| |
| R version 3.6.2 Patched (2020-02-12 r77795) -- "Dark and Stormy Night" |
| Copyright (C) 2020 The R Foundation for Statistical Computing |
| Platform: x86_64-pc-linux-gnu (64-bit) |
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| |
| > ## tests of the simulate.lm method, added Feb 2009 |
| > |
| > options(digits = 5) |
| > |
| > ## cases should be named |
| > hills <- readRDS("hills.rds") # copied from package MASS |
| > fit1 <- lm(time ~ dist, data = hills) |
| > set.seed(1) |
| > simulate(fit1, nsim = 3) |
| sim_1 sim_2 sim_3 |
| Greenmantle 3.4841 7.7039 25.4746 |
| Carnethy 48.8068 37.2737 30.9745 |
| Craig Dunain 28.4665 43.9584 57.3295 |
| Ben Rha 89.4727 79.5895 38.9971 |
| Ben Lomond 68.3785 77.0326 36.7858 |
| Goatfell 45.4299 58.5197 67.6189 |
| Bens of Jura 138.1736 123.3906 119.6004 |
| Cairnpapple 59.8758 59.0504 45.1641 |
| Scolty 48.3017 47.9202 38.2951 |
| Traprain 39.0478 31.3974 33.3777 |
| Lairig Ghru 258.5807 214.2935 217.0639 |
| Dollar 44.5912 44.0871 34.1140 |
| Lomonds 61.9013 89.6352 97.8082 |
| Cairn Table 0.9461 42.9001 14.7382 |
| Eildon Two 55.0951 50.2295 44.4990 |
| Cairngorm 77.5672 86.4083 85.1081 |
| Seven Hills 111.4626 99.5722 133.0006 |
| Knock Hill 38.9856 26.9579 14.0804 |
| Black Hill 49.0344 10.1091 40.0303 |
| Creag Beag 52.8285 69.5738 46.3069 |
| Kildcon Hill 38.4895 59.6709 9.3243 |
| Meall Ant-Suidhe 39.9240 16.9877 48.4197 |
| Half Ben Nevis 46.6300 24.3056 68.2987 |
| Cow Hill -27.8787 23.1894 25.7935 |
| N Berwick Law 32.5197 17.4555 51.8170 |
| Creag Dubh 27.3610 76.4071 39.6261 |
| Burnswark 42.0330 44.3590 19.6667 |
| Largo Law 7.4616 50.5758 25.3716 |
| Criffel 39.7654 49.8660 24.8692 |
| Acmony 45.1519 21.9790 27.3645 |
| Ben Nevis 105.5773 82.2313 66.0840 |
| Knockfarrel 43.0908 9.1228 45.9825 |
| Two Breweries 152.8438 174.3537 126.9294 |
| Cockleroi 31.5726 35.7046 35.7999 |
| Moffat Chase 134.2882 205.1244 148.7057 |
| > |
| > ## and weights should be taken into account |
| > fit2 <- lm(time ~ -1 + dist + climb, hills[-18, ], weight = 1/dist^2) |
| > coef(summary(fit2)) |
| Estimate Std. Error t value Pr(>|t|) |
| dist 4.8999847 0.4737032 10.3440 9.8468e-12 |
| climb 0.0084718 0.0016869 5.0221 1.8636e-05 |
| > set.seed(1); ( ys <- simulate(fit2, nsim = 3) ) |
| sim_1 sim_2 sim_3 |
| Greenmantle 15.754 13.355 18.247 |
| Carnethy 51.988 47.396 67.247 |
| Craig Dunain 30.614 34.000 40.672 |
| Ben Rha 58.825 42.959 36.719 |
| Ben Lomond 68.579 76.460 71.455 |
| Goatfell 55.088 71.287 53.926 |
| Bens of Jura 151.910 138.573 116.292 |
| Cairnpapple 41.841 34.234 38.413 |
| Scolty 34.958 35.733 28.443 |
| Traprain 32.564 39.177 34.915 |
| Lairig Ghru 209.113 130.333 157.652 |
| Dollar 43.936 36.921 37.675 |
| Lomonds 57.642 69.616 58.280 |
