I am building regression models to evaluate the effect of several characteristics of genetic variants (my predictors) on a handful of phenotypic parameters (continuous and binary - hence I am building linear and binomial models).
In some cases I am observing that:
- the p-value for the 'slope coefficient' of predictor Z within the model is not significant (p-value displayed in the output of the
summary()function in R
when I compare nested models ([A~ X+Y] vs [A~ X+Y+Z]) with a likelihood ratio test, the model containing Z among predictors has a significantly lower residual deviance than one without Z. I do this with the R command:
How should this type of results be interpreted?
In my understanding this means that, when taken into account, predictor Z significantly improves the model's performance. However, I would then also expect the coefficient of Z in the model to be significantly different from 0.