[R] Finding out collinearity in regression

[Simon Blomberg]

At 01:45 PM 30/06/2005, Young Cho wrote:
>Hi, I am trying to find out a collinearity in
>explanatory variables with alias().
>
>I creat a dataframe:
>
>dat <- ds[,sapply(ds,nlevels)>=2]
>dat$Y <- Response
>
>Explanatory variables are factor and response is
>continuous random variable. When I run a regression, I
>have the following error:
>
>fit <- aov( Y ~ . , data = dat)
>Error in "contrasts<-"(`*tmp*`, value =
>"contr.treatment") :
>         contrasts can be applied only to factors with
>2 or more levels

1. Sounds like at least one of your factors has only one level. This should 
be easy to spot.

2. Have you considered package perturb?

HTH,

Simon.



>I think there is a dependency in explanatory
>variables. So, I wanted to use alias to find out a
>dependency in design matrix but I can't because I
>cannot create "fit" in the first place.
>
>One of examples I found is:
>
>carprice1.lm <- lm(gpm100 ~
>Type+Min.Price+Price+Max.Price+Range.Price,data=carprice)
>alias(carprice1.lm)
>
>But, what if I can create lm object ? Then is there a
>way to find out a dependency in design matrix? Thanks
>a lot for help in advance!
>
>-Young.
>
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Simon Blomberg, B.Sc.(Hons.), Ph.D, M.App.Stat.
Centre for Resource and Environmental Studies
The Australian National University
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