[R] find confounder in covariates

[Young Cho]

Hi,

I was wondering if there is a way, or function in R to
find confounders. For istance, 

> a = sample( c(1:3), size=10,replace=T)
> X1 = factor( c('A','B','C')[a] )
> X2 = factor( c('Aa','Bb','Cc')[a] )
> Xmat = data.frame(X1,X2,rnorm(10),rnorm(10))
> dimnames(Xmat)[[2]] = c('z1','z2','z3','y')

Now, z2 is just an alias of z1. There can be a
collinearity like one is a linear combination of
others. If you run lm on it:
 
> f = lm(y~.,data=Xmat)
> summary(f)

Call:
lm(formula = y ~ ., data = Xmat)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2853 -0.3708 -0.1224  0.4617  1.2821 

Coefficients: (2 not defined because of singularities)
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.82141    0.44583   1.842   0.1150  
z1B         -1.34167    0.65176  -2.059   0.0852 .
z1C          0.80891    1.07639   0.751   0.4808  
z2Bb              NA         NA      NA       NA  
z2Cc              NA         NA      NA       NA  
z3           0.04231    0.23397   0.181   0.8625  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.'
0.1 ' ' 1 

Residual standard error: 0.971 on 6 degrees of freedom
Multiple R-Squared: 0.5086,     Adjusted R-squared:
0.2629 
F-statistic:  2.07 on 3 and 6 DF,  p-value: 0.2057 

In this case, I can look at data and figure out which
variable is confounded with which. But, if we have
many categorial covariates ( not necessarily same
number of levels ), it is almost impossible to check
it out.  

Any help would be greatly appreicated. Thanks.

Young.