I have 11 possible predictor variables and use them to model quite a few
target variables.
In search for a consistent manner and possibly non-manual manner to identify
the significant predictor vars out of the eleven I thought the option
"select=T" might do.
Example: (here only 4 pedictors)
first is vanilla with "select=F"
> fit1<-gam(target~s(mgs)+s(gsd)+s(mud)+s(ssCmax),family=quasi(link=log),data=wspe1,select=F)
> summary(fit1)
Family: quasi
Link function: log
Formula:
target ~ s(mgs) + s(gsd) + s(mud) + s(ssCmax)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -34.57 20.47 -1.689 0.0913 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(mgs) 2.335 2.623 0.260 0.829
s(gsd) 6.868 7.506 13.955 < 2e-16 ***
s(mud) 8.990 9.000 11.727 < 2e-16 ***
s(ssCmax) 6.770 6.978 6.664 7.68e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.402 Deviance explained = 40.4%
GCV score = 8.8563e+05 Scale est. = 8.8053e+05 n = 4511
then turn select=TRUE
fit2<-gam(target~s(mgs)+s(gsd)+s(mud)+s(ssCmax),family=quasi(link=log),data=wspe1,select=TRUE)
> summary(fit2)
Family: quasi
Link function: log
Formula:
target ~ s(mgs) + s(gsd) + s(mud) + s(ssCmax)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1585 1.7439 0.091 0.928
Approximate significance of smooth terms:
edf Ref.df F p-value
s(mgs) 2.456 8 24.50 <2e-16 ***
s(gsd) 7.272 9 14.33 <2e-16 ***
s(mud) 7.678 9 20.38 <2e-16 ***
s(ssCmax) 6.556 9 14.36 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.397 Deviance explained = 40%
GCV score = 8.9209e+05 Scale est. = 8.8715e+05 n = 4511
I seem to not fully understand how to work with "select".
The predictor "mgs" is obviously not significant, as seen from "fit"
(above), yet here it appears as significant. Why was it not dropped? How are
not-significant predictors are identified?
--
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