Heimaverkefni 4 Hoft
## Warning: package 'ggplot2' was built under R version 4.4.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniona-liður
Teikna punktarit
dat_long <- dat %>%
pivot_longer(cols = starts_with("x"),
names_to = "xvar",
values_to = "xvalue")
ggplot(dat_long, aes(x = xvalue, y = Y)) +
geom_point(color = "hotpink", size = 3) +
facet_wrap(~ xvar, scales = "free_x")
Útfrá þessum tvem plottum, myndi ég telja Y og x3 markverðast þar sem það er línulegast. Einnig hjá Y og x1 en ekki eins línulegt.
b-liður
Meta fylgnifylki
## Y x1 x2 x3 x4 x5
## Y 1.0000000 0.32306071 -0.24968780 0.69082860 -0.5574363 -0.3347743
## x1 0.3230607 1.00000000 -0.06432696 0.23521052 -0.0689409 -0.2190429
## x2 -0.2496878 -0.06432696 1.00000000 0.00307297 0.4462797 0.4699373
## x3 0.6908286 0.23521052 0.00307297 1.00000000 -0.2247722 -0.0404318
## x4 -0.5574363 -0.06894090 0.44627974 -0.22477219 1.0000000 0.4007609
## x5 -0.3347743 -0.21904291 0.46993729 -0.04043180 0.4007609 1.0000000
## x6 -0.2480903 -0.38948249 0.19276170 -0.21524481 0.3843357 0.4293733
## x6
## Y -0.2480903
## x1 -0.3894825
## x2 0.1927617
## x3 -0.2152448
## x4 0.3843357
## x5 0.4293733
## x6 1.0000000Hérna fyrir ofan má sjá fylgnifylkið. Það sýnir að það sé sterk jákvæð fylgni milli Y og x3 (r=0,69) og einnig jákvæð fylgni milli Y og x1 (r=0,32), en hún er ekki jafn sterk. Neikvæð fylgni er milli Y og x4 (r=-0,56) og minni á milli Y og x2, x5 og x6. Hjá óháðu skýribreytanna sést miðlungs jákvæð fylgni milli x2 og x4 (r=0,45) og milli x2 og x5 (r=0,47). Fylgnin sem er næst núlli (minnst að stærð) er milli x2 og x3 (r=0,003).
c-liður
Meta líkan með einni óháðri skýribreytu
##
## Call:
## lm(formula = Y ~ x1, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9899 -0.9913 -0.1316 0.5795 2.5879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7677 4.5316 0.831 0.417
## x1 1.0972 0.7796 1.407 0.177
##
## Residual standard error: 1.323 on 17 degrees of freedom
## Multiple R-squared: 0.1044, Adjusted R-squared: 0.05168
## F-statistic: 1.981 on 1 and 17 DF, p-value: 0.1773##
## Call:
## lm(formula = Y ~ x2, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.05453 -1.18306 0.05259 1.03118 2.08841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.0529 6.5175 2.616 0.0181 *
## x2 -0.3571 0.3358 -1.063 0.3026
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 17 degrees of freedom
## Multiple R-squared: 0.06234, Adjusted R-squared: 0.007188
## F-statistic: 1.13 on 1 and 17 DF, p-value: 0.3026##
## Call:
## lm(formula = Y ~ x3, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.94395 -0.72298 -0.06256 0.84675 1.64911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.9710 4.8545 -1.848 0.08208 .
## x3 0.5931 0.1505 3.940 0.00106 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 17 degrees of freedom
## Multiple R-squared: 0.4772, Adjusted R-squared: 0.4465
## F-statistic: 15.52 on 1 and 17 DF, p-value: 0.001057##
## Call:
## lm(formula = Y ~ x4, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6415 -0.9401 -0.1711 0.8832 1.9009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.60167 5.23368 4.701 0.000206 ***
## x4 -0.16344 0.05904 -2.768 0.013151 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.161 on 17 degrees of freedom
## Multiple R-squared: 0.3107, Adjusted R-squared: 0.2702
## F-statistic: 7.664 on 1 and 17 DF, p-value: 0.01315##
## Call:
## lm(formula = Y ~ x5, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0608 -0.7866 -0.0608 1.0634 2.2392
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.475 6.386 3.050 0.00724 **
## x5 -4.483 3.060 -1.465 0.16122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.317 on 17 degrees of freedom
## Multiple R-squared: 0.1121, Adjusted R-squared: 0.05984
## F-statistic: 2.146 on 1 and 17 DF, p-value: 0.1612##
## Call:
## lm(formula = Y ~ x6, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1218 -0.9779 -0.4218 0.9236 2.4721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.966 3.645 3.832 0.00134 **
## x6 -1.969 1.865 -1.056 0.30578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 17 degrees of freedom
## Multiple R-squared: 0.06155, Adjusted R-squared: 0.006346
## F-statistic: 1.115 on 1 and 17 DF, p-value: 0.3058Af þessum líkönunum er x3 langbesta skýribreytan, bæði marktæk og með besta R (r=0,48). x4 er næst best og er einnig martæk.