| Cairn Table 16.646 39.532 32.599 |
| Eildon Two 41.230 34.111 41.536 |
| Cairngorm 73.841 85.681 54.935 |
| Seven Hills 86.948 94.364 97.870 |
| Black Hill 35.952 27.000 32.437 |
| Creag Beag 37.808 34.432 39.509 |
| Kildcon Hill 19.520 12.910 16.075 |
| Meall Ant-Suidhe 33.970 36.271 31.514 |
| Half Ben Nevis 54.038 63.231 50.087 |
| Cow Hill 17.615 16.486 16.037 |
| N Berwick Law 12.152 15.778 24.416 |
| Creag Dubh 39.714 39.457 42.478 |
| Burnswark 35.747 35.141 41.549 |
| Largo Law 31.552 47.902 42.693 |
| Criffel 34.452 46.350 51.317 |
| Acmony 25.679 33.145 20.575 |
| Ben Nevis 91.620 86.634 78.946 |
| Knockfarrel 44.906 28.781 25.088 |
| Two Breweries 129.888 136.598 121.358 |
| Cockleroi 31.482 18.866 25.682 |
| Moffat Chase 138.983 177.836 141.436 |
| > for(i in seq_len(3)) |
| + print(coef(summary(update(fit2, ys[, i] ~ .)))) |
| Estimate Std. Error t value Pr(>|t|) |
| dist 4.8759333 0.4295826 11.3504 9.3646e-13 |
| climb 0.0091824 0.0015298 6.0023 1.0781e-06 |
| Estimate Std. Error t value Pr(>|t|) |
| dist 4.6969341 0.4406227 10.66 4.6417e-12 |
| climb 0.0099795 0.0015691 6.36 3.8442e-07 |
| Estimate Std. Error t value Pr(>|t|) |
| dist 4.8215499 0.420077 11.4778 7.0162e-13 |
| climb 0.0090388 0.001496 6.0422 9.6065e-07 |
| > ## should be identical to glm(*, gaussian): |
| > fit2. <- glm(time ~ -1 + dist + climb, family=gaussian, data=hills[-18, ], |
| + weight = 1/dist^2) |
| > set.seed(1); ys. <- simulate(fit2., nsim = 3) |
| > stopifnot(all.equal(ys, ys.)) |
| > |
| > ## Poisson fit |
| > load("anorexia.rda") # copied from package MASS |
| > fit3 <- glm(Postwt ~ Prewt + Treat + offset(Prewt), |
| + family = gaussian, data = anorexia) |
| > coef(summary(fit3)) |
| Estimate Std. Error t value Pr(>|t|) |
| (Intercept) 49.77111 13.39096 3.7168 0.00041011 |
| Prewt -0.56554 0.16118 -3.5087 0.00080343 |
| TreatCont -4.09707 1.89349 -2.1638 0.03399931 |
| TreatFT 4.56306 2.13334 2.1389 0.03603508 |
| > set.seed(1) |
| > simulate(fit3, nsim = 8) |
| sim_1 sim_2 sim_3 sim_4 sim_5 sim_6 sim_7 sim_8 |
| 1 76.364 84.997 72.948 78.581 83.762 62.645 70.046 87.655 |
| 2 85.796 77.997 79.276 75.769 91.529 93.684 84.340 95.638 |
| 3 79.726 76.809 100.122 90.039 82.835 81.123 89.696 75.979 |
| 4 88.956 79.858 77.946 77.512 80.451 74.824 76.441 76.082 |
| 5 81.905 76.512 70.629 67.511 81.309 78.424 85.830 87.696 |
| 6 78.312 84.045 72.589 84.052 74.084 88.309 83.858 76.262 |
| 7 87.004 84.121 86.744 79.204 96.013 88.336 79.083 65.958 |
| 8 83.454 74.188 78.173 75.923 79.240 82.265 82.812 71.771 |
| 9 84.710 76.723 78.472 72.621 86.034 76.696 77.664 73.942 |
| 10 77.605 78.792 73.251 92.318 86.401 70.223 92.105 80.067 |
| 11 89.938 87.609 69.008 77.078 79.035 76.676 79.261 76.571 |
| 12 86.931 73.579 76.708 73.007 82.077 86.150 90.162 85.826 |
