dummy variable regression
hekai
2016年10月19日
Checking the data
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homedata<-read.csv("C:/Users/hekai.BEAN_PC/Desktop/Data.csv")
x0<-homedata[which(homedata$Level==0),2:21]
x1<-homedata[which(homedata$Level==1),2:21]
x2<-homedata[which(homedata$Level==2),2:21]
dev.new()
plot(x0)
dev.new()
plot(x1)
dev.new()
plot(x2)A scrutiny
homedata[which(homedata$Level==2),1]<-0
x<-homedata[1:9]
plot(x)
n<-7
a<-list(dim(1))
for (i in 3:9) {
for (j in 1:n-1) {
a[[j+(i-3)*(15-i)/2+1]]<-lm(x[,i]~x[,i+j]+x[,1])
j<j+1
print(colnames(x[i]))
print("~")
print(colnames(x[i+j]))
print(summary(a[[j+(i-3)*(15-i)/2+1]]))
}
n<-n-1
i<-i+1
}## [1] "TPLFA"
## [1] "~"
## [1] "TPLFA"## Warning in summary.lm(a[[j + (i - 3) * (15 - i)/2 + 1]]): essentially
## perfect fit: summary may be unreliable##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.973e-15 -9.292e-16 -5.470e-16 1.856e-16 2.934e-14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.338e-15 1.508e-15 4.867e+00 9.33e-06 ***
## x[, i + j] 1.000e+00 8.366e-17 1.195e+16 < 2e-16 ***
## x[, 1] -1.067e-15 1.066e-15 -1.001e+00 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.98e-15 on 57 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 7.384e+31 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "TPLFA"
## [1] "~"
## [1] "GPB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1478 -1.3910 0.0446 1.1165 5.1533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3883 0.6908 3.457 0.00104 **
## x[, i + j] 3.3259 0.1462 22.746 < 2e-16 ***
## x[, 1] -0.9133 0.5419 -1.685 0.09736 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.985 on 57 degrees of freedom
## Multiple R-squared: 0.904, Adjusted R-squared: 0.9006
## F-statistic: 268.4 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "TPLFA"
## [1] "~"
## [1] "GNB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0738 -1.5349 -0.9304 1.2759 7.0899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0738 0.8218 4.957 6.77e-06 ***
## x[, i + j] 1.9099 0.1112 17.179 < 2e-16 ***
## x[, 1] 0.1455 0.6798 0.214 0.831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.535 on 57 degrees of freedom
## Multiple R-squared: 0.8434, Adjusted R-squared: 0.8379
## F-statistic: 153.5 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "TPLFA"
## [1] "~"
## [1] "BPLFA"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5053 -1.3295 -0.3894 1.2411 5.8324
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.92856 0.64465 4.543 3e-05 ***
## x[, i + j] 1.26745 0.05273 24.036 <2e-16 ***
## x[, 1] -0.40868 0.47931 -0.853 0.397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.774 on 56 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.9137, Adjusted R-squared: 0.9106
## F-statistic: 296.5 on 2 and 56 DF, p-value: < 2.2e-16
##
## [1] "TPLFA"
## [1] "~"
## [1] "FPLFA"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4333 -1.5705 -0.4991 1.0656 9.6712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2294 0.8318 3.882 0.000271 ***
## x[, i + j] 3.4950 0.1955 17.878 < 2e-16 ***
## x[, 1] -0.1672 0.6606 -0.253 0.801138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.451 on 57 degrees of freedom
## Multiple R-squared: 0.8536, Adjusted R-squared: 0.8484
## F-statistic: 166.1 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "TPLFA"
## [1] "~"
## [1] "ANE"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5515 -1.6347 -0.0212 0.9376 6.2410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7847 0.7641 3.644 0.000581 ***
## x[, i + j] 8.2941 0.4138 20.044 < 2e-16 ***
## x[, 1] -0.7218 0.6044 -1.194 0.237382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.221 on 57 degrees of freedom
## Multiple R-squared: 0.8798, Adjusted R-squared: 0.8756
## F-statistic: 208.6 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "TPLFA"
## [1] "~"
## [1] "MOB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8055 -1.1447 -0.0707 0.9276 7.1804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.8055 0.6905 6.959 3.72e-09 ***
## x[, i + j] 26.5282 1.3496 19.657 < 2e-16 ***
## x[, 1] 0.8313 0.6001 1.385 0.171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.259 