| |
| 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) |
| |
| R is free software and comes with ABSOLUTELY NO WARRANTY. |
| You are welcome to redistribute it under certain conditions. |
| Type 'license()' or 'licence()' for distribution details. |
| |
| R is a collaborative project with many contributors. |
| Type 'contributors()' for more information and |
| 'citation()' on how to cite R or R packages in publications. |
| |
| Type 'demo()' for some demos, 'help()' for on-line help, or |
| 'help.start()' for an HTML browser interface to help. |
| Type 'q()' to quit R. |
| |
| > ###-- Linear Models, basic functionality -- weights included. |
| > |
| > ## From John Maindonald : |
| > roller <- data.frame( |
| + weight = c(1.9, 3.1, 3.3, 4.8, 5.3, 6.1, 6.4, 7.6, 9.8, 12.4), |
| + depression = c( 2, 1, 5, 5, 20, 20, 23, 10, 30, 25)) |
| > |
| > roller.lmu <- lm(weight~depression, data=roller) |
| > roller.lsfu <- lsfit(roller$depression, roller$weight) |
| > |
| > roller.lsf <- lsfit(roller$depression, roller$weight, wt = 1:10) |
| > roller.lsf0 <- lsfit(roller$depression, roller$weight, wt = 0:9) |
| > roller.lm <- lm(weight~depression, data=roller, weights= 1:10) |
| > roller.lm0 <- lm(weight~depression, data=roller, weights= 0:9) |
| > roller.lm9 <- lm(weight~depression, data=roller[-1,],weights= 1:9) |
| > roller.glm <- glm(weight~depression, data=roller, weights= 1:10) |
| > roller.glm0<- glm(weight~depression, data=roller, weights= 0:9) |
| > |
| > predict(roller.glm0, type="terms")# failed till 2003-03-31 |
| depression |
| 1 -2.6692211 |
| 2 -2.8898179 |
| 3 -2.0074308 |
| 4 -2.0074308 |
| 5 1.3015211 |
| 6 1.3015211 |
| 7 1.9633114 |
| 8 -0.9044468 |
| 9 3.5074889 |
| 10 2.4045050 |
| attr(,"constant") |
| [1] 6.743646 |
| > |
| > ## FIXME : glm()$residual [1] is NA, lm()'s is ok. |
| > ## all.equal(residuals(roller.glm0, type = "partial"), |
| > ## residuals(roller.lm0, type = "partial") ) |
| > |
| > |
| > all.equal(deviance(roller.lm), |
| + deviance(roller.glm)) |
| [1] TRUE |
| > all.equal(weighted.residuals(roller.lm), |
| + residuals (roller.glm)) |
| [1] TRUE |
| > |
| > all.equal(deviance(roller.lm0), |
| + deviance(roller.glm0)) |
| [1] TRUE |
| > all.equal(weighted.residuals(roller.lm0, drop=FALSE), |
| + residuals (roller.glm0)) |
| [1] TRUE |
| > |
| > (im.lm0 <- influence.measures(roller.lm0)) |
| Influence measures of |
| lm(formula = weight ~ depression, data = roller, weights = 0:9) : |
| |
| dfb.1_ dfb.dprs dffit cov.r cook.d hat inf |