| 13 76.661 85.140 87.974 82.372 87.232 75.252 82.427 78.048 |
| 14 64.151 81.929 75.270 81.442 72.297 79.125 58.615 82.216 |
| 15 84.154 83.722 66.643 69.424 90.060 68.155 66.771 73.749 |
| 16 78.944 77.135 92.302 59.098 76.581 79.200 76.298 87.563 |
| 17 82.577 85.272 85.657 78.221 94.233 83.589 84.343 77.545 |
| 18 89.624 84.902 81.372 87.019 93.590 82.020 66.690 85.066 |
| 19 87.943 78.426 89.599 81.795 82.791 81.068 88.923 76.038 |
| 20 84.445 88.729 86.486 79.615 84.259 92.607 76.083 81.752 |
| 21 89.233 90.918 78.499 86.734 75.671 88.142 77.567 82.487 |
| 22 87.800 87.229 97.737 74.063 84.597 90.098 71.487 70.588 |
| 23 80.777 91.330 78.478 87.911 87.540 73.815 70.112 79.251 |
| 24 65.463 83.242 69.404 79.307 80.036 80.492 79.738 87.581 |
| 25 81.411 68.177 76.078 82.021 73.917 85.144 80.640 81.841 |
| 26 83.949 80.341 85.789 91.557 79.765 83.947 69.702 85.341 |
| 27 83.658 76.200 100.851 86.305 84.495 69.886 77.737 76.425 |
| 28 76.394 83.353 87.395 80.525 94.118 89.063 90.396 94.816 |
| 29 81.843 80.851 88.369 93.295 81.802 71.887 82.018 85.732 |
| 30 88.574 85.951 85.119 71.700 84.813 79.997 100.768 82.505 |
| 31 93.966 78.128 82.154 80.683 75.454 93.724 93.178 95.943 |
| 32 87.591 89.411 88.065 86.524 91.757 92.604 92.463 82.937 |
| 33 93.707 86.434 96.498 89.842 100.128 98.619 91.036 93.118 |
| 34 82.545 95.253 97.402 90.041 93.367 85.060 84.870 91.865 |
| 35 75.353 89.964 92.132 85.913 90.648 84.194 80.037 89.165 |
| 36 81.849 91.097 93.174 87.587 71.698 78.295 89.128 82.603 |
| 37 83.949 89.381 78.108 86.214 90.064 97.816 97.030 83.781 |
| 38 88.111 100.264 95.391 86.797 91.708 88.839 96.085 91.003 |
| 39 92.769 80.657 86.627 89.946 82.627 80.103 79.418 88.676 |
| 40 88.333 79.786 72.769 91.006 84.197 89.045 71.711 83.137 |
| 41 79.035 90.178 83.819 63.414 74.154 87.681 79.418 89.384 |
| 42 82.934 80.161 83.594 88.698 89.442 97.930 87.778 84.242 |
| 43 90.825 84.515 96.182 88.577 83.679 81.754 95.389 81.075 |
| 44 89.716 83.090 80.486 82.864 74.882 83.104 76.630 89.581 |
| 45 83.067 85.640 84.871 94.510 85.309 84.969 90.416 72.509 |
| 46 81.416 84.405 79.890 83.637 95.874 83.731 87.982 89.088 |
| 47 89.853 90.757 86.073 85.324 84.976 84.750 95.640 90.776 |
| 48 88.370 81.770 85.813 88.991 88.121 80.944 82.813 81.438 |
| 49 83.831 81.084 79.509 96.615 91.220 94.676 82.122 76.819 |
| 50 94.065 97.289 93.711 89.801 87.947 83.049 79.914 85.160 |
| 51 88.740 84.464 77.532 83.016 83.503 83.253 82.351 96.777 |
| 52 80.127 83.145 77.085 76.100 80.701 88.951 81.871 79.209 |
| 53 88.863 85.784 96.540 84.173 91.644 94.332 102.886 70.212 |
| 54 76.995 89.849 77.787 78.317 77.455 79.488 101.948 90.544 |
| 55 97.743 87.230 90.618 85.936 89.461 84.197 86.580 84.245 |
