Chapter 7 Fitting splines using mgcv

7.1 Fiting model

library(mgcv)
mod3<-gam(data=d,richness~s(grain))

7.2 Summary model

summary(mod3)

## 
## Family: gaussian 
## Link function: identity 
## 
## Formula:
## richness ~ s(grain)
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   5.6889     0.4601   12.36  2.6e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##            edf Ref.df     F  p-value    
## s(grain) 3.615  4.468 15.92 3.25e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.619   Deviance explained = 65.1%
## GCV = 10.616  Scale est. = 9.5269    n = 45

7.3 Plotting model

theme_set(theme_bw())
g0<-ggplot(d,aes(x=grain,y=richness))
g1<-g0+geom_point() + stat_smooth(method = "gam", formula = y ~ s(x)) 
g1 + xlab("Mean grain size") + ylab("Species richness")