| 2 -0.0530 0.0482 -0.0530 1.551 0.00164 0.1277 |
| 3 -0.1769 0.1538 -0.1782 1.569 0.01806 0.1742 |
| 4 0.0130 -0.0113 0.0131 1.842 0.00010 0.2613 |
| 5 -0.1174 -0.0185 -0.3370 1.049 0.05552 0.0892 |
| 6 -0.1031 -0.0162 -0.2960 1.229 0.04577 0.1114 |
| 7 -0.0118 -0.1891 -0.5010 1.070 0.11932 0.1555 |
| 8 0.7572 -0.5948 0.7972 1.467 0.30998 0.3510 |
| 9 0.1225 -0.2067 -0.2664 2.391 0.04079 0.4470 * |
| 10 -0.3348 1.1321 2.0937 0.233 0.89584 0.2826 * |
| > |
| > all.equal(unname(im.lm0 $ infmat), |
| + unname(cbind( dfbetas (roller.lm0) |
| + , dffits (roller.lm0) |
| + , covratio (roller.lm0) |
| + ,cooks.distance(roller.lm0) |
| + ,lm.influence (roller.lm0)$hat) |
| + )) |
| [1] TRUE |
| > |
| > all.equal(rstandard(roller.lm9), |
| + rstandard(roller.lm0),tolerance = 1e-14) |
| [1] TRUE |
| > all.equal(rstudent(roller.lm9), |
| + rstudent(roller.lm0),tolerance = 1e-14) |
| [1] TRUE |
| > all.equal(rstudent(roller.lm), |
| + rstudent(roller.glm)) |
| [1] TRUE |
| > all.equal(cooks.distance(roller.lm), |
| + cooks.distance(roller.glm)) |
| [1] TRUE |
| > |
| > |
| > all.equal(summary(roller.lm0)$coefficients, |
| + summary(roller.lm9)$coefficients, tolerance = 1e-14) |
| [1] TRUE |
| > all.equal(print(anova(roller.lm0), signif.st=FALSE), |
| + anova(roller.lm9), tolerance = 1e-14) |
| Analysis of Variance Table |
| |
| Response: weight |
| Df Sum Sq Mean Sq F value Pr(>F) |
| depression 1 158.41 158.408 5.4302 0.05259 |
| Residuals 7 204.20 29.172 |
| [1] TRUE |
| > |
| > |
| > ### more regression tests for lm(), glm(), etc : |
| > |
| > ## moved from ?influence.measures: |
| > lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings) |
| > (IM <- influence.measures(lm.SR)) |
| Influence measures of |
| lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings) : |
| |
| dfb.1_ dfb.pp15 dfb.pp75 dfb.dpi dfb.ddpi dffit cov.r |
| Australia 0.01232 -0.01044 -0.02653 0.04534 -0.000159 0.0627 1.193 |
| Austria -0.01005 0.00594 0.04084 -0.03672 -0.008182 0.0632 1.268 |
| Belgium -0.06416 0.05150 0.12070 -0.03472 -0.007265 0.1878 1.176 |
| Bolivia 0.00578 -0.01270 -0.02253 0.03185 0.040642 -0.0597 1.224 |
| Brazil 0.08973 -0.06163 -0.17907 0.11997 0.068457 0.2646 1.082 |
| Canada 0.00541 -0.00675 0.01021 -0.03531 -0.002649 -0.0390 1.328 |
| Chile -0.19941 0.13265 0.21979 -0.01998 0.120007 -0.4554 0.655 |
| China 0.02112 -0.00573 -0.08311 0.05180 0.110627 0.2008 1.150 |
| Colombia 0.03910 -0.05226 -0.02464 0.00168 0.009084 -0.0960 1.167 |
