I'm a bit stuck on running a GLM between 3 continuous variables in R. I can't make them categorical as it removes the significance. I have two questions.
I'm analysing data on eggs.
I had read that you could control for variables by adding them in using
+ like so:
m1 <- glm(Volume ~ Altitude + Breadth)
However, I have since been told that saying in my results that
Volume significantly declines as Altitude increases, when controlling for Breadth
isn't a correct statement. Is it? I was told the following:
The language works in that it IS controlling for it by taking it into account in the model as a fixed effect, BUT it isn’t removing it from the analysis entirely as your sentence suggested.
I'm very confused as I have read on this forum people advising to control variables by adding them in like this.
If I can't control for the variable, I can do an interaction. But for this purpose I had been advised to stay away from them as they are so difficult to explain. However if I can't control for Breadth then I don't have much choice.
m1 <- glm(Volume ~ Altitude*Breadth)
How would you explain this interaction? I was going with
For any given breadth, Volume decreases as Altitude increase
but apparently this isn't right either. The model summary is as below
Please let me know if you would like any more information!
Call: glm(formula = Volume ~ Altitude * Breadth) Deviance Residuals: Min 1Q Median 3Q Max -0.24775 -0.10975 -0.01984 0.06765 0.46698 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -8.5941878 2.2616181 -3.800 0.000461 *** Altitude 0.0080269 0.0033141 2.422 0.019829 * Breadth 0.8964839 0.1913092 4.686 2.93e-05 *** Altitude:Breadth -0.0007117 0.0002800 -2.542 0.014801 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for gaussian family taken to be 0.02604406) Null deviance: 3.0980 on 45 degrees of freedom Residual deviance: 1.0939 on 42 degrees of freedom AIC: -31.449 Number of Fisher Scoring iterations: 2