d-liður
Meta líkan tveggja óháða skýribreyta
##
## Call:
## lm(formula = Y ~ x1 + x2, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.95459 -1.01699 0.03796 0.73988 2.32005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.4306 8.0618 1.294 0.214
## x1 1.0470 0.7812 1.340 0.199
## x2 -0.3287 0.3289 -0.999 0.333
##
## Residual standard error: 1.323 on 16 degrees of freedom
## Multiple R-squared: 0.157, Adjusted R-squared: 0.05161
## F-statistic: 1.49 on 2 and 16 DF, p-value: 0.2551##
## Call:
## lm(formula = Y ~ x1 + x3, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.78382 -0.61681 0.02068 0.77631 1.39037
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11.2138 5.4257 -2.067 0.05533 .
## x1 0.5773 0.6149 0.939 0.36178
## x3 0.5587 0.1554 3.595 0.00243 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.014 on 16 degrees of freedom
## Multiple R-squared: 0.5045, Adjusted R-squared: 0.4426
## F-statistic: 8.147 on 2 and 16 DF, p-value: 0.003632##
## Call:
## lm(formula = Y ~ x1 + x4, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4527 -0.7209 -0.5300 0.9651 1.9104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.45623 6.57971 2.805 0.0127 *
## x1 0.97132 0.66357 1.464 0.1626
## x4 -0.15766 0.05728 -2.752 0.0142 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.123 on 16 degrees of freedom
## Multiple R-squared: 0.3921, Adjusted R-squared: 0.3162
## F-statistic: 5.161 on 2 and 16 DF, p-value: 0.01864##
## Call:
## lm(formula = Y ~ x1 + x5, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.94430 -0.99476 -0.07294 0.87318 2.22706
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.7040 8.7236 1.456 0.165
## x1 0.8909 0.7892 1.129 0.276
## x5 -3.7135 3.1115 -1.193 0.250
##
## Residual standard error: 1.307 on 16 degrees of freedom
## Multiple R-squared: 0.1776, Adjusted R-squared: 0.07478
## F-statistic: 1.727 on 2 and 16 DF, p-value: 0.2093##
## Call:
## lm(formula = Y ~ x1 + x6, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7009 -1.0257 -0.1130 0.5588 2.6099
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.1011 7.4829 0.949 0.357
## x1 0.9066 0.8638 1.049 0.310
## x6 -1.1439 2.0187 -0.567 0.579
##
## Residual standard error: 1.35 on 16 degrees of freedom
## Multiple R-squared: 0.122, Adjusted R-squared: 0.01224
## F-statistic: 1.112 on 2 and 16 DF, p-value: 0.3532##
## Call:
## lm(formula = Y ~ x2 + x3, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.79435 -0.56978 -0.07551 0.81264 1.58331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.0122 6.6277 -0.304 0.765341
## x2 -0.3601 0.2423 -1.486 0.156675
## x3 0.5937 0.1455 4.082 0.000869 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9766 on 16 degrees of freedom
## Multiple R-squared: 0.5407, Adjusted R-squared: 0.4832
## F-statistic: 9.416 on 2 and 16 DF, p-value: 0.001982##
## Call:
## lm(formula = Y ~ x2 + x4, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6410 -0.9396 -0.1690 0.8821 1.9002
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.620114 6.565670 3.750 0.00175 **
## x2 -0.001634 0.331669 -0.005 0.99613
## x4 -0.163286 0.068001 -2.401 0.02885 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.196 on 16 degrees of freedom
## Multiple R-squared: 0.3107, Adjusted R-squared: 0.2246
## F-statistic: 3.607 on 2 and 16 DF, p-value: 0.05094##
## Call:
## lm(formula = Y ~ x2 + x5, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.03599 -0.88391 0.01486 1.04449 2.12839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.2062 7.6023 2.789 0.0131 *
## x2 -0.1695 0.3793 -0.447 0.6609
## x5 -3.7370 3.5517 -1.052 0.3084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 16 degrees of freedom
## Multiple R-squared: 0.123, Adjusted R-squared: 0.0134
## F-statistic: 1.122 on 2 and 16 DF, p-value: 0.3499##
## Call:
## lm(formula = Y ~ x2 + x6, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9498 -0.9500 -0.2198 1.0097 2.2201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.1531 7.0059 2.734 0.0147 *
## x2 -0.2998 0.3449 -0.869 0.3975
## x6 -1.6483 1.9142 -0.861 0.4019
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 16 degrees of freedom
