I have a generalised additive model calculated using the bam function from the mgcv package. I have two random effects in the model and 5 fixed effects, one of which is smoothed. The R2 are quite high (see below) and I'm interested to know if this is being driven by the random effects and how much of a role the fixed effects play in explaining the variance.
I've previously done this on GLMM by calculating the conditional and marginal R2 values. Is there a way of doing this with a GAMM? Specifically one using the bam function from mgcv?
deg_test1 <- bam(deg ~ SE_score + s(ri,bs="ad") + sex + species + year +
s(code, bs = 're') + s(station, bs = 're'),
family=nb(), data=node_dat, na.action = "na.fail", discrete = TRUE)
> summary(deg_test1)
Family: Negative Binomial(41687141.289)
Link function: log
Formula:
deg ~ SE_score + s(ri, bs = "ad") + sex + species + year +
s(code, bs = "re") + s(station, bs = "re")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.06071 0.10751 -0.565 0.57232
SE_score -0.30396 0.15245 -1.994 0.04618 *
sexM 0.17797 0.09329 1.908 0.05643 .
speciesSilvertip Shark 0.58195 0.09445 6.161 7.24e-10 ***
year2015 -0.07197 0.05307 -1.356 0.17508
year2016 -0.11550 0.05927 -1.949 0.05131 .
year2017 -0.18810 0.06467 -2.908 0.00363 **
year2018 -0.43988 0.07953 -5.531 3.19e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(ri) 6.029 7.228 21.651 < 2e-16 ***
s(code) 83.744 133.000 8.133 0.001792 **
s(station) 43.302 62.000 15.196 0.000659 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.836 Deviance explained = 95.7%
fREML = 76757 Scale est. = 1 n = 82210