| 56 104.562 90.479 88.083 93.494 88.722 94.396 83.459 87.177 |
| 57 87.962 85.768 93.382 84.580 74.720 97.627 76.757 82.044 |
| 58 84.412 89.435 103.483 110.184 81.867 89.945 95.289 91.540 |
| 59 94.153 90.597 101.249 91.266 96.569 80.198 82.567 95.071 |
| 60 91.060 87.893 89.693 99.889 90.667 103.929 107.945 87.902 |
| 61 105.676 92.626 72.970 72.943 94.523 98.931 82.737 84.683 |
| 62 87.470 77.149 105.173 92.915 100.915 82.787 88.520 95.397 |
| 63 100.074 97.400 99.915 86.075 105.545 94.806 121.849 93.533 |
| 64 86.419 75.502 90.001 92.642 90.950 73.946 78.485 85.108 |
| 65 84.122 87.208 89.215 92.087 91.960 93.284 91.455 84.941 |
| 66 91.104 86.100 93.346 86.942 88.441 101.037 82.062 96.070 |
| 67 77.408 85.453 88.856 99.244 101.014 78.578 92.429 83.066 |
| 68 98.275 87.651 90.984 83.155 92.209 82.608 81.955 93.975 |
| 69 91.681 77.253 87.819 86.560 82.422 86.137 91.151 96.234 |
| 70 108.553 101.603 83.831 86.407 92.306 88.639 91.321 90.129 |
| 71 95.016 80.079 98.591 87.035 78.307 77.509 83.441 97.618 |
| 72 87.308 89.028 102.868 98.858 90.900 95.758 92.341 99.148 |
| > |
| > ## two-column binomial fit |
| > ldose <- rep(0:5, 2) |
| > numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) |
| > sex <- factor(rep(c("M", "F"), c(6, 6))) |
| > SF <- cbind(numdead, numalive = 20 - numdead) |
| > fit4 <- glm(SF ~ sex + ldose - 1, family = binomial) |
| > coef(summary(fit4)) |
| Estimate Std. Error z value Pr(>|z|) |
| sexF -3.4732 0.46852 -7.4130 1.2344e-13 |
| sexM -2.3724 0.38551 -6.1539 7.5579e-10 |
| ldose 1.0642 0.13108 8.1190 4.7015e-16 |
| > set.seed(1) |
| > ( ys <- simulate(fit4, nsim = 3) ) |
| sim_1.numdead sim_1.numalive sim_2.numdead sim_2.numalive sim_3.numdead |
| 1 1 19 2 18 1 |
| 2 4 16 4 16 4 |
| 3 9 11 10 10 4 |
| 4 11 9 14 6 15 |
| 5 19 1 17 3 16 |
| 6 18 2 16 4 20 |
| 7 2 18 0 20 0 |
| 8 2 18 3 17 2 |
| 9 5 15 7 13 4 |
| 10 5 15 7 13 7 |
| 11 15 5 13 7 12 |
| 12 19 1 19 1 17 |
| sim_3.numalive |
| 1 19 |
| 2 16 |
| 3 16 |
| 4 5 |
| 5 4 |
| 6 0 |
| 7 20 |
| 8 18 |
| 9 16 |
| 10 13 |
| 11 8 |
| 12 3 |
| > for(i in seq_len(3)) |
| + print(coef(summary(update(fit4, ys[, i] ~ .)))) |
| Estimate Std. Error z value Pr(>|z|) |
| sexF -3.3079 0.45218 -7.3154 2.5656e-13 |
| sexM -2.5067 0.39240 -6.3880 1.6812e-10 |
| ldose 1.0482 0.12869 8.1454 3.7800e-16 |
| Estimate Std. Error z value Pr(>|z|) |
| sexF -2.84478 0.40661 -6.9963 2.6289e-12 |
| sexM -2.11845 0.35818 -5.9145 3.3286e-09 |
| ldose 0.90935 0.11578 7.8541 4.0273e-15 |
| Estimate Std. Error z value Pr(>|z|) |
| sexF -3.9838 0.52156 -7.6384 2.1996e-14 |
| sexM -2.8717 0.43047 -6.6712 2.5376e-11 |
| ldose 1.1487 0.14102 8.1459 3.7655e-16 |
| > |
| > ## same via proportions |
| > fit5 <- glm(numdead/20 ~ sex + ldose - 1, family = binomial, |
| + weights = rep(20, 12)) |
