From 47f7bb500101e8317f023030edd1c757d3386eb7 Mon Sep 17 00:00:00 2001 From: elbersb Date: Wed, 20 Sep 2023 13:04:26 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20elbersb/?= =?UTF-8?q?segregation@7e935ec3dd02e3de7c6eb285fdd94b55015e50a8=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/segregation.html | 20 +++++++++--------- .../figure-html/unnamed-chunk-15-1.png | Bin 71387 -> 71071 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 25130 -> 25618 bytes pkgdown.yml | 2 +- reference/dissimilarity_expected.html | 8 +++---- 5 files changed, 15 insertions(+), 15 deletions(-) diff --git a/articles/segregation.html b/articles/segregation.html index 271277f..a1bae81 100644 --- a/articles/segregation.html +++ b/articles/segregation.html @@ -259,8 +259,8 @@

Computing the M and H indices) #> 500 bootstrap iterations on 877739 observations #> stat est se CI bias -#> 1: M 0.4219408 0.0007974201 0.4205149,0.4234578 0.003598141 -#> 2: H 0.4152602 0.0007145827 0.4138472,0.4166456 0.003548115 +#> 1: M 0.4219698 0.0007638448 0.4204032,0.4234156 0.003569183 +#> 2: H 0.4152615 0.0007109380 0.4138070,0.4166289 0.003546811

As there a large number of observations, the standard errors are very small.

@@ -491,8 +491,8 @@

Inference)) #> 500 bootstrap iterations on 877739 observations #> stat est se CI bias -#> 1: M 0.4218529 0.0007685780 0.4201174,0.4233492 0.003686053 -#> 2: H 0.4151813 0.0007183835 0.4136785,0.4164827 0.003627037 +#> 1: M 0.4219952 0.0007918967 0.4203766,0.4235564 0.003543822 +#> 2: H 0.4153134 0.0007281167 0.4139382,0.4168015 0.003494908

The confidence intervals are based on the percentiles from the bootstrap distribution, and hence require a large number of bootstrap iterations for valid interpretation. The estimate est that @@ -510,10 +510,10 @@

Inference
 # M
 with(se, c(est[1] - 1.96 * se[1], est[1] + 1.96 * se[1]))
-#> [1] 0.4203465 0.4233593
+#> [1] 0.4204430 0.4235473
 # H
 with(se, c(est[2] - 1.96 * se[2], est[2] + 1.96 * se[2]))
-#> [1] 0.4137732 0.4165893
+#> [1] 0.4138863 0.4167405

provide effectively the same coverage as the confidence intervals obtained from the percentile bootstrap.

Whenever the bootstrap is used, the bootstrap distributions for each @@ -545,8 +545,8 @@

Inference
 mutual_expected(schools00, "race", "school", weight = "n", n_bootstrap = 500)
 #>         stat         est           se
-#> 1: M under 0 0.004811694 7.764370e-05
-#> 2: H under 0 0.004735589 7.641563e-05
+#> 1: M under 0 0.004806485 7.374713e-05 +#> 2: H under 0 0.004730462 7.258069e-05

Here, there is no concern about bias due to a small sample size.

This method also supports inference by setting se = TRUE.

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n = c(rep(1, 10), rep(9, 10)) ) dissimilarity_expected(small, "race", "school", weight = "n") -#> stat est se -#> 1: D under 0 0.3777778 0.1094147 +#> stat est se +#> 1: D under 0 0.3811111 0.09243084 # with an increase in sample size (n=1000), the values improve small$n <- small$n * 10 dissimilarity_expected(small, "race", "school", weight = "n") -#> stat est se -#> 1: D under 0 0.1276667 0.03288115 +#> stat est se +#> 1: D under 0 0.123 0.03047019