Contents
What are residuals in a glm?
Residuals in glm. Raw or response residuals are the difference between the observed and the fitted value: yi − yi. Not very informative in the glm framework.
What is residual deviance in glm?
The residual deviance shows how well the response is predicted by the model when the predictors are included. From your example, it can be seen that the deviance goes up by 3443.3 when 22 predictor variables are added (note: degrees of freedom = no. of observations – no. of predictors) .
What are the assumptions of a GLM?
Assumptions of generalised linear model
- independence of Y.
- correct link function.
- correct scale of measurement of explanatory variables.
- no influential observations.
What are the different types of residuals in GLM?
There are the deviance, working, partial, Pearson, and response residuals. Because these only rely on the mean structure (not the variance), the residuals for the quasipoisson and poisson have the same form. You can take a look at the residuals.glm function for details, but here is an example:
How to get pearsonized residuals in GLMs?
In a normal model, this is a simple difference of expected and observed to obtain a residual. In GLMs, the variance is heteroscedastic because the mean changes as a function of $X$, so you can standardize each residual by dividing by the expected standard error to obtain Pearsonized residuals.
What does the deviance of a GLM look like?
Thus, the deviance residuals are analogous to the conventional residuals: when they are squared, we obtain the sum of squares that we use for assessing the fit of the model. However, while the sum of squares is the residual sum of squares for linear models, for GLMs, this is the deviance. How does such a deviance look like in practice?
Why are the residuals in a generalized linear model misleading?
Residuals for highly skewed dependent variables, eg, exponential, negative binomial, Poisson, etc, can be misleading in graphical displays since models do not reduce or remove the skewness. They leave you with the impression of many large outliers.