I am performing a multiple regression with GAM.I found the significant predictors using the stepAIC function and now I am performing a model checking with mod.check().
This is what a get:
m2.k1 <- gam(Totden ~ s(T, k=12) + s(S, k=12) + s(Si, k=12), data = train.data)
gam.check(m2.k1)
Method: GCV Optimizer: magic
Smoothing parameter selection converged after 7 iterations.
The RMS GCV score gradient at convergence was 105455022 .
The Hessian was positive definite.
Model rank = 34 / 34
Basis dimension (k) checking results. Low p-value (k-index<1) may
indicate that k is too low, especially if edf is close to k'.
k' edf k-index p-value
s(T) 11.00 4.75 0.97 0.32
s(S) 11.00 7.41 1.06 0.74
s(Si) 11.00 3.72 0.92 0.10
As the p-values were quite high I thought that the model was working fine. Then, I looked at the plots and now I can't understand if my model is performing right or not, if it is acceptable or if I have to reject it..any advice would be of a great help and very appreciated.