Dear R-helpers,
My system: R 3.5.3 osx, mgcv 1.8-28
I try to build a model for five parameters
Model = gam( Y ~ s(x1) + s(x2) + s(x3) + s(x4) + s(x5))
But, We found these five parameters have multicollinearity. We observed a
significant correlation between these parameters. So, we performed a PCA to
convert the set of five correlated air pollution variables into a set of
linearly uncorrelated main variations. (PC1 and PC2)
Then, we build the new GAM model using these two main variations as predictor
variables.
Model = gam( Y ~ s(PC1) + s(PC2))
Then, we can obtain the P value of PC1 and PC2.
Approximate significance of smooth terms:
edf Ref.df Chi.sq p-value
s(PC1) 8.892 8.996 57402 <2e-16 ***
s(PC2) 8.978 9.000 15125 <2e-16 ***
But, we still cannot find out a way to calculate the P value for the original
five parameter.
Jason / Lin Jun
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