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.

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