Я пытаюсь сделать код про dfbetas.но я не знаю разностного кода dfbeta и dfbetas в R. и я не знаю, почему эти два результата отличаются.
y= c(56.25, 75, 115.625, 68.75, 96.875, 168.750, 84.375, 171.875, 109.375, 103.125)
x1=c(106.329, 144.726, 136.287, 154.430, 385.232, 585.544, 489.451, 445.992, 270.886, 163.291)
x2=c(90.756, 203.361, 672.269, 183.193, 140.336, 146.218, 184.874, 537.815, 309.244, 190.756)
x3=c(94.650, 131.687, 123.457, 113.169, 117.284, 152.263, 121.399, 150.206, 185.185, 139.918)
x4=c(162.791, 255.814, 191.860, 133.721, 174.419, 273.256, 255.814, 552.326, 534.884, 360.465)
x5=c(114.337, 112.632, 153.684, 116.842, 87.368, 94.737, 95.789, 113.684, 108.421, 106.321)
x=cbind(1,x1,x2,x3,x4,x5)
dfbetas.js=function(x){
n=length(y);p=ncol(x)-1
b=solve(t(x)%*%x)%*%(t(x)%*%y)
C=solve(t(x)%*%x)
sigma.hat.mi=c()
for(i in 1:n){
SSE.mi=y[-i]-(x[-i,]%*%solve(t(x[-i,])%*%x[-i,])%*%t(x[-i,])%*%y[-i])
sigma.hat.mi[i]=sqrt(sum(SSE.mi^2)/(n-p-2))
}
dfbetas.self=matrix(NA,nrow=n,ncol=length(b))
for(j in 1:length(b)){
for(i in 1:n){
b.mi=solve(t(x[-i,])%*%x[-i,])%*%(t(x[-i,])%*%y[-i])
dfbetas.self[i,j]=(b[j]-b.mi[j])/(sigma.hat.mi[i]*sqrt(C[j,j]))
}
}
print(dfbetas.self)
}
dfbetas.js(x)
dfbeta(lm(y~x1+x2+x3+x4+x5)) #dfbeta
influence.measures(lm(y~x1+x2+x3+x4+x5)) #dfbetas
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.063429091 0.076679024 -0.170191979 -0.186020166 0.17467690 0.142290356
[2,] -0.011974248 0.033364876 -0.005307696 -0.021199012 0.01534060 0.012888825
[3,] 0.034969383 0.002144448 -0.082245088 -0.061036473 0.11308539 -0.018509577
[4,] 0.005045504 0.003298807 0.009567908 -0.008031121 0.01493122 -0.008276116
[5,] 1.219911819 -0.826663377 1.102479573 0.203811009 -0.83042581 -1.227683030
[6,] -8.945000499 10.367169645 -7.000656833 5.343176070 -2.29457385 7.026734861
[7,] 0.016874636 -1.157231530 0.361551135 0.994036945 -0.51006636 -0.321541196
[8,] 0.311744572 0.428829147 0.326751233 -1.469807152 1.50839649 -0.027441010
[9,] 0.416989589 0.515381241 0.134030842 -1.295376051 0.04478046 0.030603256
[10,] 0.170904382 -0.305169737 0.033076869 0.002570310 0.11207452 -0.141871843
(Intercept) x1 x2 x3 x4 x5
1 -14.209832 0.0091554553 -0.0283346416 -0.145449237 0.027449269 0.28589138
2 -2.691794 0.0039974772 -0.0008867021 -0.016632601 0.002418972 0.02598556
3 7.867826 0.0002571492 -0.0137516537 -0.047929972 0.017847118 -0.03734981
4 1.135377 0.0003956355 0.0016000395 -0.006307579 0.002356816 -0.01670272
5 261.324953 -0.0943808849 0.1755097168 0.152381077 -0.124780900 -2.35865178
6 -461.474371 0.2850560932 -0.2684010308 0.962094342 -0.083035555 3.25121671
7 1.932217 -0.0706227547 0.0307658836 0.397260124 -0.040967834 -0.33020470
8 58.207111 0.0426740977 0.0453390642 -0.957830052 0.197554757 -0.04595178
9 69.262180 0.0456249556 0.0165445082 -0.750961658 0.005217403 0.04558938
10 34.516659 -0.0328488309 0.0049645297 0.001811807 0.015877323 -0.25697856
Influence measures of
lm(formula = y ~ x1 + x2 + x3 + x4 + x5) :
dfb.1_ dfb.x1 dfb.x2 dfb.x3 dfb.x4 dfb.x5 dffit cov.r cook.d hat inf
1 -0.06343 0.07668 -0.17019 -0.18602 0.1747 0.14229 0.2940 1.81e+01 0.018982 0.711 *
2 -0.01197 0.03336 -0.00531 -0.02120 0.0153 0.01289 -0.0645 7.04e+00 0.000920 0.225 *
3 0.03497 0.00214 -0.08225 -0.06104 0.1131 -0.01851 -0.3249 6.90e+01 0.023392 0.920 *
4 0.00505 0.00330 0.00957 -0.00803 0.0149 -0.00828 -0.0544 7.52e+00 0.000656 0.265 *
5 1.21991 -0.82666 1.10248 0.20381 -0.8304 -1.22768 1.4539 2.33e+01 0.424495 0.869 *
6 -8.94500 10.36717 -7.00066 5.34318 -2.2946 7.02673 15.4185 6.27e-07 2.769152 0.814 *
7 0.01687 -1.15723 0.36155 0.99404 -0.5101 -0.32154 -2.2132 2.61e-03 0.281056 0.362 *
8 0.31174 0.42883 0.32675 -1.46981 1.5084 -0.02744 2.6441 3.59e+00 1.066710 0.836 *
9 0.41699 0.51538 0.13403 -1.29538 0.0448 0.03060 -2.3676 4.66e-01 0.676846 0.690 *
10 0.17090 -0.30517 0.03308 0.00257 0.1121 -0.14187 0.5719 2.18e+00 0.058389 0.308