多重共线性的诊断

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在非线性回归模型中如何进行多种共线性的诊断？ 比如我下面的代码，怎么添加诊断：
option nocenter;
dm 'log;clear;output;clear';
proc import datafile="c:\data.xls" replace
     out=one;
     getnames=yes;
data one;  set one;
proc nlin data=one;         
  parms b1=0.3 b2=-0.1 b3=-0.4 b4=0.1 b5=-0.8 b6=0.02 b7=0.06 b8=-0.02;
   
faNew = exp(b1+b2*tmax+b3*hmin+b4*wmax+b5*wa+b6*ev+b7*p+b8*tmm);                     

  model fa = faNew;         
  output out=two predicted=fahat

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关键词：多重共线性 多重共线 共线性 Predicted replace 诊断 线性

Are you going to diagnose the multicollinearity among the seven variables:
tmax, hmin, wmax, wa, ev, p and tmm ?

Yes, I want  to diagnose the multicollinearity among the seven variables:
tmax, hmin, wmax, wa, ev, p and tmm .

Can you help me? Thank you.

3# bridog

I think it is just the same as in linear models, because it is about the relationship among independent variables.

1. High correlation between any two independent variables suggests multicollinearity.
In SAS, the CORR procedure is used to computes Pearson correlation coefficients. PROC CORR statement invokes the CORR procedure and specifies the data set to be analyzed. The VAR statement identifies variables to correlate and their order in the correlation matrix.
proc corr data=one;
var  Tmax hmin wmax wa ev p  tmm ;
run;

2. We speak about strong multicollinearity if the largest VIF, variance inflation factor, is larger than 10 or if the mean of the VIF values is considerably larger then 1.
In SAS, the VIF option for MODEL statement in PROC REG requests the calculation of the variance inflation factors.

proc reg data= one;

model fa= Tmax hmin wmax wa ev p  tmm /vif;

run;

3. The eigenvalues of the correlation matrix is another tool to detect multicollinearity. If there is perfect multicollinearity, some of the eigenvalues are zero. Near collinearity is associated with small eigenvalues. In SAS, the COLLINOINT option for MODEL statement in PROC REG provides analysis of the correlation matrix for the original data.

proc reg data= one;

model fa= Tmax hmin wmax wa ev p  tmm /collinoint;

run;

Thank you! I know what you mean. But I want to add the code of multicollinearity diagnose to the nonlinear model. So what can I do?