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.mes at cbs.dk Priv: PDalgd at gmail.com