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tests/testthat/_snaps/confidence_intervals.md
In weibulltools: Statistical Methods for Life Data Analysis
confint_betabinom remains stable
[[1]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 1.00 0.0183 0.000674 0.0927 johnson
2 6910 1.06 0.0199 0.000859 0.0958 johnson
3 7120. 1.13 0.0216 0.00108 0.0990 johnson
4 7330. 1.20 0.0234 0.00134 0.102 johnson
5 7540 1.27 0.0252 0.00165 0.106 johnson
6 7750 1.34 0.0272 0.00201 0.109 johnson
7 7960. 1.42 0.0293 0.00242 0.113 johnson
8 8170 1.51 0.0314 0.00289 0.117 johnson
9 8380 1.59 0.0336 0.00341 0.121 johnson
10 8590 1.68 0.0360 0.00401 0.124 johnson
# i 92 more rows
[[2]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.711 0.0107 0.000128 0.0767 johnson
2 6910 0.772 0.0123 0.000200 0.0802 johnson
3 7120. 0.838 0.0140 0.000302 0.0840 johnson
4 7330. 0.911 0.0159 0.000445 0.0879 johnson
5 7540 0.989 0.0179 0.000636 0.0920 johnson
6 7750 1.07 0.0201 0.000887 0.0962 johnson
7 7960. 1.16 0.0225 0.00121 0.101 johnson
8 8170 1.26 0.0250 0.00161 0.105 johnson
9 8380 1.36 0.0276 0.00209 0.110 johnson
10 8590 1.47 0.0305 0.00268 0.115 johnson
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.911 0.0159 0.000445 0.0879 johnson
2 6910 0.971 0.0175 0.000589 0.0910 johnson
3 7120. 1.04 0.0192 0.000770 0.0943 johnson
4 7330. 1.10 0.0209 0.000991 0.0978 johnson
5 7540 1.18 0.0228 0.00126 0.101 johnson
6 7750 1.25 0.0248 0.00158 0.105 johnson
7 7960. 1.33 0.0269 0.00195 0.109 johnson
8 8170 1.42 0.0291 0.00239 0.113 johnson
9 8380 1.51 0.0314 0.00290 0.117 johnson
10 8590 1.60 0.0339 0.00347 0.121 johnson
# i 92 more rows
[[4]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.39 0.0284 0.00225 0.112 johnson
2 6910 1.43 0.0295 0.00248 0.113 johnson
3 7120. 1.48 0.0307 0.00272 0.115 johnson
4 7330. 1.52 0.0318 0.00299 0.118 johnson
5 7540 1.57 0.0331 0.00328 0.120 johnson
6 7750. 1.62 0.0343 0.00359 0.122 johnson
7 7960. 1.67 0.0357 0.00393 0.124 johnson
8 8170. 1.72 0.0370 0.00429 0.126 johnson
9 8380 1.78 0.0385 0.00469 0.129 johnson
10 8590. 1.83 0.0399 0.00511 0.131 johnson
# i 92 more rows
[[5]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.20 0.0233 0.00133 0.102 johnson
2 6910 1.25 0.0246 0.00155 0.105 johnson
3 7120. 1.30 0.0260 0.00179 0.107 johnson
4 7330. 1.35 0.0275 0.00206 0.110 johnson
5 7540 1.41 0.0290 0.00236 0.112 johnson
6 7750 1.47 0.0305 0.00269 0.115 johnson
7 7960 1.54 0.0322 0.00307 0.118 johnson
8 8170 1.60 0.0339 0.00348 0.121 johnson
9 8380. 1.67 0.0357 0.00394 0.124 johnson
10 8590 1.74 0.0376 0.00444 0.127 johnson
# i 92 more rows
[[6]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.33 0.0268 0.00192 0.109 johnson
2 6910 1.37 0.0279 0.00214 0.111 johnson
3 7120 1.42 0.0291 0.00238 0.113 johnson
4 7330 1.46 0.0303 0.00265 0.115 johnson
5 7540 1.51 0.0316 0.00294 0.117 johnson
6 7750 1.57 0.0329 0.00325 0.119 johnson
7 7960 1.62 0.0343 0.00359 0.122 johnson
8 8170 1.67 0.0358 0.00396 0.124 johnson
9 8380 1.73 0.0373 0.00437 0.127 johnson
10 8590 1.79 0.0389 0.00480 0.129 johnson
# i 92 more rows
[[1]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 1.00 0.0183 2013. 12248. johnson
2 6910 1.06 0.0199 2199. 12404. johnson
3 7120. 1.13 0.0216 2390. 12563. johnson
4 7330. 1.20 0.0234 2587. 12724. johnson
5 7540 1.27 0.0252 2788. 12886. johnson
6 7750 1.34 0.0272 2994. 13050. johnson
7 7960. 1.42 0.0293 3203. 13216. johnson
8 8170 1.51 0.0314 3416. 13383. johnson
9 8380 1.59 0.0336 3631. 13551. johnson
10 8590 1.68 0.0360 3848. 13721. johnson
# i 92 more rows
[[2]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.711 0.0107 3047. 11139. johnson
2 6910 0.772 0.0123 3258. 11295. johnson
3 7120. 0.838 0.0140 3475. 11457. johnson
4 7330. 0.911 0.0159 3697. 11624. johnson
5 7540 0.989 0.0179 3921. 11796. johnson
6 7750 1.07 0.0201 4147. 11973. johnson
7 7960. 1.16 0.0225 4375. 12153. johnson
8 8170 1.26 0.0250 4603. 12337. johnson
9 8380 1.36 0.0276 4830. 12525. johnson
10 8590 1.47 0.0305 5057. 12716. johnson
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.911 0.0159 2114. 11883. johnson
2 6910 0.971 0.0175 2314. 12033. johnson
3 7120. 1.04 0.0192 2522. 12186. johnson
