Importance of components:
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
Standard deviation 2.0751580 0.8122145 0.7706381 0.58291741 0.26332268
Proportion of Variance 0.7177134 0.1099487 0.0989805 0.05663212 0.01155647
Cumulative Proportion 0.7177134 0.8276622 0.9266427 0.98327479 0.99483126
Comp.6
Standard deviation 0.176103526
Proportion of Variance 0.005168742
Cumulative Proportion 1.000000000
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
X1 -0.381 0.572 -0.149 -0.650 -0.112 0.267
X2 -0.409 0.267 0.560 0.285 -0.491 -0.355
X3 -0.452 0.545 0.220 0.670
X4 -0.383 -0.427 -0.620 -0.525 -0.100
X5 -0.465 -0.211 0.634 -0.572
综合评分 -0.348 -0.641 0.485 -0.443 0.143 0.129
这是我主成分分析的结果,我选取了一个主成分做回归,
Call:
lm(formula = 综合评分 ~ Z1, data = a1)
Residuals:
Min 1Q Median 3Q Max
-0.14338 -0.08760 -0.04388 0.05203 0.29141
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.62671 0.03784 122.279 <2e-16 ***
Z1 -0.04864 0.01928 -2.523 0.0302 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1311 on 10 degrees of freedom
Multiple R-squared: 0.389, Adjusted R-squared: 0.3279
F-statistic: 6.366 on 1 and 10 DF, p-value: 0.03022
这是我回归的结果,模型的拟合度低了好多,之前有0.98.现在只有不到0.4,请问这样的模型可以用吗?