## Multiple R-squared: 0.1039, Adjusted R-squared: -0.008145
## F-statistic: 0.9273 on 2 and 16 DF, p-value: 0.4159##
## Call:
## lm(formula = Y ~ x3 + x4, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.78402 -0.51140 -0.00655 0.45487 1.51206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.65631 6.39482 0.728 0.47705
## x3 0.51133 0.13075 3.911 0.00125 **
## x4 -0.12418 0.04466 -2.781 0.01336 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8554 on 16 degrees of freedom
## Multiple R-squared: 0.6476, Adjusted R-squared: 0.6035
## F-statistic: 14.7 on 2 and 16 DF, p-value: 0.0002379##
## Call:
## lm(formula = Y ~ x3 + x5, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8769 -0.5410 0.1714 0.7238 1.3841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04954 6.56674 -0.008 0.994074
## x3 0.58239 0.14059 4.142 0.000766 ***
## x5 -4.11565 2.19309 -1.877 0.078927 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9432 on 16 degrees of freedom
## Multiple R-squared: 0.5716, Adjusted R-squared: 0.518
## F-statistic: 10.67 on 2 and 16 DF, p-value: 0.001136##
## Call:
## lm(formula = Y ~ x3 + x6, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0622 -0.6179 -0.1878 0.8726 1.5116
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.7399 6.3192 -1.067 0.30200
## x3 0.5738 0.1573 3.647 0.00217 **
## x6 -0.8272 1.4544 -0.569 0.57743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.031 on 16 degrees of freedom
## Multiple R-squared: 0.4876, Adjusted R-squared: 0.4236
## F-statistic: 7.613 on 2 and 16 DF, p-value: 0.004752##
## Call:
## lm(formula = Y ~ x4 + x5, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.63156 -0.83559 -0.08159 0.88298 1.95448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.9245 6.6235 4.065 0.0009 ***
## x4 -0.1479 0.0657 -2.250 0.0389 *
## x5 -1.7768 3.0009 -0.592 0.5621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.183 on 16 degrees of freedom
## Multiple R-squared: 0.3255, Adjusted R-squared: 0.2412
## F-statistic: 3.861 on 2 and 16 DF, p-value: 0.04283##
## Call:
## lm(formula = Y ~ x4 + x6, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6931 -0.9068 -0.1607 0.9014 1.8618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.81928 5.52822 4.490 0.000371 ***
## x4 -0.15896 0.06585 -2.414 0.028129 *
## x6 -0.31520 1.78265 -0.177 0.861870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.195 on 16 degrees of freedom
## Multiple R-squared: 0.3121, Adjusted R-squared: 0.2261
## F-statistic: 3.629 on 2 and 16 DF, p-value: 0.05015##
## Call:
## lm(formula = Y ~ x5 + x6, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9221 -0.6816 -0.2220 1.0413 2.0779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.919 6.594 3.021 0.00812 **
## x5 -3.747 3.467 -1.081 0.29572
## x6 -1.015 2.055 -0.494 0.62789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 16 degrees of freedom
## Multiple R-squared: 0.1254, Adjusted R-squared: 0.0161
## F-statistic: 1.147 on 2 and 16 DF, p-value: 0.3423Út frá þessum líkönunum er besta líkanið á milli x3 og x4. Þar sem r=0,6035, og því hæst af öllum. Einnig eru bæði p-gildin marktækt. (Sést að það er stjarna við hliðiná)
e-liður
Meta líkan með öllum óháðum skýribreytum
##
## Call:
## lm(formula = Y ~ x1 + x2 + x3 + x4 + x5 + x6, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.19036 -0.39392 0.00724 0.41801 1.34725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.13682 8.10979 0.387 0.70568