4 7330. 1.10 0.0209 2735. 12341. johnson
5 7540 1.18 0.0228 2953. 12499. johnson
6 7750 1.25 0.0248 3175. 12660. johnson
7 7960. 1.33 0.0269 3401. 12823. johnson
8 8170 1.42 0.0291 3630. 12988. johnson
9 8380 1.51 0.0314 3861. 13156. johnson
10 8590 1.60 0.0339 4094. 13325. johnson
# i 92 more rows
[[4]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.39 0.0284 -7252. 14421. johnson
2 6910 1.43 0.0295 -6726. 14521. johnson
3 7120. 1.48 0.0307 -6207. 14622. johnson
4 7330. 1.52 0.0318 -5695. 14725. johnson
5 7540 1.57 0.0331 -5189. 14828. johnson
6 7750. 1.62 0.0343 -4690. 14933. johnson
7 7960. 1.67 0.0357 -4198. 15039. johnson
8 8170. 1.72 0.0370 -3712. 15146. johnson
9 8380 1.78 0.0385 -3232. 15255. johnson
10 8590. 1.83 0.0399 -2759. 15364. johnson
# i 92 more rows
[[5]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.20 0.0233 -2439. 13197. johnson
2 6910 1.25 0.0246 -2031. 13318. johnson
3 7120. 1.30 0.0260 -1628. 13442. johnson
4 7330. 1.35 0.0275 -1230. 13567. johnson
5 7540 1.41 0.0290 -838. 13695. johnson
6 7750 1.47 0.0305 -451. 13824. johnson
7 7960 1.54 0.0322 -69.4 13955. johnson
8 8170 1.60 0.0339 307. 14087. johnson
9 8380. 1.67 0.0357 678. 14222. johnson
10 8590 1.74 0.0376 1044. 14358. johnson
# i 92 more rows
[[6]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.33 0.0268 -6314. 13987. johnson
2 6910 1.37 0.0279 -5784. 14090. johnson
3 7120 1.42 0.0291 -5262. 14194. johnson
4 7330 1.46 0.0303 -4748. 14299. johnson
5 7540 1.51 0.0316 -4241. 14406. johnson
6 7750 1.57 0.0329 -3742. 14514. johnson
7 7960 1.62 0.0343 -3250. 14624. johnson
8 8170 1.67 0.0358 -2766. 14735. johnson
9 8380 1.73 0.0373 -2289. 14847. johnson
10 8590 1.79 0.0389 -1819. 14962. johnson
# i 92 more rows
[[1]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.23 0.0128 0.000769 0.0561 johnson
2 96.0 1.77 0.0203 0.00243 0.0696 johnson
3 98.0 2.39 0.0288 0.00523 0.0837 johnson
4 100. 3.07 0.0383 0.00912 0.0983 johnson
5 102. 3.82 0.0486 0.0140 0.113 johnson
6 104. 4.61 0.0595 0.0199 0.129 johnson
7 106. 5.45 0.0711 0.0266 0.145 johnson
8 108. 6.33 0.0832 0.0340 0.161 johnson
9 110. 7.24 0.0959 0.0422 0.177 johnson
10 111. 7.54 0.1 0.0449 0.182 johnson
# i 92 more rows
[[2]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.53 0.0170 0.00160 0.0639 johnson
2 96.0 1.92 0.0224 0.00304 0.0731 johnson
3 98.0 2.38 0.0287 0.00517 0.0834 johnson
4 100. 2.90 0.0359 0.00808 0.0947 johnson
5 102. 3.49 0.0441 0.0118 0.107 johnson
6 104. 4.15 0.0532 0.0165 0.120 johnson
7 106. 4.88 0.0632 0.0220 0.134 johnson
8 108. 5.66 0.0741 0.0284 0.149 johnson
9 110. 6.51 0.0858 0.0356 0.164 johnson
10 112. 7.41 0.0982 0.0437 0.180 johnson
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.62 0.0182 0.00189 0.0660 johnson
2 96.0 1.99 0.0234 0.00335 0.0748 johnson
3 98.0 2.42 0.0293 0.00541 0.0844 johnson
4 100. 2.91 0.0361 0.00815 0.0949 johnson
5 102. 3.46 0.0437 0.0116 0.106 johnson
6 104. 4.07 0.0521 0.0159 0.118 johnson
7 106. 4.74 0.0614 0.0209 0.131 johnson
8 108. 5.48 0.0715 0.0268 0.145 johnson
9 110. 6.27 0.0824 0.0335 0.159 johnson
10 112. 7.11 0.0941 0.0410 0.175 johnson
# i 92 more rows
[[1]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.23 0.0128 89.3 103. johnson
2 96.0 1.77 0.0203 90.3 106. johnson
3 98.0 2.39 0.0288 91.5 108. johnson
4 100. 3.07 0.0383 92.9 110. johnson
5 102. 3.82 0.0486 94.4 113. johnson
6 104. 4.61 0.0595 95.9 115. johnson
7 106. 5.45 0.0711 97.5 117. johnson
8 108. 6.33 0.0832 99.1 119. johnson
9 110. 7.24 0.0959 101. 122. johnson
10 111. 7.54 0.1 101. 122. johnson
# i 92 more rows
[[2]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.53 0.0170 82.6 106. johnson
2 96.0 1.92 0.0224 85.0 108. johnson
3 98.0 2.38 0.0287 87.3 110. johnson
4 100. 2.90 0.0359 89.5 111. johnson
5 102. 3.49 0.0441 91.7 113. johnson
6 104. 4.15 0.0532 93.8 115. johnson
7 106. 4.88 0.0632 95.8 117. johnson
8 108. 5.66 0.0741 97.9 119. johnson
9 110. 6.51 0.0858 99.9 121. johnson
10 112. 7.41 0.0982 102. 123. johnson
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.62 0.0182 82.5 107. johnson
2 96.0 1.99 0.0234 84.6 109. johnson
3 98.0 2.42 0.0293 86.7 110. johnson
4 100. 2.91 0.0361 88.8 112. johnson