| Costa Rica -0.23367 0.28428 0.14243 0.05638 -0.032824 0.4049 0.968 |
| Denmark -0.04051 0.02093 0.04653 0.15220 0.048854 0.3845 0.934 |
| Ecuador 0.07176 -0.09524 -0.06067 0.01950 0.047786 -0.1695 1.139 |
| Finland -0.11350 0.11133 0.11695 -0.04364 -0.017132 -0.1464 1.203 |
| France -0.16600 0.14705 0.21900 -0.02942 0.023952 0.2765 1.226 |
| Germany -0.00802 0.00822 0.00835 -0.00697 -0.000293 -0.0152 1.226 |
| Greece -0.14820 0.16394 0.02861 0.15713 -0.059599 -0.2811 1.140 |
| Guatamala 0.01552 -0.05485 0.00614 0.00585 0.097217 -0.2305 1.085 |
| Honduras -0.00226 0.00984 -0.01020 0.00812 -0.001887 0.0482 1.186 |
| Iceland 0.24789 -0.27355 -0.23265 -0.12555 0.184698 -0.4768 0.866 |
| India 0.02105 -0.01577 -0.01439 -0.01374 -0.018958 0.0381 1.202 |
| Ireland -0.31001 0.29624 0.48156 -0.25733 -0.093317 0.5216 1.268 |
| Italy 0.06619 -0.07097 0.00307 -0.06999 -0.028648 0.1388 1.162 |
| Japan 0.63987 -0.65614 -0.67390 0.14610 0.388603 0.8597 1.085 |
| Korea -0.16897 0.13509 0.21895 0.00511 -0.169492 -0.4303 0.870 |
| Luxembourg -0.06827 0.06888 0.04380 -0.02797 0.049134 -0.1401 1.196 |
| Malta 0.03652 -0.04876 0.00791 -0.08659 0.153014 0.2386 1.128 |
| Norway 0.00222 -0.00035 -0.00611 -0.01594 -0.001462 -0.0522 1.168 |
| Netherlands 0.01395 -0.01674 -0.01186 0.00433 0.022591 0.0366 1.229 |
| New Zealand -0.06002 0.06510 0.09412 -0.02638 -0.064740 0.1469 1.134 |
| Nicaragua -0.01209 0.01790 0.00972 -0.00474 -0.010467 0.0397 1.174 |
| Panama 0.02828 -0.05334 0.01446 -0.03467 -0.007889 -0.1775 1.067 |
| Paraguay -0.23227 0.16416 0.15826 0.14361 0.270478 -0.4655 0.873 |
| Peru -0.07182 0.14669 0.09148 -0.08585 -0.287184 0.4811 0.831 |
| Philippines -0.15707 0.22681 0.15743 -0.11140 -0.170674 0.4884 0.818 |
| Portugal -0.02140 0.02551 -0.00380 0.03991 -0.028011 -0.0690 1.233 |
| South Africa 0.02218 -0.02030 -0.00672 -0.02049 -0.016326 0.0343 1.195 |
| South Rhodesia 0.14390 -0.13472 -0.09245 -0.06956 -0.057920 0.1607 1.313 |
| Spain -0.03035 0.03131 0.00394 0.03512 0.005340 -0.0526 1.208 |
| Sweden 0.10098 -0.08162 -0.06166 -0.25528 -0.013316 -0.4526 1.086 |
| Switzerland 0.04323 -0.04649 -0.04364 0.09093 -0.018828 0.1903 1.147 |
| Turkey -0.01092 -0.01198 0.02645 0.00161 0.025138 -0.1445 1.100 |
| Tunisia 0.07377 -0.10500 -0.07727 0.04439 0.103058 -0.2177 1.131 |
| United Kingdom 0.04671 -0.03584 -0.17129 0.12554 0.100314 -0.2722 1.189 |
| United States 0.06910 -0.07289 0.03745 -0.23312 -0.032729 -0.2510 1.655 |
| Venezuela -0.05083 0.10080 -0.03366 0.11366 -0.124486 0.3071 1.095 |