| > set.seed(1) |
| > ( ys <- simulate(fit5, nsim = 3) ) |
| sim_1 sim_2 sim_3 |
| 1 0.05 0.10 0.05 |
| 2 0.20 0.20 0.20 |
| 3 0.45 0.50 0.20 |
| 4 0.55 0.70 0.75 |
| 5 0.95 0.85 0.80 |
| 6 0.90 0.80 1.00 |
| 7 0.10 0.00 0.00 |
| 8 0.10 0.15 0.10 |
| 9 0.25 0.35 0.20 |
| 10 0.25 0.35 0.35 |
| 11 0.75 0.65 0.60 |
| 12 0.95 0.95 0.85 |
| > for(i in seq_len(3)) |
| + print(coef(summary(update(fit5, ys[, i] ~ .)))) |
| Estimate Std. Error z value Pr(>|z|) |
| sexF -3.3079 0.45218 -7.3154 2.5656e-13 |
| sexM -2.5067 0.39240 -6.3880 1.6812e-10 |
| ldose 1.0482 0.12869 8.1454 3.7800e-16 |
| Estimate Std. Error z value Pr(>|z|) |
| sexF -2.84478 0.40661 -6.9963 2.6289e-12 |
| sexM -2.11845 0.35818 -5.9145 3.3286e-09 |
| ldose 0.90935 0.11578 7.8541 4.0273e-15 |
| Estimate Std. Error z value Pr(>|z|) |
| sexF -3.9838 0.52156 -7.6384 2.1996e-14 |
| sexM -2.8717 0.43047 -6.6712 2.5376e-11 |
| ldose 1.1487 0.14102 8.1459 3.7655e-16 |
| > |
| > |
| > ## factor binomial fit |
| > load("birthwt.rda") # copied from package MASS |
| > bwt <- with(birthwt, { |
| + race <- factor(race, labels = c("white", "black", "other")) |
| + table(ptl) |
| + ptd <- factor(ptl > 0) |
| + table(ftv) |
| + ftv <- factor(ftv) |
| + levels(ftv)[-(1:2)] <- "2+" |
| + data.frame(low = factor(low), age, lwt, race, |
| + smoke = (smoke > 0), ptd, ht = (ht > 0), ui = (ui > 0), ftv) |
| + }) |
| > fit6 <- glm(low ~ ., family = binomial, data = bwt) |
| > coef(summary(fit6)) |
| Estimate Std. Error z value Pr(>|z|) |
| (Intercept) 0.823019 1.2447143 0.66121 0.5084769 |
| age -0.037234 0.0387024 -0.96207 0.3360159 |
| lwt -0.015653 0.0070804 -2.21075 0.0270532 |
| raceblack 1.192413 0.5359646 2.22480 0.0260948 |
| raceother 0.740685 0.4617443 1.60410 0.1086916 |
| smokeTRUE 0.755528 0.4250166 1.77764 0.0754623 |
| ptdTRUE 1.343763 0.4806207 2.79589 0.0051757 |
| htTRUE 1.913166 0.7207369 2.65446 0.0079436 |
| uiTRUE 0.680195 0.4643403 1.46486 0.1429580 |
| ftv1 -0.436380 0.4793936 -0.91027 0.3626779 |
| ftv2+ 0.179009 0.4563778 0.39224 0.6948827 |
| > set.seed(1) |
| > ys <- simulate(fit6, nsim = 3) |
| > ys[1:10, ] |
| sim_1 sim_2 sim_3 |
| 1 0 0 0 |
| 2 0 0 0 |
| 3 0 0 0 |
| 4 1 0 1 |
| 5 0 1 1 |
| 6 1 0 1 |
| 7 1 0 0 |
| 8 0 0 0 |
| 9 0 0 0 |
| 10 0 0 1 |
| > for(i in seq_len(3)) |
| + print(coef(summary(update(fit6, ys[, i] ~ .)))) |
| Estimate Std. Error z value Pr(>|z|) |
| (Intercept) -0.284354 1.2297301 -0.23123 0.817134 |
| age -0.011485 0.0389166 -0.29513 0.767897 |
| lwt -0.012314 0.0069041 -1.78353 0.074500 |
| raceblack 0.844051 0.5514350 1.53064 0.125857 |
| raceother 0.565993 0.4665978 1.21302 0.225122 |
| smokeTRUE 1.024337 0.4253416 2.40827 0.016028 |
| ptdTRUE 1.578045 0.4887012 3.22906 0.001242 |
| htTRUE 1.171556 0.7060321 1.65935 0.097045 |