5 102. 3.46 0.0437 90.9 114. johnson
6 104. 4.07 0.0521 93.0 116. johnson
7 106. 4.74 0.0614 95.1 118. johnson
8 108. 5.48 0.0715 97.2 119. johnson
9 110. 6.27 0.0824 99.2 121. johnson
10 112. 7.11 0.0941 101. 123. johnson
# i 92 more rows
confint_fisher remains stable
[[1]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0112 0.916 0.00186 0.0655 <NA>
2 6910 0.0123 0.895 0.00214 0.0691 <NA>
3 7120. 0.0135 0.875 0.00245 0.0729 <NA>
4 7330. 0.0148 0.855 0.00279 0.0767 <NA>
5 7540 0.0162 0.835 0.00317 0.0805 <NA>
6 7750 0.0177 0.817 0.00359 0.0845 <NA>
7 7960. 0.0192 0.799 0.00404 0.0886 <NA>
8 8170 0.0208 0.781 0.00454 0.0927 <NA>
9 8380 0.0225 0.764 0.00509 0.0969 <NA>
10 8590 0.0244 0.747 0.00568 0.101 <NA>
# i 92 more rows
[[2]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.00589 0.431 0.000385 0.0472 <NA>
2 6910 0.00694 0.421 0.000511 0.0509 <NA>
3 7120. 0.00812 0.410 0.000668 0.0549 <NA>
4 7330. 0.00942 0.401 0.000862 0.0589 <NA>
5 7418. 0.01 0.397 0.000956 0.0607 <NA>
6 7540 0.0109 0.391 0.00110 0.0631 <NA>
7 7750 0.0124 0.382 0.00138 0.0675 <NA>
8 7960. 0.0141 0.373 0.00172 0.0719 <NA>
9 8170 0.0160 0.365 0.00212 0.0765 <NA>
10 8380 0.0180 0.357 0.00259 0.0812 <NA>
# i 93 more rows
[[3]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.00906 0.982 0.00133 0.0590 <NA>
2 6891. 0.01 0.961 0.00153 0.0623 <NA>
3 6910 0.0101 0.959 0.00156 0.0626 <NA>
4 7120. 0.0112 0.936 0.00181 0.0663 <NA>
5 7330. 0.0124 0.913 0.00210 0.0701 <NA>
6 7540 0.0137 0.892 0.00242 0.0740 <NA>
7 7750 0.0151 0.871 0.00277 0.0780 <NA>
8 7960. 0.0166 0.851 0.00317 0.0821 <NA>
9 8170 0.0182 0.831 0.00362 0.0863 <NA>
10 8380 0.0199 0.813 0.00410 0.0906 <NA>
# i 93 more rows
[[4]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0286 0.698 0.00735 0.108 <NA>
2 6910 0.0297 0.691 0.00775 0.110 <NA>
3 7120. 0.0308 0.684 0.00816 0.113 <NA>
4 7330. 0.0320 0.676 0.00860 0.115 <NA>
5 7540 0.0332 0.669 0.00906 0.118 <NA>
6 7750 0.0345 0.662 0.00955 0.121 <NA>
7 7960. 0.0358 0.655 0.0101 0.124 <NA>
8 8170 0.0372 0.648 0.0106 0.126 <NA>
9 8380 0.0387 0.641 0.0112 0.129 <NA>
10 8590. 0.0401 0.634 0.0118 0.132 <NA>
# i 92 more rows
[[5]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0162 0.360 0.00222 0.0760 <NA>
2 6910 0.0173 0.356 0.00246 0.0784 <NA>
3 7120 0.0183 0.352 0.00272 0.0808 <NA>
4 7330 0.0195 0.348 0.00301 0.0834 <NA>
5 7540 0.0207 0.344 0.00333 0.0860 <NA>
6 7750 0.0219 0.340 0.00367 0.0886 <NA>
7 7960. 0.0233 0.336 0.00405 0.0914 <NA>
8 8170 0.0247 0.332 0.00445 0.0942 <NA>
9 8380 0.0261 0.328 0.00489 0.0970 <NA>
10 8590 0.0277 0.324 0.00537 0.100 <NA>
# i 92 more rows
[[6]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 0.0232 0.754 0.00538 0.0942 <NA>
2 6910 0.0242 0.745 0.00572 0.0966 <NA>
3 7120 0.0253 0.737 0.00608 0.0990 <NA>
4 7330 0.0264 0.729 0.00646 0.102 <NA>
5 7540 0.0275 0.720 0.00686 0.104 <NA>
6 7750 0.0288 0.712 0.00728 0.107 <NA>
7 7960 0.0300 0.703 0.00774 0.109 <NA>
8 8170 0.0313 0.695 0.00822 0.112 <NA>
9 8380 0.0327 0.687 0.00872 0.115 <NA>
10 8590 0.0341 0.679 0.00926 0.118 <NA>
# i 92 more rows
[[1]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0112 1943. 3795. 11827. <NA>
2 6910 0.0123 1957. 3966. 12038. <NA>
3 7120. 0.0135 1970. 4139. 12247. <NA>
4 7330. 0.0148 1982. 4314. 12453. <NA>
5 7540 0.0162 1993. 4491. 12658. <NA>
6 7750 0.0177 2003. 4670. 12861. <NA>
7 7960. 0.0192 2011. 4851. 13061. <NA>
8 8170 0.0208 2019. 5034. 13260. <NA>
9 8380 0.0225 2025. 5218. 13458. <NA>
10 8590 0.0244 2031. 5404. 13654. <NA>
# i 92 more rows
[[2]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.00589 1532. 4280. 10487. <NA>
2 6910 0.00694 1541. 4464. 10697. <NA>
3 7120. 0.00812 1549. 4648. 10906. <NA>
4 7330. 0.00942 1556. 4835. 11113. <NA>
5 7418. 0.01 1559. 4913. 11200. <NA>
6 7540 0.0109 1563. 5022. 11320. <NA>
7 7750 0.0124 1569. 5211. 11526. <NA>
8 7960. 0.0141 1575. 5401. 11731. <NA>
9 8170 0.0160 1580. 5593. 11935. <NA>
10 8380 0.0180 1584. 5785. 12139. <NA>
# i 93 more rows
[[3]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.00906 1850. 3900. 11510. <NA>
2 6891. 0.01 1860. 4060. 11697. <NA>
3 6910 0.0101 1861. 4076. 11716. <NA>
4 7120. 0.0112 1872. 4253. 11920. <NA>
5 7330. 0.0124 1881. 4432. 12122. <NA>
