[英]Draw a logarithmic curve on graph in R
我有以下數據集,並且繪制時具有曲線關系
Fish.species.richness Habitat.Complexity log.habitat
17 0.6376 -0.1954858
13 0.2335 -0.6317131
30 0.2866 -0.5427238
20 0.3231 -0.4906630
22 0.1073 -0.9694003
25 0.2818 -0.5500590
2 0.2182 -0.6612448
4 0.0189 -1.7246886
19 0.2960 -0.5287083
25 0.5507 -0.2590849
29 0.2689 -0.5704900
21 0.6286 -0.2016602
18 0.1557 -0.8078509
24 0.6851 -0.1642460
30 0.5059 -0.2959353
32 0.4434 -0.3532043
29 0.3585 -0.4455108
32 0.5920 -0.2276783
當我記錄x軸並進行線性回歸以找到截距和斜率時,我能夠添加適合數據的線:
summary(lm(Fish.species.richness~log.habitat,data=three))
plot(three$log.habitat,
three$Fish.species.richness,
xlab='Log Habitat Complexity',
ylab='Fish Species Richness')
abline(29.178,13.843)
然而,當我進行曲線回歸並嘗試繪制曲線時,它不適合數據,我哪里出錯了?
mod.log<-lm(Fish.species.richness~log(Habitat.Complexity),data=three)
plot(three$Habitat.Complexity,
three$Fish.species.richness)
abline(mod.log)
為了清晰和靈活地使用其他模型類型,您可能希望使用predict
函數來計算預測變量范圍內的預測值:
mod.log<-lm(Fish.species.richness~log(Habitat.Complexity), data=three)
# predict along predictor variable range
newdat <- data.frame(Habitat.Complexity=seq(min(three$Habitat.Complexity), max(three$Habitat.Complexity),,100))
newdat$Fish.species.richness <- predict(mod.log, newdat, type="response")
# plot
plot(Fish.species.richness ~ Habitat.Complexity, data=three)
lines(Fish.species.richness ~ Habitat.Complexity, data=newdat)
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