on 57 degrees of freedom
## Multiple R-squared: 0.8756, Adjusted R-squared: 0.8713
## F-statistic: 200.7 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "GPB"
## [1] "~"
## [1] "GPB"## Warning in summary.lm(a[[j + (i - 3) * (15 - i)/2 + 1]]): essentially
## perfect fit: summary may be unreliable##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.499e-15 -3.990e-17 1.225e-16 1.904e-16 6.704e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.835e-15 2.616e-16 7.014e+00 3.02e-09 ***
## x[, i + j] 1.000e+00 5.537e-17 1.806e+16 < 2e-16 ***
## x[, 1] 2.490e-16 2.052e-16 1.214e+00 0.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.516e-16 on 57 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 1.75e+32 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "GPB"
## [1] "~"
## [1] "GNB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2187 -0.5873 -0.0204 0.5594 3.0484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.84204 0.28136 2.993 0.00408 **
## x[, i + j] 0.52138 0.03806 13.698 < 2e-16 ***
## x[, 1] 0.37801 0.23273 1.624 0.10984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.868 on 57 degrees of freedom
## Multiple R-squared: 0.7829, Adjusted R-squared: 0.7752
## F-statistic: 102.7 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "GPB"
## [1] "~"
## [1] "BPLFA"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.96132 -0.43595 0.00921 0.35370 1.80760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34195 0.20509 1.667 0.101
## x[, i + j] 0.36260 0.01678 21.615 <2e-16 ***
## x[, 1] 0.20501 0.15249 1.344 0.184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5643 on 56 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.8992, Adjusted R-squared: 0.8956
## F-statistic: 249.7 on 2 and 56 DF, p-value: < 2.2e-16
##
## [1] "GPB"
## [1] "~"
## [1] "FPLFA"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9505 -0.6184 -0.1129 0.4499 4.1725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.91544 0.36155 2.532 0.0141 *
## x[, i + j] 0.87207 0.08497 10.263 1.44e-14 ***
## x[, 1] 0.35058 0.28713 1.221 0.2271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.066 on 57 degrees of freedom
## Multiple R-squared: 0.6728, Adjusted R-squared: 0.6613
## F-statistic: 58.59 on 2 and 57 DF, p-value: 1.493e-14
##
## [1] "GPB"
## [1] "~"
## [1] "ANE"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4943 -0.5552 -0.1101 0.4157 3.1416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5317 0.2879 1.847 0.070 .
## x[, i + j] 2.2384 0.1559 14.355 <2e-16 ***
## x[, 1] 0.1507 0.2278 0.661 0.511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.837 on 57 degrees of freedom
## Multiple R-squared: 0.7981, Adjusted R-squared: 0.791
## F-statistic: 112.6 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "GPB"
## [1] "~"
## [1] "MOB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80525 -0.55599 -0.06862 0.46392 2.98660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2219 0.2971 4.112 0.000127 ***
## x[, i + j] 6.8217 0.5807 11.746 < 2e-16 ***
## x[, 1] 0.5885 0.2582 2.279 0.026423 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9722 on 57 degrees of freedom
## Multiple R-squared: 0.7275, Adjusted R-squared: 0.718
## F-statistic: 76.1 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "GNB"
## [1] "~"
## [1] "GNB"## Warning in summary.lm(a[[j + (i - 3) * (15 - i)/2 + 1]]): essentially
## perfect fit: summary may be unreliable##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.191e-15 -3.001e-16 -1.911e-16 3.440e-17 8.921e-15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.835e-15 3.964e-16 -4.629e+00 2.17e-05 ***
## x[, i + j] 1.000e+00 5.362e-17 1.865e+16 < 2e-16 ***
## x[, 1] -3.128e-16 3.279e-16 -9.540e-01 0.344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.223e-15 on 57 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 1.8e+32 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "GNB"
## [1] "~"
## [1] "BPLFA"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8015 -0.3432 0.0265 0.4429 0.8874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.38413 0.20598 -1.865 0.0674 .