6 7540 0.0137 1890. 4614. 12322. <NA>
7 7750 0.0151 1897. 4797. 12521. <NA>
8 7960. 0.0166 1903. 4982. 12719. <NA>
9 8170 0.0182 1909. 5168. 12915. <NA>
10 8380 0.0199 1913. 5357. 13110. <NA>
# i 93 more rows
[[4]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0286 3810. 2198. 20424. <NA>
2 6910 0.0297 3771. 2371. 20139. <NA>
3 7120. 0.0308 3732. 2548. 19892. <NA>
4 7330. 0.0320 3694. 2730. 19680. <NA>
5 7540 0.0332 3655. 2916. 19497. <NA>
6 7750 0.0345 3616. 3105. 19341. <NA>
7 7960. 0.0358 3578. 3299. 19209. <NA>
8 8170 0.0372 3539. 3495. 19097. <NA>
9 8380 0.0387 3501. 3695. 19005. <NA>
10 8590. 0.0401 3463. 3898. 18929. <NA>
# i 92 more rows
[[5]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0162 3058. 2739. 16388. <NA>
2 6910 0.0173 3022. 2932. 16284. <NA>
3 7120 0.0183 2987. 3129. 16202. <NA>
4 7330 0.0195 2952. 3329. 16139. <NA>
5 7540 0.0207 2917. 3532. 16095. <NA>
6 7750 0.0219 2883. 3739. 16066. <NA>
7 7960. 0.0233 2848. 3947. 16051. <NA>
8 8170 0.0247 2815. 4159. 16049. <NA>
9 8380 0.0261 2781. 4373. 16059. <NA>
10 8590 0.0277 2748. 4589. 16079. <NA>
# i 92 more rows
[[6]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 0.0232 3570. 2358. 19039. <NA>
2 6910 0.0242 3530. 2539. 18806. <NA>
3 7120 0.0253 3490. 2725. 18606. <NA>
4 7330 0.0264 3450. 2914. 18437. <NA>
5 7540 0.0275 3410. 3108. 18294. <NA>
6 7750 0.0288 3370. 3305. 18174. <NA>
7 7960 0.0300 3331. 3505. 18076. <NA>
8 8170 0.0313 3292. 3709. 17995. <NA>
9 8380 0.0327 3253. 3916. 17932. <NA>
10 8590 0.0341 3214. 4126. 17884. <NA>
# i 92 more rows
[[1]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.00253 2.22 0.0000327 0.178 <NA>
2 95.9 0.01 0.748 0.00232 0.0426 <NA>
3 96.0 0.0106 0.716 0.00262 0.0426 <NA>
4 98.0 0.0207 0.482 0.00811 0.0525 <NA>
5 100. 0.0321 0.400 0.0148 0.0690 <NA>
6 102. 0.0444 0.358 0.0223 0.0877 <NA>
7 104. 0.0575 0.332 0.0305 0.107 <NA>
8 106. 0.0712 0.312 0.0393 0.127 <NA>
9 108. 0.0853 0.296 0.0487 0.147 <NA>
10 110. 0.0998 0.282 0.0587 0.167 <NA>
# i 93 more rows
[[2]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.0106 0.355 0.00134 0.0538 <NA>
2 96.0 0.0152 0.307 0.00284 0.0591 <NA>
3 98.0 0.0210 0.270 0.00520 0.0662 <NA>
4 100. 0.0279 0.241 0.00854 0.0748 <NA>
5 102. 0.0359 0.219 0.0129 0.0850 <NA>
6 104. 0.0450 0.201 0.0183 0.0967 <NA>
7 106. 0.0552 0.188 0.0247 0.110 <NA>
8 108. 0.0664 0.178 0.0320 0.124 <NA>
9 110. 0.0785 0.170 0.0402 0.140 <NA>
10 112. 0.0914 0.164 0.0491 0.156 <NA>
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.0109 1.04 0.00143 0.0781 <NA>
2 96.0 0.0153 0.844 0.00295 0.0750 <NA>
3 98.0 0.0205 0.702 0.00527 0.0766 <NA>
4 100. 0.0267 0.597 0.00844 0.0813 <NA>
5 102. 0.0338 0.519 0.0125 0.0882 <NA>
6 104. 0.0419 0.459 0.0175 0.0971 <NA>
7 106. 0.0509 0.414 0.0233 0.108 <NA>
8 108. 0.0608 0.379 0.0299 0.120 <NA>
9 110. 0.0717 0.352 0.0373 0.133 <NA>
10 112. 0.0834 0.330 0.0455 0.148 <NA>
# i 92 more rows
[[1]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.00253 1.69 90.7 97.4 <NA>
2 95.9 0.01 1.62 92.7 99.1 <NA>
3 96.0 0.0106 1.62 92.9 99.2 <NA>
4 98.0 0.0207 1.82 94.5 102. <NA>
5 100. 0.0321 2.11 95.9 104. <NA>
6 102. 0.0444 2.43 97.3 107. <NA>
7 104. 0.0575 2.75 98.7 109. <NA>
8 106. 0.0712 3.06 100. 112. <NA>
9 108. 0.0853 3.35 102. 115. <NA>
10 110. 0.0998 3.62 103. 117. <NA>
# i 93 more rows
[[2]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.0106 4.78 85.1 104. <NA>
2 96.0 0.0152 4.50 87.6 105. <NA>
3 98.0 0.0210 4.29 89.9 107. <NA>
4 100. 0.0279 4.12 92.2 108. <NA>
5 102. 0.0359 4.01 94.4 110. <NA>
6 104. 0.0450 3.94 96.5 112. <NA>
7 106. 0.0552 3.91 98.6 114. <NA>
8 108. 0.0664 3.91 101. 116. <NA>
9 110. 0.0785 3.95 102. 118. <NA>
10 112. 0.0914 4.01 104. 120. <NA>
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.0109 5.64 83.6 106. <NA>
2 96.0 0.0153 5.24 86.2 107. <NA>
3 98.0 0.0205 4.91 88.8 108. <NA>
4 100. 0.0267 4.65 91.3 110. <NA>
5 102. 0.0338 4.44 93.6 111. <NA>
6 104. 0.0419 4.30 95.9 113. <NA>
7 106. 0.0509 4.20 98.0 114. <NA>
8 108. 0.0608 4.14 100. 116. <NA>
9 110. 0.0717 4.12 102. 118. <NA>
10 112. 0.0834 4.13 104. 120. <NA>
# i 92 more rows
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confint_betabinom remains stable
