虽然不知道这么做有什么意义不过
df = mtcars
# colnames(df)
do.call(rbind, lapply(2:ncol(df), function(i) {
m = lm(df[, 1] ~ df[, i])
s = summary(m)
df = data.frame(
intercept = m$coefficients[1],
slope = m$coefficients[2]
)
df = cbind(df, t(m$residuals))
return(df)
}))
#> intercept slope 1 2 3
#> (Intercept) 37.884576 -2.87579014 0.3701643 0.3701643 -3.5814159
#> (Intercept)1 29.599855 -0.04121512 -2.0054356 -2.0054356 -2.3486218
#> (Intercept)2 30.098861 -0.06822828 -1.5937500 -1.5937500 -0.9536307
#> (Intercept)3 -7.524618 7.67823260 -1.4204887 -1.4204887 0.7634229
#> (Intercept)4 37.285126 -5.34447157 -2.2826106 -0.9197704 -2.0859521
#> (Intercept)5 -5.114038 1.41212484 2.8704634 2.0796735 1.6343950
#> (Intercept)6 16.616667 7.94047619 4.3833333 4.3833333 -1.7571429
#> (Intercept)7 17.147368 7.24493927 -3.3923077 -3.3923077 -1.5923077
#> (Intercept)8 5.623333 3.92333333 -0.3166667 -0.3166667 1.4833333
#> (Intercept)9 25.872334 -2.05571870 3.3505410 3.3505410 -1.0166151
#> 4 5 6 7 8
#> (Intercept) 0.7701643 3.8217446 -2.5298357 -0.5782554 -1.9814159
#> (Intercept)1 2.4336462 3.9375884 -2.2264528 -0.4624116 0.8464033
#> (Intercept)2 -1.1937500 0.5410881 -4.8348913 0.9170676 -1.4687073
#> (Intercept)3 5.2756620 2.0381857 4.4326965 -2.8225082 3.5919401
#> (Intercept)4 1.2973499 -0.2001440 -0.6932545 -3.9053627 4.1637381
#> (Intercept)5 -0.9376686 -0.2203265 -5.3391260 -2.9540192 1.2715414
#> (Intercept)6 -3.1571429 2.0833333 -6.4571429 -2.3166667 -0.1571429
#> (Intercept)7 4.2526316 1.5526316 0.9526316 -2.8473684 7.2526316
#> (Intercept)8 4.0066667 1.3066667 0.7066667 -3.0933333 3.0833333
#> (Intercept)9 -2.4166151 -3.0608964 -5.7166151 -3.3494590 2.6391036
#> 9 10 11 12 13
#> (Intercept) -3.5814159 -1.429836 -2.8298357 1.5217446 2.42174463
#> (Intercept)1 -0.9967659 -3.492201 -4.8922007 -1.8327247 -0.93272467
#> (Intercept)2 -0.8171741 -2.506782 -3.9067823 -1.4177705 -0.51777049
#> (Intercept)3 0.2259466 -3.374053 -4.7740534 0.3524444 1.25244435
#> (Intercept)4 2.3499593 0.299856 -1.1001440 0.8668731 -0.05024720
#> (Intercept)5 -4.4236206 -1.527846 -3.7751212 -3.0569340 -2.43935895
#> (Intercept)6 -1.7571429 -5.357143 -6.7571429 -0.2166667 0.68333333
#> (Intercept)7 5.6526316 2.052632 0.6526316 -0.7473684 0.15263158
#> (Intercept)8 1.4833333 -2.116667 -3.5166667 -0.9933333 -0.09333333
#> (Intercept)9 1.0391036 1.550541 0.1505410 -3.3051777 -2.40517774
#> 14 15 16 17 18
#> (Intercept) 0.3217446 -4.4782554 -4.4782554 -0.1782554 6.018584
#> (Intercept)1 -3.0327247 0.2536819 -0.2408996 3.2347980 6.043775
#> (Intercept)2 -2.6177705 -5.7120635 -5.0297808 0.2936434 6.804206
