Depending on the data and the research question, a meta-analytic
approach might be appropriate. You can see every campaign as a
"study". See the package metafor for example. You can only draw very
general conclusions, but at least your inference will be closer to
correct.
Cheers
Joris
On Thu, Jul
Thank you
Actually when I do this myself I always try to make day or week averages
if possible. However this was done by one of my colleagues and basically
the aggregation was done on basis of campaigns. There is 4 to 6 campaigns
per year and sometimes there is apparent relationship in aggregat
You examples are pretty extreme... Combining 120 data points in 4
points is off course never going to give a result. Try :
fac <- rep(1:8,each=15)
xprum <- tapply(x, fac, mean)
yprum <- tapply(y, fac, mean)
plot(xprum, yprum)
Relation is not obvious, but visible.
Yes, you lose information. Yes,
Dear all
My question is more on statistics than on R, however it can be
demonstrated by R. It is about pros and cons trying to find a relationship
by aggregated data. I can have two variables which can be related and I
measure them regularly during some time (let say a year) but I can not
meas
4 matches
Mail list logo