[[1]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 1.00 0.0183 0.000674 0.0927 johnson
2 6910 1.06 0.0199 0.000859 0.0958 johnson
3 7120. 1.13 0.0216 0.00108 0.0990 johnson
4 7330. 1.20 0.0234 0.00134 0.102 johnson
5 7540 1.27 0.0252 0.00165 0.106 johnson
6 7750 1.34 0.0272 0.00201 0.109 johnson
7 7960. 1.42 0.0293 0.00242 0.113 johnson
8 8170 1.51 0.0314 0.00289 0.117 johnson
9 8380 1.59 0.0336 0.00341 0.121 johnson
10 8590 1.68 0.0360 0.00401 0.124 johnson
# i 92 more rows
[[2]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.711 0.0107 0.000128 0.0767 johnson
2 6910 0.772 0.0123 0.000200 0.0802 johnson
3 7120. 0.838 0.0140 0.000302 0.0840 johnson
4 7330. 0.911 0.0159 0.000445 0.0879 johnson
5 7540 0.989 0.0179 0.000636 0.0920 johnson
6 7750 1.07 0.0201 0.000887 0.0962 johnson
7 7960. 1.16 0.0225 0.00121 0.101 johnson
8 8170 1.26 0.0250 0.00161 0.105 johnson
9 8380 1.36 0.0276 0.00209 0.110 johnson
10 8590 1.47 0.0305 0.00268 0.115 johnson
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.911 0.0159 0.000445 0.0879 johnson
2 6910 0.971 0.0175 0.000589 0.0910 johnson
3 7120. 1.04 0.0192 0.000770 0.0943 johnson
4 7330. 1.10 0.0209 0.000991 0.0978 johnson
5 7540 1.18 0.0228 0.00126 0.101 johnson
6 7750 1.25 0.0248 0.00158 0.105 johnson
7 7960. 1.33 0.0269 0.00195 0.109 johnson
8 8170 1.42 0.0291 0.00239 0.113 johnson
9 8380 1.51 0.0314 0.00290 0.117 johnson
10 8590 1.60 0.0339 0.00347 0.121 johnson
# i 92 more rows
[[4]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.39 0.0284 0.00225 0.112 johnson
2 6910 1.43 0.0295 0.00248 0.113 johnson
3 7120. 1.48 0.0307 0.00272 0.115 johnson
4 7330. 1.52 0.0318 0.00299 0.118 johnson
5 7540 1.57 0.0331 0.00328 0.120 johnson
6 7750. 1.62 0.0343 0.00359 0.122 johnson
7 7960. 1.67 0.0357 0.00393 0.124 johnson
8 8170. 1.72 0.0370 0.00429 0.126 johnson
9 8380 1.78 0.0385 0.00469 0.129 johnson
10 8590. 1.83 0.0399 0.00511 0.131 johnson
# i 92 more rows
[[5]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.20 0.0233 0.00133 0.102 johnson
2 6910 1.25 0.0246 0.00155 0.105 johnson
3 7120. 1.30 0.0260 0.00179 0.107 johnson
4 7330. 1.35 0.0275 0.00206 0.110 johnson
5 7540 1.41 0.0290 0.00236 0.112 johnson
6 7750 1.47 0.0305 0.00269 0.115 johnson
7 7960 1.54 0.0322 0.00307 0.118 johnson
8 8170 1.60 0.0339 0.00348 0.121 johnson
9 8380. 1.67 0.0357 0.00394 0.124 johnson
10 8590 1.74 0.0376 0.00444 0.127 johnson
# i 92 more rows
[[6]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.33 0.0268 0.00192 0.109 johnson
2 6910 1.37 0.0279 0.00214 0.111 johnson
3 7120 1.42 0.0291 0.00238 0.113 johnson
4 7330 1.46 0.0303 0.00265 0.115 johnson
5 7540 1.51 0.0316 0.00294 0.117 johnson
6 7750 1.57 0.0329 0.00325 0.119 johnson
7 7960 1.62 0.0343 0.00359 0.122 johnson
8 8170 1.67 0.0358 0.00396 0.124 johnson
9 8380 1.73 0.0373 0.00437 0.127 johnson
10 8590 1.79 0.0389 0.00480 0.129 johnson
# i 92 more rows
[[1]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 1.00 0.0183 2013. 12248. johnson
2 6910 1.06 0.0199 2199. 12404. johnson
3 7120. 1.13 0.0216 2390. 12563. johnson
4 7330. 1.20 0.0234 2587. 12724. johnson
5 7540 1.27 0.0252 2788. 12886. johnson
6 7750 1.34 0.0272 2994. 13050. johnson
7 7960. 1.42 0.0293 3203. 13216. johnson
8 8170 1.51 0.0314 3416. 13383. johnson
9 8380 1.59 0.0336 3631. 13551. johnson
10 8590 1.68 0.0360 3848. 13721. johnson
# i 92 more rows
[[2]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.711 0.0107 3047. 11139. johnson
2 6910 0.772 0.0123 3258. 11295. johnson
3 7120. 0.838 0.0140 3475. 11457. johnson
4 7330. 0.911 0.0159 3697. 11624. johnson
5 7540 0.989 0.0179 3921. 11796. johnson
6 7750 1.07 0.0201 4147. 11973. johnson
7 7960. 1.16 0.0225 4375. 12153. johnson
8 8170 1.26 0.0250 4603. 12337. johnson
9 8380 1.36 0.0276 4830. 12525. johnson
10 8590 1.47 0.0305 5057. 12716. johnson
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.911 0.0159 2114. 11883. johnson
2 6910 0.971 0.0175 2314. 12033. johnson
3 7120. 1.04 0.0192 2522. 12186. johnson
4 7330. 1.10 0.0209 2735. 12341. johnson
5 7540 1.18 0.0228 2953. 12499. johnson
6 7750 1.25 0.0248 3175. 12660. johnson
7 7960. 1.33 0.0269 3401. 12823. johnson
8 8170 1.42 0.0291 3630. 12988. johnson
