Extracts weights from model objects. The weights are re-scaled to up to the total number of observations in the fitting procedure.
# S4 method for EL
weights(object, ...)An object that inherits from EL.
Further arguments passed to methods.
A numeric vector of the re-scaled weights.
Glenn N, Zhao Y (2007). ``Weighted Empirical Likelihood Estimates and Their Robustness Properties.'' Computational Statistics & Data Analysis, 51(10), 5130--5141. doi:10.1016/j.csda.2006.07.032 .
data("airquality")
x <- airquality$Wind
w <- airquality$Day
fit <- el_mean(x, par = 10, weights = w)
weights(fit)
#> [1] 0.06327543 0.12655087 0.18982630 0.25310174 0.31637717 0.37965261
#> [7] 0.44292804 0.50620347 0.56947891 0.63275434 0.69602978 0.75930521
#> [13] 0.82258065 0.88585608 0.94913151 1.01240695 1.07568238 1.13895782
#> [19] 1.20223325 1.26550868 1.32878412 1.39205955 1.45533499 1.51861042
#> [25] 1.58188586 1.64516129 1.70843672 1.77171216 1.83498759 1.89826303
#> [31] 1.96153846 0.06327543 0.12655087 0.18982630 0.25310174 0.31637717
#> [37] 0.37965261 0.44292804 0.50620347 0.56947891 0.63275434 0.69602978
#> [43] 0.75930521 0.82258065 0.88585608 0.94913151 1.01240695 1.07568238
#> [49] 1.13895782 1.20223325 1.26550868 1.32878412 1.39205955 1.45533499
#> [55] 1.51861042 1.58188586 1.64516129 1.70843672 1.77171216 1.83498759
#> [61] 1.89826303 0.06327543 0.12655087 0.18982630 0.25310174 0.31637717
#> [67] 0.37965261 0.44292804 0.50620347 0.56947891 0.63275434 0.69602978
#> [73] 0.75930521 0.82258065 0.88585608 0.94913151 1.01240695 1.07568238
#> [79] 1.13895782 1.20223325 1.26550868 1.32878412 1.39205955 1.45533499
#> [85] 1.51861042 1.58188586 1.64516129 1.70843672 1.77171216 1.83498759
#> [91] 1.89826303 1.96153846 0.06327543 0.12655087 0.18982630 0.25310174
#> [97] 0.31637717 0.37965261 0.44292804 0.50620347 0.56947891 0.63275434
#> [103] 0.69602978 0.75930521 0.82258065 0.88585608 0.94913151 1.01240695
#> [109] 1.07568238 1.13895782 1.20223325 1.26550868 1.32878412 1.39205955
#> [115] 1.45533499 1.51861042 1.58188586 1.64516129 1.70843672 1.77171216
#> [121] 1.83498759 1.89826303 1.96153846 0.06327543 0.12655087 0.18982630
#> [127] 0.25310174 0.31637717 0.37965261 0.44292804 0.50620347 0.56947891
#> [133] 0.63275434 0.69602978 0.75930521 0.82258065 0.88585608 0.94913151
#> [139] 1.01240695 1.07568238 1.13895782 1.20223325 1.26550868 1.32878412
#> [145] 1.39205955 1.45533499 1.51861042 1.58188586 1.64516129 1.70843672
#> [151] 1.77171216 1.83498759 1.89826303