| uiTRUE 0.384214 0.4835840 0.79451 0.426897 |
| ftv1 -0.790699 0.5271049 -1.50008 0.133594 |
| ftv2+ 0.522245 0.4498448 1.16094 0.245665 |
| Estimate Std. Error z value Pr(>|z|) |
| (Intercept) 0.916922 1.433819 0.63950 5.2250e-01 |
| age 0.034662 0.041752 0.83019 4.0643e-01 |
| lwt -0.036299 0.009577 -3.79021 1.5052e-04 |
| raceblack 2.588992 0.631372 4.10058 4.1211e-05 |
| raceother 1.073046 0.522576 2.05338 4.0036e-02 |
| smokeTRUE 0.710930 0.478870 1.48460 1.3765e-01 |
| ptdTRUE 0.679588 0.503299 1.35027 1.7693e-01 |
| htTRUE 1.988224 0.801811 2.47967 1.3151e-02 |
| uiTRUE 1.080176 0.489033 2.20880 2.7189e-02 |
| ftv1 -0.418153 0.549009 -0.76165 4.4627e-01 |
| ftv2+ 0.657962 0.500605 1.31433 1.8873e-01 |
| Estimate Std. Error z value Pr(>|z|) |
| (Intercept) 1.4927579 1.2779154 1.168119 0.24275859 |
| age -0.0986485 0.0415094 -2.376532 0.01747625 |
| lwt -0.0088426 0.0070635 -1.251885 0.21061194 |
| raceblack 0.2106382 0.5622160 0.374657 0.70791549 |
| raceother 0.8608985 0.4814556 1.788116 0.07375727 |
| smokeTRUE 0.7884781 0.4449757 1.771958 0.07640158 |
| ptdTRUE 1.9686209 0.5435116 3.622040 0.00029229 |
| htTRUE 1.7835637 0.7279712 2.450047 0.01428375 |
| uiTRUE 1.4816743 0.5139743 2.882779 0.00394184 |
| ftv1 -0.7388479 0.5090637 -1.451386 0.14667244 |
| ftv2+ -0.0097438 0.4670024 -0.020865 0.98335371 |
| > |
| > ## This requires MASS::gamma.shape |
| > if(!require("MASS")) q() |
| Loading required package: MASS |
| > |
| > ## gamma fit, from example(glm) |
| > clotting <- data.frame(u = c(5,10,15,20,30,40,60,80,100), |
| + lot1 = c(118,58,42,35,27,25,21,19,18)) |
| > fit7 <- glm(lot1 ~ log(u), data = clotting, family = Gamma) |
| > coef(summary(fit7)) |
| Estimate Std. Error t value Pr(>|t|) |
| (Intercept) -0.016554 0.00092755 -17.847 4.2791e-07 |
| log(u) 0.015343 0.00041496 36.975 2.7512e-09 |
| > set.seed(1) |
| > ( ys <- simulate(fit7, nsim = 3) ) |
| sim_1 sim_2 sim_3 |
| 1 119.451 118.552 123.140 |
| 2 56.310 51.798 48.747 |
| 3 42.193 39.456 40.843 |
| 4 34.581 33.905 35.580 |
| 5 26.208 26.971 27.210 |
| 6 25.476 25.840 24.437 |
| 7 22.287 22.151 21.375 |
| 8 20.206 20.503 19.626 |
| 9 18.224 19.094 17.952 |
| > for(i in seq_len(3)) |
| + print(coef(summary(update(fit7, ys[, i] ~ .)))) |
| Estimate Std. Error t value Pr(>|t|) |
| (Intercept) -0.016008 0.00077887 -20.553 1.6197e-07 |
| log(u) 0.015044 0.00034611 43.465 8.9084e-10 |
| Estimate Std. Error t value Pr(>|t|) |
| (Intercept) -0.015483 0.00050102 -30.903 9.5910e-09 |
| log(u) 0.014935 0.00021999 67.889 3.9547e-11 |
| Estimate Std. Error t value Pr(>|t|) |
| (Intercept) -0.016850 0.00075120 -22.431 8.8554e-08 |
| log(u) 0.015569 0.00033659 46.256 5.7704e-10 |
| > |
| > |
| > proc.time() |
| user system elapsed |
| 0.277 0.062 0.318 |
