lm4 <- lm(logMaxAbund ~ logMass, data=bird, subset=bird$Passerine == 1)

# Examinez les graphiques de diagnostic
opar <- par(mfrow=c(2,2))
plot(lm4)
summary(lm4)
par(opar)

# Comparez la variance expliquée par lm2, lm3 et lm4
str(summary(lm4)) # Rappelez-vous qu'on veut le R^2 ajusté
summary(lm4)$adj.r.squared # R2-adj = -0.02
summary(lm2)$adj.r.squared # R2-adj = 0.05
summary(lm3)$adj.r.squared # R2-adj = 0.25

# Comparez visuellement les trois modèles
opar <- par(mfrow=c(1,3))
plot(logMaxAbund ~ logMass, data=bird, main="All birds", 
     ylab = expression("log"[10]*"(Maximum Abundance)"), 
     xlab = expression("log"[10]*"(Mass)"), pch=19, col="yellowgreen")
abline(lm2,lwd=2)

plot(logMaxAbund ~ logMass, subset=Passerine == 1, data=bird, main="Passerine birds",
     ylab = expression("log"[10]*"(Maximum Abundance)"), 
     xlab = expression("log"[10]*"(Mass)"), pch=19, col="violet")
abline(lm4,lwd=2)

plot(logMaxAbund ~ logMass, data=bird, subset=!bird$Aquatic, main="Terrestrial birds",
     ylab = expression("log"[10]*"(Maximum Abundance)"), 
     xlab = expression("log"[10]*"(Mass)"), pch=19, col="skyblue")
abline(lm3,lwd=2)
par(opar)