Hi Drew and R-help,
Many thanks for your email, and your explanations and suggestions have been
very helpful in aiding me to understand this problem. Should you, or anyone
else on the helplist have time, perhaps I may elaborate on my problem with a
little more detail.
I appreciate the
Matt,
The interaction term is why the usual transformation doesn't work, at least
initially.
The R parameterization of factor effects makes it easy to fall into the trap of
thinking that the coefficient for a main effect "means" something when there is
an interaction in the model. Sometimes
On 12/01/16 10:54, Matt Perkins wrote:
Hi Drew and R-help,
Many thanks for your email, and your explanations and suggestions have been
very helpful in aiding me to understand this problem. Should you, or anyone
else on the helplist have time, perhaps I may elaborate on my problem with a
Just to echo Bob O'Hara's comment and elaborate a bit more - Don't model
average the regression coefficients, especially if you are considering
models with and without interactions among the predictors. Follow the link
provided by Bob to Cade (2015. Model averaging and muddled multimodel