## x[, i + j] 0.63806 0.01685 37.870 <2e-16 ***
## x[, 1] -0.18390 0.15315 -1.201 0.2349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5668 on 56 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.9634, Adjusted R-squared: 0.9621
## F-statistic: 737 on 2 and 56 DF, p-value: < 2.2e-16
##
## [1] "GNB"
## [1] "~"
## [1] "FPLFA"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6948 -0.9338 0.2303 0.8776 2.4292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34125 0.46725 0.730 0.468
## x[, i + j] 1.61855 0.10981 14.739 <2e-16 ***
## x[, 1] -0.01442 0.37108 -0.039 0.969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 57 degrees of freedom
## Multiple R-squared: 0.7992, Adjusted R-squared: 0.7922
## F-statistic: 113.5 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "GNB"
## [1] "~"
## [1] "ANE"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6393 -1.0485 0.5281 1.2084 2.5141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4981 0.5452 0.914 0.365
## x[, i + j] 3.6165 0.2953 12.248 <2e-16 ***
## x[, 1] -0.1894 0.4313 -0.439 0.662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.585 on 57 degrees of freedom
## Multiple R-squared: 0.7341, Adjusted R-squared: 0.7247
## F-statistic: 78.66 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "GNB"
## [1] "~"
## [1] "MOB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5111 -1.0153 0.4521 1.0574 3.1359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2858 0.4629 2.777 0.0074 **
## x[, i + j] 11.7850 0.9048 13.025 <2e-16 ***
## x[, 1] 0.4757 0.4023 1.183 0.2419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.515 on 57 degrees of freedom
## Multiple R-squared: 0.7571, Adjusted R-squared: 0.7486
## F-statistic: 88.83 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "BPLFA"
## [1] "~"
## [1] "BPLFA"## Warning in summary.lm(a[[j + (i - 3) * (15 - i)/2 + 1]]): essentially
## perfect fit: summary may be unreliable##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.396e-14 -1.710e-16 7.410e-16 1.083e-15 4.334e-15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.250e-15 1.692e-15 5.467e+00 1.1e-06 ***
## x[, i + j] 1.000e+00 1.384e-16 7.225e+15 < 2e-16 ***
## x[, 1] 1.280e-15 1.258e-15 1.017e+00 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.656e-15 on 56 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 2.713e+31 on 2 and 56 DF, p-value: < 2.2e-16
##
## [1] "BPLFA"
## [1] "~"
## [1] "FPLFA"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2663 -1.2106 -0.0438 0.8916 6.4301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4166 0.7778 1.821 0.0739 .