| Zambia 0.16361 -0.07917 -0.33899 0.09406 0.228232 0.7482 0.512 |
| Jamaica 0.10958 -0.10022 -0.05722 -0.00703 -0.295461 -0.3456 1.200 |
| Uruguay -0.13403 0.12880 0.02953 0.13132 0.099591 -0.2051 1.187 |
| Libya 0.55074 -0.48324 -0.37974 -0.01937 -1.024477 -1.1601 2.091 |
| Malaysia 0.03684 -0.06113 0.03235 -0.04956 -0.072294 -0.2126 1.113 |
| cook.d hat inf |
| Australia 8.04e-04 0.0677 |
| Austria 8.18e-04 0.1204 |
| Belgium 7.15e-03 0.0875 |
| Bolivia 7.28e-04 0.0895 |
| Brazil 1.40e-02 0.0696 |
| Canada 3.11e-04 0.1584 |
| Chile 3.78e-02 0.0373 * |
| China 8.16e-03 0.0780 |
| Colombia 1.88e-03 0.0573 |
| Costa Rica 3.21e-02 0.0755 |
| Denmark 2.88e-02 0.0627 |
| Ecuador 5.82e-03 0.0637 |
| Finland 4.36e-03 0.0920 |
| France 1.55e-02 0.1362 |
| Germany 4.74e-05 0.0874 |
| Greece 1.59e-02 0.0966 |
| Guatamala 1.07e-02 0.0605 |
| Honduras 4.74e-04 0.0601 |
| Iceland 4.35e-02 0.0705 |
| India 2.97e-04 0.0715 |
| Ireland 5.44e-02 0.2122 |
| Italy 3.92e-03 0.0665 |
| Japan 1.43e-01 0.2233 |
| Korea 3.56e-02 0.0608 |
| Luxembourg 3.99e-03 0.0863 |
| Malta 1.15e-02 0.0794 |
| Norway 5.56e-04 0.0479 |
| Netherlands 2.74e-04 0.0906 |
| New Zealand 4.38e-03 0.0542 |
| Nicaragua 3.23e-04 0.0504 |
| Panama 6.33e-03 0.0390 |
| Paraguay 4.16e-02 0.0694 |
| Peru 4.40e-02 0.0650 |
| Philippines 4.52e-02 0.0643 |
| Portugal 9.73e-04 0.0971 |
| South Africa 2.41e-04 0.0651 |
| South Rhodesia 5.27e-03 0.1608 |
| Spain 5.66e-04 0.0773 |
| Sweden 4.06e-02 0.1240 |
| Switzerland 7.33e-03 0.0736 |
| Turkey 4.22e-03 0.0396 |
| Tunisia 9.56e-03 0.0746 |
| United Kingdom 1.50e-02 0.1165 |
| United States 1.28e-02 0.3337 * |
| Venezuela 1.89e-02 0.0863 |
| Zambia 9.66e-02 0.0643 * |
| Jamaica 2.40e-02 0.1408 |
| Uruguay 8.53e-03 0.0979 |
| Libya 2.68e-01 0.5315 * |
| Malaysia 9.11e-03 0.0652 |
| > summary(IM) |
| Potentially influential observations of |
| lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings) : |
| |
| dfb.1_ dfb.pp15 dfb.pp75 dfb.dpi dfb.ddpi dffit cov.r cook.d |
| Chile -0.20 0.13 0.22 -0.02 0.12 -0.46 0.65_* 0.04 |
| United States 0.07 -0.07 0.04 -0.23 -0.03 -0.25 1.66_* 0.01 |
| Zambia 0.16 -0.08 -0.34 0.09 0.23 0.75 0.51_* 0.10 |
| Libya 0.55 -0.48 -0.38 -0.02 -1.02_* -1.16_* 2.09_* 0.27 |
| hat |
| Chile 0.04 |
| United States 0.33_* |
| Zambia 0.06 |
| Libya 0.53_* |
| > ## colnames will differ in the next line |
| > all.equal(dfbetas(lm.SR), IM$infmat[, 1:5], check.attributes = FALSE, |
| + tolerance = 1e-12) |
| [1] TRUE |
