I used R to run a Geographically Weighted Regression (GWR) model.
The resulting model comes with a bunch of values that are given in a table. These are:
- X.Intercept, X.Intercept_se (standard error), X.Intercept_se_EDF
- variable1, variable1_se, variable1_se_EDF
- variable2, variable2_se, variable2_se_EDF,
- variableX, ...
- pred, pred_se, pred_se_1
Could you provide a short summary of what the calculated values mean?
Variable coefficient: I know that for each variable a coefficient is given, which shows the strength of the correlation. But are these values normalized and can be compared between variables? As an example: if variable1 has a coefficient of 5 in a certain area, does a coefficient for variable2 of 10 in the same area mean that its correlation is twice as high?
Standard error, EDF: What does the standard error tell? And what is the value EDF?
Sum, gwr.e, Intercept: I am also not sure what sum.w, gwr.e and the Intercept are for.
Prediction: The prediction values show what y-value is expected through the model.
Local R2: The local R2 shows the model performance at a local scale.