## x[, i + j] 2.4673 0.1802 13.689 <2e-16 ***
## x[, 1] 0.2920 0.5816 0.502 0.6176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.156 on 56 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.7787, Adjusted R-squared: 0.7708
## F-statistic: 98.51 on 2 and 56 DF, p-value: < 2.2e-16
##
## [1] "BPLFA"
## [1] "~"
## [1] "ANE"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.160 -1.616 0.163 1.557 4.079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.20610 0.78388 1.539 0.130
## x[, i + j] 5.78756 0.41856 13.827 <2e-16 ***
## x[, 1] -0.06903 0.58247 -0.119 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.14 on 56 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.7821, Adjusted R-squared: 0.7743
## F-statistic: 100.5 on 2 and 56 DF, p-value: < 2.2e-16
##
## [1] "BPLFA"
## [1] "~"
## [1] "MOB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5889 -1.8191 0.3509 1.4991 5.0891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7614 0.6902 4.001 0.000187 ***
## x[, i + j] 18.2733 1.3300 13.739 < 2e-16 ***
## x[, 1] 0.9847 0.5727 1.719 0.091077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.15 on 56 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.7799, Adjusted R-squared: 0.7721
## F-statistic: 99.23 on 2 and 56 DF, p-value: < 2.2e-16
##
## [1] "FPLFA"
## [1] "~"
## [1] "FPLFA"## Warning in summary.lm(a[[j + (i - 3) * (15 - i)/2 + 1]]): essentially
## perfect fit: summary may be unreliable##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.393e-16 -6.433e-17 7.090e-18 6.754e-17 5.812e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.586e-16 4.803e-17 9.549e+00 1.98e-13 ***
## x[, i + j] 1.000e+00 1.129e-17 8.859e+16 < 2e-16 ***
## x[, 1] -4.327e-17 3.814e-17 -1.134e+00 0.261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416e-16 on 57 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 4.103e+33 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "FPLFA"
## [1] "~"
## [1] "ANE"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.86964 -0.53874 -0.01531 0.59876 2.35392
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.47185 0.29435 1.603 0.114
## x[, i + j] 2.00227 0.15940 12.561 <2e-16 ***
## x[, 1] -0.02352 0.23285 -0.101 0.920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8556 on 57 degrees of freedom
## Multiple R-squared: 0.7462, Adjusted R-squared: 0.7373
## F-statistic: 83.8 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "FPLFA"
## [1] "~"
## [1] "MOB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.35813 -0.38219 0.02365 0.36467 1.34232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7293 0.1908 3.823 0.000329 ***
## x[, i + j] 6.9414 0.3728 18.619 < 2e-16 ***
## x[, 1] 0.3216 0.1658 1.940 0.057287 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6241 on 57 degrees of freedom
## Multiple R-squared: 0.865, Adjusted R-squared: 0.8602
## F-statistic: 182.6 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "ANE"
## [1] "~"
## [1] "ANE"## Warning in summary.lm(a[[j + (i - 3) * (15 - i)/2 + 1]]): essentially
## perfect fit: summary may be unreliable##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.170e-15 -1.050e-17 6.900e-17 9.340e-17 3.799e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.147e-15 1.484e-16 7.726e+00 1.96e-10 ***
## x[, i + j] 1.000e+00 8.037e-17 1.244e+16 < 2e-16 ***
## x[, 1] 1.030e-16 1.174e-16 8.770e-01 0.384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.314e-16 on 57 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 8.254e+31 on 2 and 57 DF, p-value: < 2.2e-16
##
## [1] "ANE"
## [1] "~"
## [1] "MOB"
##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82403 -0.16600 0.02694 0.12395 1.11996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4664 0.1194 3.907 0.000251 ***
## x[, i + j] 2.6791 0.2333 11.483 < 2e-16 ***
## x[, 1] 0.2160 0.1037 2.082 0.041807 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3906 on 57 degrees of freedom
## Multiple R-squared: 0.717, Adjusted R-squared: 0.707
## F-statistic: 72.2 on 2 and 57 DF, p-value: 2.383e-16
##
## [1] "MOB"
## [1] "~"
## [1] "MOB"## Warning in summary.lm(a[[j + (i - 3) * (15 - i)/2 + 1]]): essentially
## perfect fit: summary may be unreliable##
## Call:
## lm(formula = x[, i] ~ x[, i + j] + x[, 1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.043e-17 -1.176e-17 -4.757e-18 4.319e-18 2.841e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.147e-16 1.230e-17 9.319e+00 4.68e-13 ***
## x[, i + j] 1.000e+00 2.405e-17 4.158e+16 < 2e-16 ***
## x[, 1] -1.509e-17 1.069e-17 -1.412e+00 0.164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.026e-17 on 57 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 8.782e+32 on 2 and 57 DF, p-value: < 2.2e-16