Hi R people,
I have a very basic question to ask - I'm sorry if it's been asked before, but
I searched the archives and could not find an answer. All the examples I found
were much more complicated/nuanced versions of the problem - my question is
much more simple.
I have data with multiple, nested fixed effects (as I understand it, fixed
effects are specified by the experimental design while random effects are
measured) and one continuous response variable. All the fixed effects are
catagorical.
e.g.
fix1 fix2 fix3 response
0 0 0 16.260
0 0 0 16.128
0 0 1 22.969
0 0 1 23.245
0 1 0 14.687
0 1 0 14.635
0 1 1 22.954
0 1 1 23.345
1 0 0 19.866
1 0 0 19.589
1 0 1 22.748
1 0 1 22.817
1 1 0 17.861
1 1 0 17.872
1 1 1 22.925
1 1 1 23.138
I was thinking I could use a linear model to determine whether any of the
nested fixed effects or their interactions effect the response, but I could not
determine how to specify whether effects were fixed or random, and how to
specify nesting.
For example:
lm(response~ fix1+fix2+fix3)
The above, as I understand it, simply asks whether the effects fix1 through
fix4 have an effect on the response. However, in reality my experimental
design has multiple levels of nesting:
fix1(fix2(fix3(fix4)))
So, how do I do this? To specify nesting, do I need to use another type of
model such as lmer or glm?
I also don't know whether the above example is specifying whether the effects
are fixed or random - how do I do this?
Thanks very much,
Jojo
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