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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