I'm currently trying to write a linear model with data from some behavioural experiments with termites.
I started to read Ben Bolker's "Ecological Models and Data in R", which has been a great help. My main independent variable is a measure of relatedness, and I'm interested in how behaviour between termites changes in response to relatedness. The response variable is continuous and consists of an aggression index, constructed by counts of differently weighted behaviours divided by the total amount of observed behaviours per experiment.
I realised that considering how the response variable is constructed, we would expect a value of 0.1 as the default, when termites are highly related. This would be at a relatedness value of 0.5, indicating full siblings.
I was looking into how to specify the intercept in linear models with the
offset function, and I found a solution to easily tell the model that the baseline response should be 0.1.
However, this seems to apply to the idea that the intercept is specified for when all dependent variables are 0.
This might be a very trivial issue, but how would you specify an intercept for a specific value of a continuous dependent variable? 0 makes sense in many contexts, but with the relatedness measure we use, a value of 0 and it's effect is what we want to investigate, since it represents no relatedness, and it's not the default.
Or more exact: is there a way of writing the
lm formula to specify that for a specific value of
x, in this case 0.5, one would expect a response
y of 0.1?