# [R] MANCOVA

Jonathan DuBois jonathan.m.dubois at gmail.com
Fri Oct 8 16:18:55 CEST 2010

```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 <pdalgd at 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.mes at cbs.dk  Priv: PDalgd at gmail.com
>

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