9 8380 1.51 0.0314 3861. 13156. johnson
10 8590 1.60 0.0339 4094. 13325. johnson
# i 92 more rows
[[4]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.39 0.0284 -7252. 14421. johnson
2 6910 1.43 0.0295 -6726. 14521. johnson
3 7120. 1.48 0.0307 -6207. 14622. johnson
4 7330. 1.52 0.0318 -5695. 14725. johnson
5 7540 1.57 0.0331 -5189. 14828. johnson
6 7750. 1.62 0.0343 -4690. 14933. johnson
7 7960. 1.67 0.0357 -4198. 15039. johnson
8 8170. 1.72 0.0370 -3712. 15146. johnson
9 8380 1.78 0.0385 -3232. 15255. johnson
10 8590. 1.83 0.0399 -2759. 15364. johnson
# i 92 more rows
[[5]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.20 0.0233 -2439. 13197. johnson
2 6910 1.25 0.0246 -2031. 13318. johnson
3 7120. 1.30 0.0260 -1628. 13442. johnson
4 7330. 1.35 0.0275 -1230. 13567. johnson
5 7540 1.41 0.0290 -838. 13695. johnson
6 7750 1.47 0.0305 -451. 13824. johnson
7 7960 1.54 0.0322 -69.4 13955. johnson
8 8170 1.60 0.0339 307. 14087. johnson
9 8380. 1.67 0.0357 678. 14222. johnson
10 8590 1.74 0.0376 1044. 14358. johnson
# i 92 more rows
[[6]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 1.33 0.0268 -6314. 13987. johnson
2 6910 1.37 0.0279 -5784. 14090. johnson
3 7120 1.42 0.0291 -5262. 14194. johnson
4 7330 1.46 0.0303 -4748. 14299. johnson
5 7540 1.51 0.0316 -4241. 14406. johnson
6 7750 1.57 0.0329 -3742. 14514. johnson
7 7960 1.62 0.0343 -3250. 14624. johnson
8 8170 1.67 0.0358 -2766. 14735. johnson
9 8380 1.73 0.0373 -2289. 14847. johnson
10 8590 1.79 0.0389 -1819. 14962. johnson
# i 92 more rows
[[1]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.23 0.0128 0.000769 0.0561 johnson
2 96.0 1.77 0.0203 0.00243 0.0696 johnson
3 98.0 2.39 0.0288 0.00523 0.0837 johnson
4 100. 3.07 0.0383 0.00912 0.0983 johnson
5 102. 3.82 0.0486 0.0140 0.113 johnson
6 104. 4.61 0.0595 0.0199 0.129 johnson
7 106. 5.45 0.0711 0.0266 0.145 johnson
8 108. 6.33 0.0832 0.0340 0.161 johnson
9 110. 7.24 0.0959 0.0422 0.177 johnson
10 111. 7.54 0.1 0.0449 0.182 johnson
# i 92 more rows
[[2]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.53 0.0170 0.00160 0.0639 johnson
2 96.0 1.92 0.0224 0.00304 0.0731 johnson
3 98.0 2.38 0.0287 0.00517 0.0834 johnson
4 100. 2.90 0.0359 0.00808 0.0947 johnson
5 102. 3.49 0.0441 0.0118 0.107 johnson
6 104. 4.15 0.0532 0.0165 0.120 johnson
7 106. 4.88 0.0632 0.0220 0.134 johnson
8 108. 5.66 0.0741 0.0284 0.149 johnson
9 110. 6.51 0.0858 0.0356 0.164 johnson
10 112. 7.41 0.0982 0.0437 0.180 johnson
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.62 0.0182 0.00189 0.0660 johnson
2 96.0 1.99 0.0234 0.00335 0.0748 johnson
3 98.0 2.42 0.0293 0.00541 0.0844 johnson
4 100. 2.91 0.0361 0.00815 0.0949 johnson
5 102. 3.46 0.0437 0.0116 0.106 johnson
6 104. 4.07 0.0521 0.0159 0.118 johnson
7 106. 4.74 0.0614 0.0209 0.131 johnson
8 108. 5.48 0.0715 0.0268 0.145 johnson
9 110. 6.27 0.0824 0.0335 0.159 johnson
10 112. 7.11 0.0941 0.0410 0.175 johnson
# i 92 more rows
[[1]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.23 0.0128 89.3 103. johnson
2 96.0 1.77 0.0203 90.3 106. johnson
3 98.0 2.39 0.0288 91.5 108. johnson
4 100. 3.07 0.0383 92.9 110. johnson
5 102. 3.82 0.0486 94.4 113. johnson
6 104. 4.61 0.0595 95.9 115. johnson
7 106. 5.45 0.0711 97.5 117. johnson
8 108. 6.33 0.0832 99.1 119. johnson
9 110. 7.24 0.0959 101. 122. johnson
10 111. 7.54 0.1 101. 122. johnson
# i 92 more rows
[[2]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.53 0.0170 82.6 106. johnson
2 96.0 1.92 0.0224 85.0 108. johnson
3 98.0 2.38 0.0287 87.3 110. johnson
4 100. 2.90 0.0359 89.5 111. johnson
5 102. 3.49 0.0441 91.7 113. johnson
6 104. 4.15 0.0532 93.8 115. johnson
7 106. 4.88 0.0632 95.8 117. johnson
8 108. 5.66 0.0741 97.9 119. johnson
9 110. 6.51 0.0858 99.9 121. johnson
10 112. 7.41 0.0982 102. 123. johnson
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x rank prob lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 1.62 0.0182 82.5 107. johnson
2 96.0 1.99 0.0234 84.6 109. johnson
3 98.0 2.42 0.0293 86.7 110. johnson
4 100. 2.91 0.0361 88.8 112. johnson
5 102. 3.46 0.0437 90.9 114. johnson
6 104. 4.07 0.0521 93.0 116. johnson
7 106. 4.74 0.0614 95.1 118. johnson
