逐步回归分析是以AIC信息统计量为准则，通过选择最小的AIC信息统计量，来达到删除或增加变量的目的。R语言中用于逐步回归分析的函数 step()，drop1()，add1()。
1.载入数据 首先对数据进行多元线性回归分析
tdata<-data.frame(
x1=c( 7, 1,11,11, 7,11, 3, 1, 2,21, 1,11,10),
x2=c(26,29,56,31,52,55,71,31,54,47,40,66,68),
x3=c( 6,15, 8, 8, 6, 9,17,22,18, 4,23, 9, 8),
x4=c(60,52,20,47,33,22, 6,44,22,26,34,12,12),
Y =c(78.5,74.3,104.3,87.6,95.9,109.2,102.7,72.5,
93.1,115.9,83.8,113.3,109.4)
)
tlm<-lm(Y~x1+x2+x3+x4,data=tdata)
summary(tlm)
多元线性回归结果分析
Call:
lm(formula = Y ~ x1 + x2 + x3 + x4, data = tdata)
Residuals:
Min 1Q Median 3Q Max
-3.1750 -1.6709 0.2508 1.3783 3.9254
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.4054 70.0710 0.891 0.3991
x1 1.5511 0.7448 2.083 0.0708 .
x2 0.5102 0.7238 0.705 0.5009
x3 0.1019 0.7547 0.135 0.8959
x4 -0.1441 0.7091 -0.203 0.8441
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.446 on 8 degrees of freedom
Multiple R-squared: 0.9824, Adjusted R-squared: 0.9736
F-statistic: 111.5 on 4 and 8 DF, p-value: 4.756e-07
通过观察，回归方程的系数都没有通过显著性检验
2.逐步回归分析###
tstep<-step(tlm)
summary(tstep)
Start: AIC=26.94
Y ~ x1 + x2 + x3 + x4
Df Sum of Sq RSS AIC
- x3 1 0.1091 47.973 24.974
- x4 1 0.2470 48.111 25.011
- x2 1 2.9725 50.836 25.728
<none> 47.864 26.944
- x1 1 25.9509 73.815 30.576
Step: AIC=24.97
Y ~ x1 + x2 + x4
Df Sum of Sq RSS AIC
<none> 47.97 24.974
- x4 1 9.93 57.90 25.420
- x2 1 26.79 74.76 28.742
- x1 1 820.91 868.88 60.629
结果分析：当用x1 x2 x3 x4作为回归方程的系数时，AIC的值为26.94
去掉x3 回归方程的AIC值为24.974；
去掉x4 回归方程的AIC值为25.011；
……
由于去x3可以使得AIC达到最小值，因此R会自动去掉x3;
去掉x3之后 AIC的值都增加 逐步回归分析终止 得到当前最优的回归方程
Call:
lm(formula = Y ~ x1 + x2 + x4, data = tdata)
Residuals:
Min 1Q Median 3Q Max
-3.0919 -1.8016 0.2562 1.2818 3.8982
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.6483 14.1424 5.066 0.000675 ***
x1 1.4519 0.1170 12.410 5.78e-07 ***
x2 0.4161 0.1856 2.242 0.051687 .
x4 -0.2365 0.1733 -1.365 0.205395
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.309 on 9 degrees of freedom
Multiple R-squared: 0.9823, Adjusted R-squared: 0.9764
F-statistic: 166.8 on 3 and 9 DF, p-value: 3.323e-08
回归系数的显著性水平有所提高 但是x2 x4的显著性水平仍然不理想
3.逐步回归分析的优化
drop1(tstep)
结果分析：
Single term deletions
Model:
Y ~ x1 + x2 + x4
Df Sum of Sq RSS AIC
<none> 47.97 24.974
x1 1 820.91 868.88 60.629
x2 1 26.79 74.76 28.742
x4 1 9.93 57.90 25.420
如果去掉x4 AIC的值从24.974增加到25.420 是三个变量中增加最小的
4.进一步进行多元回归分析
tlm<-lm(Y~x1+x2,data=tdata)
summary(tlm)
结果分析：
Call:
lm(formula = Y ~ x1 + x2, data = tdata)
Residuals:
Min 1Q Median 3Q Max
-2.893 -1.574 -1.302 1.363 4.048
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.57735 2.28617 23.00 5.46e-10 ***
x1 1.46831 0.12130 12.11 2.69e-07 ***
x2 0.66225 0.04585 14.44 5.03e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.406 on 10 degrees of freedom
Multiple R-squared: 0.9787, Adjusted R-squared: 0.9744
F-statistic: 229.5 on 2 and 10 DF, p-value: 4.407e-09
所有的检验均为显著。
因此所得回归方程为y=52.57735+ 1.46831x1+ 0.66225x2.
