应用回归分析,R语言,多元线性回归总结(中)

R 复制代码
r<-matrix(nrow =10,ncol = 10)
 for(i in 1:10){
       for(j in 1:10)
       r[i,j]<-covBeta[i,j]/(sqrt(covBeta[i,i])*sqrt(covBeta[j,j]))}
r
复制代码
             [,1]        [,2]        [,3]        [,4]        [,5]        [,6]       [,7]
 [1,]  1.00000000 -0.06053935  0.00914841 -0.28334204 -0.03304466  0.04233039 -0.2883136
 [2,] -0.06053935  1.00000000  0.76117459  0.63132117  0.10655347  0.72992413  0.1190900
 [3,]  0.00914841  0.76117459  1.00000000  0.69220144 -0.26424621  0.59208828  0.0639940
 [4,] -0.28334204  0.63132117  0.69220144  1.00000000 -0.22886901  0.47795015  0.3342855
 [5,] -0.03304466  0.10655347 -0.26424621 -0.22886901  1.00000000 -0.02366330  0.1391050
 [6,]  0.04233039  0.72992413  0.59208828  0.47795015 -0.02366330  1.00000000  0.0573803
 [7,] -0.28831363  0.11908997  0.06399400  0.33428549  0.13910500  0.05738030  1.0000000
 [8,]  0.38642324 -0.02895360 -0.13894158 -0.32861926 -0.25030979  0.03728372 -0.5001870
 [9,] -0.94875453 -0.08563015 -0.15591018  0.09365267 -0.03454892 -0.09369497  0.1023999
[10,] -0.46881141  0.08848222 -0.06488233  0.22536209  0.09624905  0.09598542  0.5044800
             [,8]        [,9]       [,10]
 [1,]  0.38642324 -0.94875453 -0.46881141
 [2,] -0.02895360 -0.08563015  0.08848222
 [3,] -0.13894158 -0.15591018 -0.06488233
 [4,] -0.32861926  0.09365267  0.22536209
 [5,] -0.25030979 -0.03454892  0.09624905
 [6,]  0.03728372 -0.09369497  0.09598542
 [7,] -0.50018700  0.10239990  0.50448003
 [8,]  1.00000000 -0.19482468 -0.31455212
 [9,] -0.19482468  1.00000000  0.28444927
[10,] -0.31455212  0.28444927  1.00000000
R 复制代码
install.packages("car")
library(carData)
Anova(lm1,type = "III")
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Anova Table (Type III tests)

Response: y
              Sum Sq Df  F value    Pr(>F)    
(Intercept)      998  1   0.0066 0.9360967    
x1          23314547  1 153.7558 3.969e-11 ***
x2           4561056  1  30.0795 1.927e-05 ***
x3           2662593  1  17.5594 0.0004121 ***
x4                21  1   0.0001 0.9907203    
x5           9377500  1  61.8432 1.083e-07 ***
x6             89586  1   0.5908 0.4506651    
x7             17700  1   0.1167 0.7360055    
x8             54295  1   0.3581 0.5559828    
x9             17149  1   0.1131 0.7399858    
Residuals    3184305 21                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R 复制代码
r<-cor(data3_1)
r
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           x1         x2         x3          x4         x5          x6         x7          x8
x1  1.0000000 0.22714089  0.6117634  0.21301742  0.7872537  0.69676095  0.6970034 -0.16339935
x2  0.2271409 1.00000000  0.3053681  0.64622334  0.4704869  0.46044177  0.6146573  0.14367061
x3  0.6117634 0.30536809  1.0000000  0.58409947  0.7364894  0.53927001  0.7768628 -0.17839396
x4  0.2130174 0.64622334  0.5840995  1.00000000  0.4881049  0.38109255  0.6513102  0.07004622
x5  0.7872537 0.47048687  0.7364894  0.48810487  1.0000000  0.74689394  0.8141689 -0.10432612
x6  0.6967609 0.46044177  0.5392700  0.38109255  0.7468939  1.00000000  0.7801488 -0.01790576
x7  0.6970034 0.61465733  0.7768628  0.65131022  0.8141689  0.78014879  1.0000000 -0.01989850
x8 -0.1633994 0.14367061 -0.1783940  0.07004622 -0.1043261 -0.01790576 -0.0198985  1.00000000
x9 -0.3755017 0.01334004 -0.3247017 -0.10969051 -0.3743180 -0.49913442 -0.2623661 -0.13009092
y   0.9022762 0.51172104  0.7811370  0.49423568  0.9414255  0.78487674  0.8733947 -0.13026967
            x9          y
x1 -0.37550174  0.9022762
x2  0.01334004  0.5117210
x3 -0.32470168  0.7811370
x4 -0.10969051  0.4942357
x5 -0.37431801  0.9414255
x6 -0.49913442  0.7848767
x7 -0.26236608  0.8733947
x8 -0.13009092 -0.1302697
x9  1.00000000 -0.3614779
y  -0.36147795  1.0000000
R 复制代码
install.packages("corpcor")
library(corpcor)
 pcor<-cor2pcor(r)
 pcor
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             [,1]        [,2]        [,3]         [,4]        [,5]       [,6]        [,7]
 [1,]  1.00000000 -0.76117459 -0.63132117 -0.106553471 -0.72992413 -0.1190900  0.02895360
 [2,] -0.76117459  1.00000000 -0.69220144  0.264246208 -0.59208828 -0.0639940  0.13894158
 [3,] -0.63132117 -0.69220144  1.00000000  0.228869013 -0.47795015 -0.3342855  0.32861926
 [4,] -0.10655347  0.26424621  0.22886901  1.000000000  0.02366330 -0.1391050  0.25030979
 [5,] -0.72992413 -0.59208828 -0.47795015  0.023663295  1.00000000 -0.0573803 -0.03728372
 [6,] -0.11908997 -0.06399400 -0.33428549 -0.139105005 -0.05738030  1.0000000  0.50018700
 [7,]  0.02895360  0.13894158  0.32861926  0.250309793 -0.03728372  0.5001870  1.00000000
 [8,]  0.08563015  0.15591018 -0.09365267  0.034548922  0.09369497 -0.1023999  0.19482468
 [9,] -0.08848222  0.06488233 -0.22536209 -0.096249054 -0.09598542 -0.5044800  0.31455212
[10,]  0.93799379  0.76738247  0.67482266 -0.002568401  0.86400751  0.1654203  0.07434895
             [,8]        [,9]        [,10]
 [1,]  0.08563015 -0.08848222  0.937993789
 [2,]  0.15591018  0.06488233  0.767382474
 [3,] -0.09365267 -0.22536209  0.674822662
 [4,]  0.03454892 -0.09624905 -0.002568401
 [5,]  0.09369497 -0.09598542  0.864007510
 [6,] -0.10239990 -0.50448003  0.165420303
 [7,]  0.19482468  0.31455212  0.074348952
 [8,]  1.00000000 -0.28444927 -0.129479136
 [9,] -0.28444927  1.00000000  0.073188710
[10,] -0.12947914  0.07318871  1.000000000
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