8 108. 5.48 0.0715 97.2 119. johnson
9 110. 6.27 0.0824 99.2 121. johnson
10 112. 7.11 0.0941 101. 123. johnson
# i 92 more rows
confint_fisher remains stable
[[1]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0112 0.916 0.00186 0.0655 <NA>
2 6910 0.0123 0.895 0.00214 0.0691 <NA>
3 7120. 0.0135 0.875 0.00245 0.0729 <NA>
4 7330. 0.0148 0.855 0.00279 0.0767 <NA>
5 7540 0.0162 0.835 0.00317 0.0805 <NA>
6 7750 0.0177 0.817 0.00359 0.0845 <NA>
7 7960. 0.0192 0.799 0.00404 0.0886 <NA>
8 8170 0.0208 0.781 0.00454 0.0927 <NA>
9 8380 0.0225 0.764 0.00509 0.0969 <NA>
10 8590 0.0244 0.747 0.00568 0.101 <NA>
# i 92 more rows
[[2]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.00589 0.431 0.000385 0.0472 <NA>
2 6910 0.00694 0.421 0.000511 0.0509 <NA>
3 7120. 0.00812 0.410 0.000668 0.0549 <NA>
4 7330. 0.00942 0.401 0.000862 0.0589 <NA>
5 7418. 0.01 0.397 0.000956 0.0607 <NA>
6 7540 0.0109 0.391 0.00110 0.0631 <NA>
7 7750 0.0124 0.382 0.00138 0.0675 <NA>
8 7960. 0.0141 0.373 0.00172 0.0719 <NA>
9 8170 0.0160 0.365 0.00212 0.0765 <NA>
10 8380 0.0180 0.357 0.00259 0.0812 <NA>
# i 93 more rows
[[3]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.00906 0.982 0.00133 0.0590 <NA>
2 6891. 0.01 0.961 0.00153 0.0623 <NA>
3 6910 0.0101 0.959 0.00156 0.0626 <NA>
4 7120. 0.0112 0.936 0.00181 0.0663 <NA>
5 7330. 0.0124 0.913 0.00210 0.0701 <NA>
6 7540 0.0137 0.892 0.00242 0.0740 <NA>
7 7750 0.0151 0.871 0.00277 0.0780 <NA>
8 7960. 0.0166 0.851 0.00317 0.0821 <NA>
9 8170 0.0182 0.831 0.00362 0.0863 <NA>
10 8380 0.0199 0.813 0.00410 0.0906 <NA>
# i 93 more rows
[[4]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0286 0.698 0.00735 0.108 <NA>
2 6910 0.0297 0.691 0.00775 0.110 <NA>
3 7120. 0.0308 0.684 0.00816 0.113 <NA>
4 7330. 0.0320 0.676 0.00860 0.115 <NA>
5 7540 0.0332 0.669 0.00906 0.118 <NA>
6 7750 0.0345 0.662 0.00955 0.121 <NA>
7 7960. 0.0358 0.655 0.0101 0.124 <NA>
8 8170 0.0372 0.648 0.0106 0.126 <NA>
9 8380 0.0387 0.641 0.0112 0.129 <NA>
10 8590. 0.0401 0.634 0.0118 0.132 <NA>
# i 92 more rows
[[5]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0162 0.360 0.00222 0.0760 <NA>
2 6910 0.0173 0.356 0.00246 0.0784 <NA>
3 7120 0.0183 0.352 0.00272 0.0808 <NA>
4 7330 0.0195 0.348 0.00301 0.0834 <NA>
5 7540 0.0207 0.344 0.00333 0.0860 <NA>
6 7750 0.0219 0.340 0.00367 0.0886 <NA>
7 7960. 0.0233 0.336 0.00405 0.0914 <NA>
8 8170 0.0247 0.332 0.00445 0.0942 <NA>
9 8380 0.0261 0.328 0.00489 0.0970 <NA>
10 8590 0.0277 0.324 0.00537 0.100 <NA>
# i 92 more rows
[[6]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 0.0232 0.754 0.00538 0.0942 <NA>
2 6910 0.0242 0.745 0.00572 0.0966 <NA>
3 7120 0.0253 0.737 0.00608 0.0990 <NA>
4 7330 0.0264 0.729 0.00646 0.102 <NA>
5 7540 0.0275 0.720 0.00686 0.104 <NA>
6 7750 0.0288 0.712 0.00728 0.107 <NA>
7 7960 0.0300 0.703 0.00774 0.109 <NA>
8 8170 0.0313 0.695 0.00822 0.112 <NA>
9 8380 0.0327 0.687 0.00872 0.115 <NA>
10 8590 0.0341 0.679 0.00926 0.118 <NA>
# i 92 more rows
[[1]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0112 1943. 3795. 11827. <NA>
2 6910 0.0123 1957. 3966. 12038. <NA>
3 7120. 0.0135 1970. 4139. 12247. <NA>
4 7330. 0.0148 1982. 4314. 12453. <NA>
5 7540 0.0162 1993. 4491. 12658. <NA>
6 7750 0.0177 2003. 4670. 12861. <NA>
7 7960. 0.0192 2011. 4851. 13061. <NA>
8 8170 0.0208 2019. 5034. 13260. <NA>
9 8380 0.0225 2025. 5218. 13458. <NA>
10 8590 0.0244 2031. 5404. 13654. <NA>
# i 92 more rows
[[2]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.00589 1532. 4280. 10487. <NA>
2 6910 0.00694 1541. 4464. 10697. <NA>
3 7120. 0.00812 1549. 4648. 10906. <NA>
4 7330. 0.00942 1556. 4835. 11113. <NA>
5 7418. 0.01 1559. 4913. 11200. <NA>
6 7540 0.0109 1563. 5022. 11320. <NA>
7 7750 0.0124 1569. 5211. 11526. <NA>
8 7960. 0.0141 1575. 5401. 11731. <NA>
9 8170 0.0160 1580. 5593. 11935. <NA>
10 8380 0.0180 1584. 5785. 12139. <NA>
# i 93 more rows
[[3]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.00906 1850. 3900. 11510. <NA>
2 6891. 0.01 1860. 4060. 11697. <NA>
3 6910 0.0101 1861. 4076. 11716. <NA>
4 7120. 0.0112 1872. 4253. 11920. <NA>
5 7330. 0.0124 1881. 4432. 12122. <NA>
6 7540 0.0137 1890. 4614. 12322. <NA>
7 7750 0.0151 1897. 4797. 12521. <NA>
8 7960. 0.0166 1903. 4982. 12719. <NA>