| > signif(dfbeta(lm.SR), 3) |
| (Intercept) pop15 pop75 dpi ddpi |
| Australia 0.0916 -1.53e-03 -0.02910 4.27e-05 -3.16e-05 |
| Austria -0.0747 8.69e-04 0.04470 -3.46e-05 -1.62e-03 |
| Belgium -0.4750 7.50e-03 0.13200 -3.26e-05 -1.44e-03 |
| Bolivia 0.0429 -1.86e-03 -0.02470 3.00e-05 8.06e-03 |
| Brazil 0.6600 -8.92e-03 -0.19400 1.12e-04 1.34e-02 |
| Canada 0.0402 -9.87e-04 0.01120 -3.32e-05 -5.25e-04 |
| Chile -1.4000 1.83e-02 0.22700 -1.78e-05 2.25e-02 |
| China 0.1560 -8.33e-04 -0.09060 4.85e-05 2.18e-02 |
| Colombia 0.2900 -7.63e-03 -0.02700 1.58e-06 1.80e-03 |
| Costa Rica -1.7000 4.07e-02 0.15300 5.19e-05 -6.37e-03 |
| Denmark -0.2940 2.99e-03 0.04980 1.40e-04 9.46e-03 |
| Ecuador 0.5310 -1.39e-02 -0.06620 1.83e-05 9.44e-03 |
| Finland -0.8420 1.62e-02 0.12800 -4.10e-05 -3.39e-03 |
| France -1.2300 2.14e-02 0.23900 -2.76e-05 4.73e-03 |
| Germany -0.0597 1.20e-03 0.00915 -6.56e-06 -5.82e-05 |
| Greece -1.0900 2.38e-02 0.03110 1.47e-04 -1.17e-02 |
| Guatamala 0.1140 -7.95e-03 0.00667 5.46e-06 1.91e-02 |
| Honduras -0.0168 1.44e-03 -0.01120 7.64e-06 -3.74e-04 |
| Iceland 1.7800 -3.87e-02 -0.24700 -1.14e-04 3.55e-02 |
| India 0.1560 -2.31e-03 -0.01580 -1.29e-05 -3.76e-03 |
| Ireland -2.2800 4.28e-02 0.52200 -2.40e-04 -1.83e-02 |
| Italy 0.4910 -1.04e-02 0.00335 -6.57e-05 -5.67e-03 |
| Japan 4.6300 -9.33e-02 -0.71800 1.34e-04 7.49e-02 |
| Korea -1.2200 1.91e-02 0.23300 4.66e-06 -3.26e-02 |
| Luxembourg -0.5070 1.01e-02 0.04790 -2.63e-05 9.73e-03 |
| Malta 0.2700 -7.08e-03 0.00861 -8.09e-05 3.01e-02 |
| Norway 0.0165 -5.12e-05 -0.00670 -1.50e-05 -2.90e-04 |
| Netherlands 0.1040 -2.45e-03 -0.01300 4.07e-06 4.48e-03 |
| New Zealand -0.4440 9.48e-03 0.10300 -2.47e-05 -1.28e-02 |
| Nicaragua -0.0899 2.62e-03 0.01060 -4.46e-06 -2.08e-03 |
| Panama 0.2080 -7.73e-03 0.01570 -3.24e-05 -1.55e-03 |
| Paraguay -1.6700 2.33e-02 0.16800 1.31e-04 5.20e-02 |
| Peru -0.5150 2.07e-02 0.09670 -7.79e-05 -5.49e-02 |
| Philippines -1.1200 3.19e-02 0.16600 -1.01e-04 -3.26e-02 |
| Portugal -0.1590 3.73e-03 -0.00416 3.76e-05 -5.55e-03 |
| South Africa 0.1650 -2.97e-03 -0.00737 -1.93e-05 -3.24e-03 |
| South Rhodesia 1.0700 -1.97e-02 -0.10100 -6.54e-05 -1.15e-02 |
| Spain -0.2260 4.58e-03 0.00432 3.31e-05 1.06e-03 |
| Sweden 0.7390 -1.17e-02 -0.06650 -2.37e-04 -2.60e-03 |
| Switzerland 0.3200 -6.77e-03 -0.04760 8.52e-05 -3.72e-03 |
| Turkey -0.0807 -1.74e-03 0.02880 1.51e-06 4.96e-03 |
| Tunisia 0.5450 -1.53e-02 -0.08410 4.15e-05 2.03e-02 |
