Re: [R] NaN response with gam (mgcv library)
Dear Simon, Thanks ! It works ! Best, Le lundi 1 mai 2023 à 11:19:26 UTC+2, Simon Wood a écrit : try... sum(residuals(model1)^2) On 30/04/2023 22:03, varin sacha via R-help wrote: > Dear R-experts, > > Here below my R code. I get a NaN response for gam with mgcv library. How to > solve that problem? > Many thanks. > > # > library(mgcv) > > y=c(23,24,34,40,42,43,54,34,52,54,23,32,35,45,46,54,34,36,37,48) > x1=c(0.1,0.3,0.5,0.7,0.8,0.9,0.1,0.7,0.67,0.98,0.56,0.54,0.34,0.12,0.47,0.52,0.87,0.56,0.71,0.6) > x2=c(9,7,5,3,2,1,1,2,8,9,6,3,1,5,6,7,3,1,3,5) > x3=c(11,10,13,15,10,9,14,16,18,19,20,9,13,12,14,17,21,19,23,12) > > model=lm(y~x1+x2+x3) > model1=gam(y ~ s(x1, bs = 'cr', k = 3) + s(x2, bs = 'cr', k = 3)+ s(x3, bs = > 'cr', k = 3)) > > > #Calculate MSE > model_summ=summary(model) > mean(model_summ$residuals^2) > > > #Calculate MSE > model_summ=summary(model1) > mean(model_summ$residuals^2) > # > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Simon Wood, School of Mathematics, University of Edinburgh, https://www.maths.ed.ac.uk/~swood34/ __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] NaN response with gam (mgcv library)
try... sum(residuals(model1)^2) On 30/04/2023 22:03, varin sacha via R-help wrote: Dear R-experts, Here below my R code. I get a NaN response for gam with mgcv library. How to solve that problem? Many thanks. # library(mgcv) y=c(23,24,34,40,42,43,54,34,52,54,23,32,35,45,46,54,34,36,37,48) x1=c(0.1,0.3,0.5,0.7,0.8,0.9,0.1,0.7,0.67,0.98,0.56,0.54,0.34,0.12,0.47,0.52,0.87,0.56,0.71,0.6) x2=c(9,7,5,3,2,1,1,2,8,9,6,3,1,5,6,7,3,1,3,5) x3=c(11,10,13,15,10,9,14,16,18,19,20,9,13,12,14,17,21,19,23,12) model=lm(y~x1+x2+x3) model1=gam(y ~ s(x1, bs = 'cr', k = 3) + s(x2, bs = 'cr', k = 3)+ s(x3, bs = 'cr', k = 3)) #Calculate MSE model_summ=summary(model) mean(model_summ$residuals^2) #Calculate MSE model_summ=summary(model1) mean(model_summ$residuals^2) # __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Simon Wood, School of Mathematics, University of Edinburgh, https://www.maths.ed.ac.uk/~swood34/ __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] NaN response with gam (mgcv library)
There is no "residuals" component of a gam fit, as you could have immediately determined by: > names(model_summ) [1] "p.coeff" "se""p.t" [4] "p.pv" "residual.df" "m" [7] "chi.sq""s.pv" "scale" [10] "r.sq" "family""formula" [13] "n" "dev.expl" "edf" [16] "dispersion""pTerms.pv" "pTerms.chi.sq" [19] "pTerms.df" "cov.unscaled" "cov.scaled" [22] "p.table" "pTerms.table" "s.table" [25] "method" Bert On Sun, Apr 30, 2023 at 2:03 PM varin sacha via R-help wrote: > Dear R-experts, > > Here below my R code. I get a NaN response for gam with mgcv library. How > to solve that problem? > Many thanks. > > # > library(mgcv) > > y=c(23,24,34,40,42,43,54,34,52,54,23,32,35,45,46,54,34,36,37,48) > > x1=c(0.1,0.3,0.5,0.7,0.8,0.9,0.1,0.7,0.67,0.98,0.56,0.54,0.34,0.12,0.47,0.52,0.87,0.56,0.71,0.6) > x2=c(9,7,5,3,2,1,1,2,8,9,6,3,1,5,6,7,3,1,3,5) > x3=c(11,10,13,15,10,9,14,16,18,19,20,9,13,12,14,17,21,19,23,12) > > model=lm(y~x1+x2+x3) > model1=gam(y ~ s(x1, bs = 'cr', k = 3) + s(x2, bs = 'cr', k = 3)+ s(x3, bs > = 'cr', k = 3)) > > > #Calculate MSE > model_summ=summary(model) > mean(model_summ$residuals^2) > > > #Calculate MSE > model_summ=summary(model1) > mean(model_summ$residuals^2) > # > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