9 8170 0.0182 1909. 5168. 12915. <NA>
10 8380 0.0199 1913. 5357. 13110. <NA>
# i 93 more rows
[[4]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0286 3810. 2198. 20424. <NA>
2 6910 0.0297 3771. 2371. 20139. <NA>
3 7120. 0.0308 3732. 2548. 19892. <NA>
4 7330. 0.0320 3694. 2730. 19680. <NA>
5 7540 0.0332 3655. 2916. 19497. <NA>
6 7750 0.0345 3616. 3105. 19341. <NA>
7 7960. 0.0358 3578. 3299. 19209. <NA>
8 8170 0.0372 3539. 3495. 19097. <NA>
9 8380 0.0387 3501. 3695. 19005. <NA>
10 8590. 0.0401 3463. 3898. 18929. <NA>
# i 92 more rows
[[5]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700. 0.0162 3058. 2739. 16388. <NA>
2 6910 0.0173 3022. 2932. 16284. <NA>
3 7120 0.0183 2987. 3129. 16202. <NA>
4 7330 0.0195 2952. 3329. 16139. <NA>
5 7540 0.0207 2917. 3532. 16095. <NA>
6 7750 0.0219 2883. 3739. 16066. <NA>
7 7960. 0.0233 2848. 3947. 16051. <NA>
8 8170 0.0247 2815. 4159. 16049. <NA>
9 8380 0.0261 2781. 4373. 16059. <NA>
10 8590 0.0277 2748. 4589. 16079. <NA>
# i 92 more rows
[[6]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 6700 0.0232 3570. 2358. 19039. <NA>
2 6910 0.0242 3530. 2539. 18806. <NA>
3 7120 0.0253 3490. 2725. 18606. <NA>
4 7330 0.0264 3450. 2914. 18437. <NA>
5 7540 0.0275 3410. 3108. 18294. <NA>
6 7750 0.0288 3370. 3305. 18174. <NA>
7 7960 0.0300 3331. 3505. 18076. <NA>
8 8170 0.0313 3292. 3709. 17995. <NA>
9 8380 0.0327 3253. 3916. 17932. <NA>
10 8590 0.0341 3214. 4126. 17884. <NA>
# i 92 more rows
[[1]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.00253 2.22 0.0000327 0.178 <NA>
2 95.9 0.01 0.748 0.00232 0.0426 <NA>
3 96.0 0.0106 0.716 0.00262 0.0426 <NA>
4 98.0 0.0207 0.482 0.00811 0.0525 <NA>
5 100. 0.0321 0.400 0.0148 0.0690 <NA>
6 102. 0.0444 0.358 0.0223 0.0877 <NA>
7 104. 0.0575 0.332 0.0305 0.107 <NA>
8 106. 0.0712 0.312 0.0393 0.127 <NA>
9 108. 0.0853 0.296 0.0487 0.147 <NA>
10 110. 0.0998 0.282 0.0587 0.167 <NA>
# i 93 more rows
[[2]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.0106 0.355 0.00134 0.0538 <NA>
2 96.0 0.0152 0.307 0.00284 0.0591 <NA>
3 98.0 0.0210 0.270 0.00520 0.0662 <NA>
4 100. 0.0279 0.241 0.00854 0.0748 <NA>
5 102. 0.0359 0.219 0.0129 0.0850 <NA>
6 104. 0.0450 0.201 0.0183 0.0967 <NA>
7 106. 0.0552 0.188 0.0247 0.110 <NA>
8 108. 0.0664 0.178 0.0320 0.124 <NA>
9 110. 0.0785 0.170 0.0402 0.140 <NA>
10 112. 0.0914 0.164 0.0491 0.156 <NA>
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.0109 1.04 0.00143 0.0781 <NA>
2 96.0 0.0153 0.844 0.00295 0.0750 <NA>
3 98.0 0.0205 0.702 0.00527 0.0766 <NA>
4 100. 0.0267 0.597 0.00844 0.0813 <NA>
5 102. 0.0338 0.519 0.0125 0.0882 <NA>
6 104. 0.0419 0.459 0.0175 0.0971 <NA>
7 106. 0.0509 0.414 0.0233 0.108 <NA>
8 108. 0.0608 0.379 0.0299 0.120 <NA>
9 110. 0.0717 0.352 0.0373 0.133 <NA>
10 112. 0.0834 0.330 0.0455 0.148 <NA>
# i 92 more rows
[[1]]
# A tibble: 103 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.00253 1.69 90.7 97.4 <NA>
2 95.9 0.01 1.62 92.7 99.1 <NA>
3 96.0 0.0106 1.62 92.9 99.2 <NA>
4 98.0 0.0207 1.82 94.5 102. <NA>
5 100. 0.0321 2.11 95.9 104. <NA>
6 102. 0.0444 2.43 97.3 107. <NA>
7 104. 0.0575 2.75 98.7 109. <NA>
8 106. 0.0712 3.06 100. 112. <NA>
9 108. 0.0853 3.35 102. 115. <NA>
10 110. 0.0998 3.62 103. 117. <NA>
# i 93 more rows
[[2]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.0106 4.78 85.1 104. <NA>
2 96.0 0.0152 4.50 87.6 105. <NA>
3 98.0 0.0210 4.29 89.9 107. <NA>
4 100. 0.0279 4.12 92.2 108. <NA>
5 102. 0.0359 4.01 94.4 110. <NA>
6 104. 0.0450 3.94 96.5 112. <NA>
7 106. 0.0552 3.91 98.6 114. <NA>
8 108. 0.0664 3.91 101. 116. <NA>
9 110. 0.0785 3.95 102. 118. <NA>
10 112. 0.0914 4.01 104. 120. <NA>
# i 92 more rows
[[3]]
# A tibble: 102 x 6
x prob std_err lower_bound upper_bound cdf_estimation_method
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 94 0.0109 5.64 83.6 106. <NA>
2 96.0 0.0153 5.24 86.2 107. <NA>
3 98.0 0.0205 4.91 88.8 108. <NA>
4 100. 0.0267 4.65 91.3 110. <NA>
5 102. 0.0338 4.44 93.6 111. <NA>
6 104. 0.0419 4.30 95.9 113. <NA>
7 106. 0.0509 4.20 98.0 114. <NA>
8 108. 0.0608 4.14 100. 116. <NA>
9 110. 0.0717 4.12 102. 118. <NA>
10 112. 0.0834 4.13 104. 120. <NA>
# i 92 more rows
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