r - Variable Selection with mgcv -


is there way of automating variable selection of gam in r, similar step? i've read documentation of step.gam , selection.gam, i've yet see answer code works. additionally, i've tried method= "reml" , select = true, neither remove insignificant variables model.

i've theorized create step model , use variables create gam, not seem computationally efficient.

example:

library(mgcv)  set.seed(0) dat <- data.frame(rsp = rnorm(100, 0, 1),                    pred1 = rnorm(100, 10, 1),                    pred2 = rnorm(100, 0, 1),                    pred3 = rnorm(100, 0, 1),                    pred4 = rnorm(100, 0, 1))  model <- gam(rsp ~ s(pred1) + s(pred2) + s(pred3) + s(pred4),              data = dat, method = "reml", select = true)  summary(model)  #family: gaussian  #link function: identity   #formula: #rsp ~ s(pred1) + s(pred2) + s(pred3) + s(pred4)  #parametric coefficients: #            estimate std. error t value pr(>|t|) #(intercept)  0.02267    0.08426   0.269    0.788  #approximate significance of smooth terms: #            edf ref.df     f p-value   #s(pred1) 0.8770      9 0.212  0.1174   #s(pred2) 1.8613      9 0.638  0.0374 * #s(pred3) 0.5439      9 0.133  0.1406   #s(pred4) 0.4504      9 0.091  0.1775   --- #signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  #r-sq.(adj) =  0.0887   deviance explained = 12.3% #-reml = 129.06  scale est. = 0.70996   n = 100

marra , wood (2011, computational statistics , data analysis 55; 2372-2387) compare various approaches feature selection in gams. concluded additional penalty term in smoothness selection procedure gave best results. can activated in mgcv::gam() using select = true argument/setting, or of following variations:

model <- gam(rsp ~ s(pred1,bs="ts") + s(pred2,bs="ts") + s(pred3,bs="ts") + s(pred4,bs="ts"), data = dat, method = "reml") model <- gam(rsp ~ s(pred1,bs="cr") + s(pred2,bs="cr") + s(pred3,bs="cr") + s(pred4,bs="cr"),              data = dat, method = "reml",select=t) model <- gam(rsp ~ s(pred1,bs="cc") + s(pred2,bs="cc") + s(pred3,bs="cc") + s(pred4,bs="cc"),              data = dat, method = "reml") model <- gam(rsp ~ s(pred1,bs="tp") + s(pred2,bs="tp") + s(pred3,bs="tp") + s(pred4,bs="tp"), data = dat, method = "reml")

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