## x1 0.64443 0.58892 1.094 0.29532
## x2 -0.01042 0.26765 -0.039 0.96959
## x3 0.50465 0.14234 3.545 0.00403 **
## x4 -0.11967 0.05623 -2.128 0.05475 .
## x5 -2.46177 2.59776 -0.948 0.36200
## x6 1.50441 1.51936 0.990 0.34164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8941 on 12 degrees of freedom
## Multiple R-squared: 0.7113, Adjusted R-squared: 0.5669
## F-statistic: 4.927 on 6 and 12 DF, p-value: 0.00921Hér sést að eina gildið sem er marktækt er hjá x3. Síðan í heild er hægt að sjá að það er marktækt, p-gildið er undir 0,05.
f-liður
Hvað er besta líkanið?
Miðað við úr fyrri liðum er líkanið fyrir Y ~ x3 + x4 best. Bæði hefur það hæsta og besta R-gildið, og báðar skýribreytunar eru marktækar. Einnig er p-gildið mjög látt.
g-liður
Stepwise aðferð
m0 <- lm(Y ~ 1, data = dat)
mstep <- step(m0, scope = list(lower = m0, upper = m_full), direction = "both", trace = 0)
summary(mstep)##
## Call:
## lm(formula = Y ~ x3 + x4, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.78402 -0.51140 -0.00655 0.45487 1.51206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.65631 6.39482 0.728 0.47705
## x3 0.51133 0.13075 3.911 0.00125 **
## x4 -0.12418 0.04466 -2.781 0.01336 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8554 on 16 degrees of freedom
## Multiple R-squared: 0.6476, Adjusted R-squared: 0.6035
## F-statistic: 14.7 on 2 and 16 DF, p-value: 0.0002379Útfrá stepwise aðferðinni er besta líkanið Y ~ x3 + x4, sem staðfestir mínar niðurstöður.
h-liður
Restliðar besta líkans
Hægt er að sjá að það eru nokkrir lestliðar eins og í punkti 14 og 2. Þeir eru lengst frá rauðu línunni (0-línana) á gröfunum. Hægt er að skoða hvort að þau hafi áhrif á líkanið með Cook´s.
## 1 2 3 4 5 6
## 9.442844e-06 9.466994e-02 5.849973e-04 4.142350e-02 1.046971e-01 3.188670e-02
## 7 8 9 10 11 12
## 2.198302e-02 5.514382e-02 6.620952e-03 2.336330e-03 1.348012e-03 1.121959e-02
## 13 14 15 16 17 18
## 4.799338e-03 2.249583e-01 8.816668e-02 1.047565e-02 7.047434e-02 8.138294e-02
## 19
## 5.770174e-02## 14
## 14Cook´s segir okkur að mæling nr. 14 sé að hafa áhrif á líkanið.
i-liður
Spá fyrir um gildi Y
newx <- data.frame(x1=11, x2=18, x3=31, x4=100, x5=2, x6=2)
pred <- predict(mstep, newdata = newx, interval = "prediction", level = 0.95)