| United Kingdom 0.3450 -5.21e-03 -0.18700 1.17e-04 1.98e-02 |
| United States 0.5130 -1.06e-02 0.04100 -2.19e-04 -6.48e-03 |
| Venezuela -0.3740 1.46e-02 -0.03650 1.06e-04 -2.44e-02 |
| Zambia 1.1200 -1.06e-02 -0.34100 8.14e-05 4.16e-02 |
| Jamaica 0.8080 -1.45e-02 -0.06220 -6.57e-06 -5.81e-02 |
| Uruguay -0.9920 1.88e-02 0.03220 1.23e-04 1.97e-02 |
| Libya 4.0400 -6.98e-02 -0.41100 -1.80e-05 -2.01e-01 |
| Malaysia 0.2720 -8.88e-03 0.03520 -4.63e-05 -1.42e-02 |
| > covratio (lm.SR) |
| Australia Austria Belgium Bolivia Brazil |
| 1.1928303 1.2678392 1.1761879 1.2238199 1.0823332 |
| Canada Chile China Colombia Costa Rica |
| 1.3283009 0.6547098 1.1498637 1.1666845 0.9681384 |
| Denmark Ecuador Finland France Germany |
| 0.9344047 1.1393880 1.2031561 1.2262654 1.2256855 |
| Greece Guatamala Honduras Iceland India |
| 1.1396174 1.0852720 1.1855450 0.8658808 1.2024438 |
| Ireland Italy Japan Korea Luxembourg |
| 1.2680432 1.1624611 1.0845999 0.8695843 1.1961844 |
| Malta Norway Netherlands New Zealand Nicaragua |
| 1.1282611 1.1680616 1.2285315 1.1336998 1.1742677 |
| Panama Paraguay Peru Philippines Portugal |
| 1.0667255 0.8732040 0.8312741 0.8177726 1.2331038 |
| South Africa South Rhodesia Spain Sweden Switzerland |
| 1.1945449 1.3130954 1.2081541 1.0864869 1.1471125 |
| Turkey Tunisia United Kingdom United States Venezuela |
| 1.1003557 1.1314365 1.1886236 1.6554816 1.0945955 |
| Zambia Jamaica Uruguay Libya Malaysia |
| 0.5116454 1.1995171 1.1872025 2.0905736 1.1126445 |
| > |
| > ## Multivariate lm ("mlm") --- Example from ?SSD |
| > reacttime <- matrix(c( |
| + 420, 420, 480, 480, 600, 780, |
| + 420, 480, 480, 360, 480, 600, |
| + 480, 480, 540, 660, 780, 780, |
| + 420, 540, 540, 480, 780, 900, |
| + 540, 660, 540, 480, 660, 720, |
| + 360, 420, 360, 360, 480, 540, |
| + 480, 480, 600, 540, 720, 840, |
| + 480, 600, 660, 540, 720, 900, |
| + 540, 600, 540, 480, 720, 780, |
| + 480, 420, 540, 540, 660, 780), |
| + ncol = 6, byrow = TRUE, |
| + dimnames = list(subj = 1:10, |
| + cond = c("deg0NA", "deg4NA", "deg8NA", |
| + "deg0NP", "deg4NP", "deg8NP"))) |
| > mlmfit <- lm(reacttime ~ 1) |
| > ImMLM <- influence.measures(mlmfit)## fails in R <= 3.5.1 |
| > ## and the print() and summary() methods had failed additionally: |
| > oo <- capture.output(ImMLM) # now ok |
| > summary(ImMLM) # "ok" |
| Potentially influential observations of |
| lm(formula = reacttime ~ 1) : |
| |
| , , dfb.1_ |
| |
| deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP |
| 1 -0.25 -0.37 -0.21 -0.04 -0.18 0.05 |
