[R] help interpreting output?

[Michael F. Palopoli]

Dear R experts,

I'm hoping someone can help me to interpret the results of building gam's with mgcv in R.

Below are summaries of two gam's based on the same dataset. The first gam (named "gam.mod") has six predictor variables. The second gam (named "gam.mod2") is exactly the same except it is missing one of the predictor variables. What is confusing me is the estimated defrees of freedom for each of the splines in the second model....

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> summary.gam(mod.gam)

Family: gaussian
Link function: identity

Formula:
INT ~ s(IGS) + s(L2E) + s(TED) + s(PSD) + s(OPD) + s(GED)

Parametric coefficients:
Estimate std. err. t ratio Pr(>|t|)
constant 302.32 5.192 58.23 < 2.22e-16

Approximate significance of smooth terms:
edf chi.sq p-value
s(IGS) 4.254 58.308 9.5524e-12
s(L2E) 1 8.7673 0.0030668
s(TED) 1 8.3915 0.0037697
s(PSD) 1 6.0234 0.014118
s(OPD) 2.289 12.745 0.0024349
s(GED) 3.791 152.68 < 2.22e-16

R-sq.(adj) = 0.885 Deviance explained = 91.1%
GCV score = 2124.9 Scale est. = 1617.3 n = 60

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>summary.gam(mod.gam2)

Family: gaussian
Link function: identity

Formula:
INT ~ s(IGS) + s(L2E) + s(TED) + s(PSD) + s(OPD)

Parametric coefficients:
Estimate std. err. t ratio Pr(>|t|)
constant 302.32 4.736e-14 6.384e+15 < 2.22e-16

Approximate significance of smooth terms:
edf chi.sq p-value
s(IGS) 1.757e-05 1.3524e+09 < 2.22e-16
s(L2E) 0.009991 0.21394 0.6437
s(TED) 2.945e-05 1.4913e+07 < 2.22e-16
s(PSD) 2.566e-05 6.5495e+06 < 2.22e-16
s(OPD) 5.023e-05 3.2332e+07 < 2.22e-16

R-sq.(adj) = 0.645 Deviance explained = 64.5%
GCV score = 7489.7 Scale est. = 6069.7 n = 60


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Any suggestions about either (1) what went wrong with the second model? or (2) how the heck do I interpet these results?

Thanks,

Mike.

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