在SPSS-analyze-Regression-linear-Statistic中可以实现，勾选Collinearity diagnostics

在SPSS-analyze-Regression-linear-Statistic中可以实现，勾选Collinearity diagnostics

在SPSS-analyze-Regression-linear-Statistic中可以实现，勾选Collinearity diagnostics

在SPSS-analyze-Regression-linear-Statistic中可以实现，勾选Collinearity diagnostics

你那是在回归中，楼主问的是做完岭回归怎么得到VIF值，看看岭回归中K值的选取有没有消除共线性的影响

Using R Package 'genridge' http://cran.r-project.org/web/packages/genridge/genridge.pdf
Examples
data(longley)
lmod <- lm(Employed ~ GNP + Unemployed + Armed.Forces + Population + Year + GNP.deflator, data=longley)
vif(lmod)
longley.y <- longley[, "Employed"]
longley.X <- data.matrix(longley[, c(2:6,1)])
lambda <- c(0, 0.005, 0.01, 0.02, 0.04, 0.08)
lridge <- ridge(longley.y, longley.X, lambda=lambda)
coef(lridge)
vridge <- vif(lridge)
vridge
# plot VIFs
pch <- c(15:18, 7, 9)
clr <- c("black", rainbow(5, start=.6, end=.1))
matplot(rownames(vridge), vridge, type=’b’,
xlab=’Ridge constant (k)’, ylab="Variance Inflation",
xlim=c(0, 0.08),
col=clr, pch=pch, cex=1.2)
text(0.0, vridge[1,], colnames(vridge), pos=4)
matplot(lridge$df, vridge, type=’b’,
xlab=’Degrees of freedom’, ylab="Variance Inflation",
col=clr, pch=pch, cex=1.2)
text(6, vridge[1,], colnames(vridge), pos=2)
# more useful to plot VIF on the sqrt scale
matplot(rownames(vridge), sqrt(vridge), type=’b’,

xlab=’Ridge constant (k)’, ylab=expression(sqrt(VIF)),
xlim=c(-0.01, 0.08),
col=clr, pch=pch, cex=1.2, cex.lab=1.25)
text(-0.01, sqrt(vridge[1,]), colnames(vridge), pos=4, cex=1.2)
matplot(lridge$df, sqrt(vridge), type=’b’,
xlab=’Degrees of freedom’, ylab=expression(sqrt(VIF)),
col=clr, pch=pch, cex=1.2, cex.lab=1.25)
text(6, sqrt(vridge[1,]), colnames(vridge), pos=2, cex=1.2)

For SAS program to do Ridge Regression

Data one; input x1 x2 y; cards;
1 1 2
5 4 5
5 5 6
5 6 6
9 9 10
;
proc reg ridge=.00 to .1 by .02 outest=b;
model y = x1 x2 /vif;
proc plot; plot (x1 x2) * _RIDGE_ / vref=0 vpos=25 hpos=45;
proc print;

proc reg data=one ridge= .00 to .1 by .02 outest=c outvif;
model y = x1 x2;
proc print;

run;

for NCSS

http://www.ncss.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Ridge_Regression.pdf

这个不是岭回归，只是求出回归的VIF

楼主，与您遇到同样的问题，不知解决了木有？等待您的回复

你找到解决办法没？

推荐你去看一本书吧，老外写的《例解回归分析》，里面有岭回归的推导过程，都是矩阵运算，但是我算出的VIF结果老是不对，或者也可以看人大出版的何晓群《应用回归分析》也有岭回归的过程。

希望和你可以交流一下

你这个是线性回归，人家要的是岭回归

您好，请问您知道如何计算岭回归后的VIF值了吗？求指导！

我放弃了岭回归，这方法没人用