| 2 -0.25 -0.12 -0.21 -0.58 -0.67 -0.53 |
| 3 0.11 -0.12 0.05 0.84 0.39 0.05 |
| 4 -0.25 0.12 0.05 -0.04 0.39 0.43 |
| 5 0.52 0.73 0.05 -0.04 0.00 -0.12 |
| 6 -0.76 -0.37 -1.06_* -0.58 -0.67 -0.85 |
| 7 0.11 -0.12 0.32 0.18 0.18 0.23 |
| 8 0.11 0.37 0.68 0.18 0.18 0.43 |
| 9 0.52 0.37 0.05 -0.04 0.18 0.05 |
| 10 0.11 -0.37 0.05 0.18 0.00 0.05 |
| |
| , , dffit |
| |
| deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP |
| 1 -0.25 -0.37 -0.21 -0.04 -0.18 0.05 |
| 2 -0.25 -0.12 -0.21 -0.58 -0.67 -0.53 |
| 3 0.11 -0.12 0.05 0.84 0.39 0.05 |
| 4 -0.25 0.12 0.05 -0.04 0.39 0.43 |
| 5 0.52 0.73 0.05 -0.04 0.00 -0.12 |
| 6 -0.76 -0.37 -1.06_* -0.58 -0.67 -0.85 |
| 7 0.11 -0.12 0.32 0.18 0.18 0.23 |
| 8 0.11 0.37 0.68 0.18 0.18 0.43 |
| 9 0.52 0.37 0.05 -0.04 0.18 0.05 |
| 10 0.11 -0.37 0.05 0.18 0.00 0.05 |
| |
| , , cov.r |
| |
| deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP |
| 1 0.08_* 0.16_* 0.15_* 0.19_* 0.29_* 0.34_* |
| 2 0.08_* 0.18_* 0.15_* 0.14_* 0.20_* 0.26_* |
| 3 0.08_* 0.18_* 0.15_* 0.11_* 0.25_* 0.34_* |
| 4 0.08_* 0.18_* 0.15_* 0.19_* 0.25_* 0.28_* |
| 5 0.06_* 0.11_* 0.15_* 0.19_* 0.30_* 0.33_* |
| 6 0.05_* 0.16_* 0.07_* 0.14_* 0.20_* 0.19_* |
| 7 0.08_* 0.18_* 0.14_* 0.19_* 0.29_* 0.32_* |
| 8 0.08_* 0.16_* 0.10_* 0.19_* 0.29_* 0.28_* |
| 9 0.06_* 0.16_* 0.15_* 0.19_* 0.29_* 0.34_* |
| 10 0.08_* 0.16_* 0.15_* 0.19_* 0.30_* 0.34_* |
| |
| , , cook.d |
| |
| deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP |
| 1 0.00 0.02 0.01 0.00 0.01 0.00 |
| 2 0.00 0.00 0.01 0.04 0.08 0.06 |
| 3 0.00 0.00 0.00 0.07 0.04 0.00 |
| 4 0.00 0.00 0.00 0.00 0.04 0.05 |
| 5 0.01 0.06 0.00 0.00 0.00 0.00 |
| 6 0.03 0.02 0.07 0.04 0.08 0.12 |
| 7 0.00 0.00 0.01 0.01 0.01 0.01 |
| 8 0.00 0.02 0.04 0.01 0.01 0.05 |
| 9 0.01 0.02 0.00 0.00 0.01 0.00 |
| 10 0.00 0.02 0.00 0.01 0.00 0.00 |
| |
| , , hat |
| |
| deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP |
| 1 0.10 0.10 0.10 0.10 0.10 0.10 |
| 2 0.10 0.10 0.10 0.10 0.10 0.10 |
| 3 0.10 0.10 0.10 0.10 0.10 0.10 |
| 4 0.10 0.10 0.10 0.10 0.10 0.10 |
| 5 0.10 0.10 0.10 0.10 0.10 0.10 |
| 6 0.10 0.10 0.10 0.10 0.10 0.10 |
| 7 0.10 0.10 0.10 0.10 0.10 0.10 |
| 8 0.10 0.10 0.10 0.10 0.10 0.10 |
| 9 0.10 0.10 0.10 0.10 0.10 0.10 |
| 10 0.10 0.10 0.10 0.10 0.10 0.10 |
| |
| > |
| > |
| > |
| > ## predict.lm(.) |
| > |
| > all.equal(predict(roller.lm, se.fit=TRUE)$se.fit, |
| + predict(roller.lm, newdata=roller, se.fit=TRUE)$se.fit, tolerance = 1e-14) |
| [1] TRUE |
| > |
