sep = ""))
gwglmnet.model = gwglmnet.nen(formula = formula, data = data,
coords = coords, gweight = gweight, bw = bw, verbose = verbose,
longlat = longlat, adapt = adapt, s = s, family = family,
weights = weights, D = D, tol = tolerance, beta1 = beta1,
beta2 = beta2, type, parallel = parallel)
print(gwglmnet.model[["model"]][["cv.error"]])
print(names(gwglmnet.model))
print(gwglmnet.model[["model"]])
cv.error = sum(sapply(gwglmnet.model[["model"]], function(x) min(x[["cv.error"]])))
cat(paste("Bandwidth: ", bw, ". CV error: ", cv.error, "\n",
sep = ""))
return(cv.error)
}
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