I have the following set of data and when plotted has a curvilinear relationship
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
When I log the x axis and do a linear regression to find the intercept and slope I am able to add a line that fits the data:
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)
However when I then do a curvilinear regression and try to plot the curve it doesn't fit the data, where am I going wrong?
mod.log<-lm(Fish.species.richness~log(Habitat.Complexity),data=three)
plot(three$Habitat.Complexity,
three$Fish.species.richness)
abline(mod.log)
For clarity and flexibility to other model types, you may want to use the predict
function to calculate the predicted values along the range of your predictor variable:
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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