Thanks so much Peter, If doing linear regression with a similar goal - to determine a relationship corrected for age, is there a similar procedure? I have been using:
>lm(Y~X+age) However, I am guessing from the previous response that this was simply including both X and age as independent variables in a multiple regression. Is there a more appropriate formula? Thanks again! Jon On Fri, Oct 8, 2010 at 2:29 AM, Peter Dalgaard <pda...@gmail.com> wrote: > On 10/08/2010 06:55 AM, Jonathan DuBois wrote: >> Hi, >> >> I have been using R to do multiple analyses of variance with two >> covariates, but recently found that the results in SPSS were very >> different. I have check several books and web resources and I think >> that both methods are correct, but I am less familiar with R, so I was >> hoping someone could offer some suggestions. Oddly simple ANOVA is the >> same in SPSS and R. Including covariates improves the main effect >> (p-value) in R and diminishes it in SPSS.. >> >> The formula I have been using is: >>> Y = cbind(dV1, dV2, dV3) >>> aov(lm(Y~iV1+cV1+cV2)) > > I wouldn't use aov() and lm() in combination like that. I'm a bit > surprised that it actually does something, in fact -- the argument to > aov() is documented to be a model formula and aov() is not a generic > function. Anyways, what you do get is sequential (type1) ANOVA for each > variable, and these depend on the order of terms in the model. > > What I would do is explicitly to compare the the models with and without > the group effect: > > fit1 <- lm(Y~iV1+cV1+cV2) > fit2 <- lm(Y~cV1+cV2) > anova(fit1, fit2) > > which will give you a multivariate test of iV1 specifically. > >> The main independent variable is disease group and the covariates are >> continuous nuisance variables such as age. Both nuisance variables >> interact with the dependent variable but not each other. The frequency >> distribution of the covariates is similar for each group, but the >> groups are not matched 1 to 1. Therefore we would like to control for >> these factors statistically. Is this the proper formula for such a >> test? If so, what might be cause of major discrepancy with SPSS? > > > > -- > Peter Dalgaard > Center for Statistics, Copenhagen Business School > Phone: (+45)38153501 > Email: pd....@cbs.dk Priv: pda...@gmail.com > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
