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) #########################################################
NaN response with gam (mgcv library)
2 messages · varin sacha, Bert Gunter
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 <r-help at r-project.org> 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) #########################################################
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