pred## fit lwr upr
## 1 8.089072 5.947112 10.23103Y = 8,089072 með 95% spábili [5,947112 ; 10,23103].
Tafla
## Warning: package 'knitr' was built under R version 4.4.3# Hjálparföll
fmt_coef <- function(model, term){
s <- summary(model)$coefficients
if (!(term %in% rownames(s))) return("")
b <- s[term, "Estimate"]
se <- s[term, "Std. Error"]
sprintf("%.3f (%.3f)", b, se)
}
row_from_model <- function(model){
terms <- c("(Intercept)", paste0("x", 1:6))
out <- setNames(rep("", 7), c("b0","b1","b2","b3","b4","b5","b6"))
for (j in 1:7){
out[j] <- fmt_coef(model, terms[j])
}
sm <- summary(model)
c(out,
sig2 = sprintf("%.3f", sm$sigma^2),
R2 = sprintf("%.3f", sm$r.squared),
Radj = sprintf("%.3f", sm$adj.r.squared)
)
}
# L1–L6 (1 breyta)
models_1 <- lapply(1:6, function(j){
lm(as.formula(paste0("Y ~ x", j)), data = dat)
})
names(models_1) <- paste0("L", 1:6)
# L7–L21 (2 breytur)
pairs2 <- combn(paste0("x", 1:6), 2, simplify = FALSE)
models_2 <- lapply(pairs2, function(v){
lm(as.formula(paste("Y ~", paste(v, collapse = " + "))), data = dat)
})
names(models_2) <- paste0("L", 7:21)
# L22 (fullt líkan)
m22 <- lm(Y ~ x1 + x2 + x3 + x4 + x5 + x6, data = dat)
# Tafla L1–L22
tab <- rbind(
do.call(rbind, lapply(models_1, row_from_model)),
do.call(rbind, lapply(models_2, row_from_model)),
L22 = row_from_model(m22)
)
tab <- data.frame(Model = rownames(tab), tab, row.names = NULL)
names(tab) <- c("Líkan","β0","β1","β2","β3","β4","β5","β6","σe^2","R^2","R^2_adj")
kable(tab, caption = "L1–L22: Stuðlar (SE), σe^2, R^2 og R^2_adj")| Líkan | β0 | β1 | β2 | β3 | β4 | β5 | β6 | σe^2 | R^2 | R^2_adj |
|---|---|---|---|---|---|---|---|---|---|---|
| L1 | 3.768 (4.532) | 1.097 (0.780) | 1.750 | 0.104 | 0.052 | |||||
| L2 | 17.053 (6.518) | -0.357 (0.336) | 1.832 | 0.062 | 0.007 | |||||
| L3 | -8.971 (4.854) | 0.593 (0.151) | 1.022 | 0.477 | 0.446 | |||||
| L4 | 24.602 (5.234) | -0.163 (0.059) | 1.347 | 0.311 | 0.270 | |||||
| L5 | 19.475 (6.386) | -4.483 (3.060) | 1.735 | 0.112 | 0.060 | |||||
| L6 | 13.966 (3.645) | -1.969 (1.865) | 1.834 | 0.062 | 0.006 | |||||
| L7 | 10.431 (8.062) | 1.047 (0.781) | -0.329 (0.329) | 1.750 | 0.157 | 0.052 | ||||
| L8 | -11.214 (5.426) | 0.577 (0.615) | 0.559 (0.155) | 1.029 | 0.505 | 0.443 | ||||
| L9 | 18.456 (6.580) | 0.971 (0.664) | -0.158 (0.057) | 1.262 | 0.392 | 0.316 | ||||
| L10 | 12.704 (8.724) | 0.891 (0.789) | -3.714 (3.112) | 1.708 | 0.178 | 0.075 | ||||
| L11 | 7.101 (7.483) | 0.907 (0.864) | -1.144 (2.019) | 1.823 | 0.122 | 0.012 | ||||
| L12 | -2.012 (6.628) | -0.360 (0.242) | 0.594 (0.145) | 0.954 | 0.541 | 0.483 | ||||
| L13 | 24.620 (6.566) | -0.002 (0.332) | -0.163 (0.068) | 1.431 | 0.311 | 0.225 | ||||
| L14 | 21.206 (7.602) | -0.170 (0.379) | -3.737 (3.552) | 1.821 | 0.123 | 0.013 | ||||
| L15 | 19.153 (7.006) | -0.300 (0.345) | -1.648 (1.914) | 1.861 | 0.104 | -0.008 | ||||
| L16 | 4.656 (6.395) | 0.511 (0.131) | -0.124 (0.045) | 0.732 | 0.648 | 0.604 | ||||
| L17 | -0.050 (6.567) | 0.582 (0.141) | -4.116 (2.193) | 0.890 | 0.572 | 0.518 | ||||
| L18 | -6.740 (6.319) | 0.574 (0.157) | -0.827 (1.454) | 1.064 | 0.488 | 0.424 | ||||
| L19 | 26.925 (6.624) | -0.148 (0.066) | -1.777 (3.001) | 1.400 | 0.326 | 0.241 | ||||
| L20 | 24.819 (5.528) | -0.159 (0.066) | -0.315 (1.783) | 1.428 | 0.312 | 0.226 | ||||
| L21 | 19.919 (6.594) | -3.747 (3.467) | -1.015 (2.055) | 1.816 | 0.125 | 0.016 | ||||
| L22 | 3.137 (8.110) | 0.644 (0.589) | -0.010 (0.268) | 0.505 (0.142) | -0.120 (0.056) | -2.462 (2.598) | 1.504 (1.519) | 0.799 | 0.711 | 0.567 |