#> (Intercept)3 -0.8475556 -4.5726031 -5.1100794 -2.5760729 8.597429
#> (Intercept)4 -1.8830236 1.1733496 2.1032876 5.9810744 6.872711
#> (Intercept)5 -5.1042089 -9.8759664 -9.6500264 -4.7851765 10.019968
#> (Intercept)6 -1.4166667 -6.2166667 -6.2166667 -1.9166667 7.842857
#> (Intercept)7 -1.9473684 -6.7473684 -6.7473684 -2.4473684 8.007692
#> (Intercept)8 -2.1933333 -6.9933333 -6.9933333 -2.6933333 11.083333
#> (Intercept)9 -4.5051777 -7.2494590 -7.2494590 -2.9494590 8.583385
#> 19 20 21 22 23
#> (Intercept) 4.01858407 7.518584 -4.8814159 0.6217446 0.3217446
#> (Intercept)1 3.92012983 7.230540 -3.1499188 -0.9934466 -1.8704583
#> (Intercept)2 3.84900992 8.235978 -1.9807176 -4.3646188 -4.6646188
#> (Intercept)3 0.07093171 9.022477 0.6151578 1.8326965 -1.4618143
#> (Intercept)4 1.74619542 6.421979 -2.6110037 -2.9725862 -3.7268663
#> (Intercept)5 9.36148620 10.912754 -1.6425798 -3.2085078 -4.1157215
#> (Intercept)6 5.84285714 9.342857 -3.0571429 -1.1166667 -1.4166667
#> (Intercept)7 6.00769231 9.507692 4.3526316 -1.6473684 -1.9473684
#> (Intercept)8 9.08333333 12.583333 4.1066667 -1.8933333 -2.1933333
#> (Intercept)9 8.63910355 10.083385 -2.3166151 -6.2608964 -6.5608964
#> 24 25 26 27 28
#> (Intercept) -1.57825537 4.3217446 0.9185841 -0.3814159 4.018584
#> (Intercept)1 -1.87456277 6.0861932 0.9561397 1.3583242 4.719703
#> (Intercept)2 -0.08293241 1.0410881 1.7042058 2.1099128 8.010935
#> (Intercept)3 -7.81518916 3.0756620 3.4974294 -0.4899520 8.977682
#> (Intercept)4 -3.46235533 2.4643670 0.3564263 0.1520430 1.201059
#> (Intercept)5 -3.34680556 0.2373097 5.7248788 7.5315534 11.649128
#> (Intercept)6 -3.31666667 2.5833333 2.7428571 9.3833333 5.842857
#> (Intercept)7 -3.84736842 2.0526316 2.9076923 1.6076923 6.007692
#> (Intercept)8 -4.09333333 1.8066667 5.9833333 0.7600000 5.160000
#> (Intercept)9 -4.34945904 -2.5608964 3.4833849 4.2391036 8.639104
#> 29 30 31 32
#> (Intercept) 0.9217446 -0.9298357 0.1217446 -4.98141593
#> (Intercept)1 0.6666524 -3.9236624 -2.1941036 -3.21282524
#> (Intercept)2 3.7134049 1.5410881 7.7576126 -1.26197823
#> (Intercept)3 -9.0775231 -0.5705836 -4.6563250 -2.63291755
#> (Intercept)4 -4.5431513 -2.7809399 -3.2053627 -1.02749520
#> (Intercept)5 0.4382280 2.9261032 -0.5029844 0.24851622
#> (Intercept)6 -0.8166667 3.0833333 -1.6166667 -3.15714286
#> (Intercept)7 -8.5923077 -4.6923077 -9.3923077 -2.99230769
#> (Intercept)8 -9.4400000 -5.5400000 -10.2400000 0.08333333
#> (Intercept)9 -1.8494590 6.1619784 5.5734158 -0.36089645
<sup>Created on 2019-04-16 by the reprex package (v0.2.1)</sup>