Hello - I am fairly new to R, (i.e., ability to create functions/write
programs insignificant) and was wondering if there might be a convenient way
to model the following: I want to fit a gaussian glm to grouped data, while
allowing for unequal variances in each of the groups.
More specifically, my data set looks something like this:
----------------
data group
1 76 1
2 82 1
3 83 1
4 54 1
5 35 1
6 46 1
7 87 1
8 68 1
9 87 2
10 95 2
11 98 2
12 100 2
13 109 2
14 109 2
15 100 2
16 81 2
17 75 2
18 68 2
19 67 2
20 105 3
.... et cetera.
---------------
There are seven groups in all, each with a different number of observations.
The idea is to compare a model in which all the data points can be modeled
with a single mean (i.e., if all the group means are equal), or if the data
suggests that each of the groups has a different mean. In other words, I
want to do a Likelihood ratio test on whether or not the group means are
significantly different from each other: the full model would be glm(data ~
as.factor(group)-1, family = gaussian), to be compared against a restricted
model that only includes an intercept. However, I also need to allow for the
fact that each group has a different variance. And this I have no idea how
to do. I would really appreciate some help in this matter.
Thank you in advance,
Dawn.
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