Hi all,
I have a question regarding the anova function aov(). I want to perform an
anova calculation
using one grouping variable but more than one numerical variables:
So instead of:
aov(v ~ g)
I want something like
aov(v1 + v2 + v3 ~ g)
Essentially I want to find out whether the variables v1, v2, v3, etc can
collectively discriminate
between different values of variable g. Could somebody tell whether this is
possible and if so
how?
Also, could somebody please tell me the difference between the aov() function
and the anova()
function. I've been using aov() so far and it seems to work fine. Could
somebody tell me what the
difference is and which one is better.
Any help would be greatly appreciated. Many Thanks
Rishabh
Hi all,
I have a question regarding the anova function aov(). I want to perform an
anova calculation
using one grouping variable but more than one numerical variables:
So instead of:
aov(v ~ g)
I want something like
aov(v1 + v2 + v3 ~ g)
Essentially I want to find out whether the variables v1, v2, v3, etc can
collectively discriminate
between different values of variable g. Could somebody tell whether this is
possible and if so
how?
Also, could somebody please tell me the difference between the aov() function
and the anova()
function. I've been using aov() so far and it seems to work fine. Could
somebody tell me what the
difference is and which one is better.
Any help would be greatly appreciated. Many Thanks
Rishabh
Rishabh Gupta wrote:> I want something like > aov(v1 + v2 + v3 ~ g) > Essentially I want to find out whether the variables v1, v2, v3, etc can collectively discriminate > between different values of variable g. Could somebody tell whether this is possible and if so > how?Rishabh: With v1, v2, v3, and g in the dataframe mydata, try the following: summary(manova(cbind(v1, v2, v3) ~ g, data = mydata), test="Wilks") hope it helps, Chuck Cleland
Prof, It is true that you sent me the email earlier but the way that my email is sorted, Chuck's emails were at the top of my list and hence I read it and responded to it first. With respect to providing me with *all* the info, Chuck did this also although he did so in two differnt emails. He seems to have CCed only one of them two the r-help mailing list though. Finally, my follow up question was about something that was mentioned in Chuck's email but was not in yours. THANK YOU Rishabh --- Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote: > On Fri, 11 Apr 2003, Rishabh Gupta wrote:> > > Thanks very much for your help. Just a follow up question.. > > Although you had been told precisely that earlier, for some reason I don't > get any thanks. > > > Like I said I was using aov() instead of anova(). I want to maintain maximum compatability > with > > what I've been using so far and I notice that manova() is just a wrapper to > > aov(). > > Notice a bit harder. The `wrapper' changes the class, and summary.manova > does all the work. > > > How important > > is it to use summary(......, test="Wilks") exactly, do you think that the default test > statistic > > would be sufficient. > > Sufficient for what? The default test is the default for a good reason. > > > Once again, many thanks for your help. > > Once again, it is not polite to ignore others who give you the same help, > especially when it comes from the person who provided you with *all* of > this. > > -- > Brian D. Ripley, ripley at stats.ox.ac.uk > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ > University of Oxford, Tel: +44 1865 272861 (self) > 1 South Parks Road, +44 1865 272866 (PA) > Oxford OX1 3TG, UK Fax: +44 1865 272595 >