# Testing difference between two non linear growth curves

I would like to identify the significance of the difference between two growth curves with the same formula but different parameters. The dummy data generated by the code produces a normal spread of values for each x increment giving two curves that are visually distinct. I suspect there is no clear cut 'best' solution but comments would be welcome. I am not keen on the transform -> lm route.

Thanks.

library(car)
library(nls)
#### Make some tree growth data according to height ~ 1.3 + (theta1 * DiaBH)/(theta2 + DiaBH)
dbh <-seq(from=1,to= 75, by =5)
pines <-data.frame(1.3,0,"") # starter data frame
names(pines) <-c("height","DiaBH","Species")
a <- 22 # theta1 for P.peuce
b <- 10 # theta2 for P.peuce
c <- 25 # theta 1 for P.Sitch
d <- 30 # theta2 for P.sitch
### Assume a normal distribution and build a random
### data set of heights for each increment of DiaBH for each species
for(i in 1:length(dbh)){
hs <- rnorm(8, 1.3+((dbh[i]*a)/(b + dbh[i])),.8)
ds <- rep(dbh[i],8)
ns <- rep("P.Peuce",8)
hsdsns <-as.data.frame(cbind(hs,ds,ns))
names(hsdsns) <-c("height","DiaBH","Species")
pines <-rbind(pines,hsdsns)
hs <- rnorm(8, 1.3+((dbh[i]*c)/(d + dbh[i])),.8)
ds <- rep(dbh[i],8)
ns <- rep("P.Sitch",8)
hsdsns <-as.data.frame(cbind(hs,ds,ns))
names(hsdsns) <-c("height","DiaBH","Species")
pines <-rbind(pines,hsdsns)
}
pines <-pines[-1,] # strip off the starter row
pines$height <-as.numeric(pines$height)
pines$DiaBH <-as.numeric(pines$DiaBH)
pines$Sp <-Recode(pines$Species, "'P.Peuce'=1;'P.Sitch'=0 ")
pines$Sp <-factor(pines$Sp) # remove unwanted levels
pines$Species <- factor(pines$Species) # remove unwanted levels

### Plot shows the two curves to be visually distinct #######################
sp <-scatterplot(main="Comparison of Growth Rates",
legend.coords = "bottomright", x = pines$DiaBH,y=pines$height,
xlab = "Diameter at Breast Height (cm)", ylab="Height (M)",
groups = pines$Species,reg.line = FALSE,legend.title = "Species") #### nls analysis ########################################################### with(pines, table(Sp)) ### Firstly all together pines.nls.1 <- nls(height ~ 1.3 + ((DiaBH*theta1)/(theta2+DiaBH)), pines, start = list(theta1=40, theta2=40)) summary(pines.nls.1) bs <-coef(pines.nls.1) ### Then by species pines.nls.2 <- nls(height ~ 1.3 + ((DiaBH*theta1[Sp])/(theta2[Sp]+DiaBH)), pines, start = list(theta1=rep(bs[1],2), theta2=rep(bs[2],2))) summary(pines.nls.2) ### or alternatively... library(statmod) compareGrowthCurves(pines$Species,as.matrix(pines$height),nsim = 10000)  ## 1 Answer I'm not sure what exactly is meant by "significance of the difference between two growth curves". nlme::gnls fits can be used to test significance of parameter differences: library(nlme) fit <- gnls(height ~ 1.3 + ((DiaBH*theta1)/(theta2+DiaBH)), pines, start = list(theta1=c(40, 0), theta2=c(40, 0)), params = theta1 + theta2 ~ Sp) summary(fit)$tTable

Value Std.Error    t-value       p-value
theta1.(Intercept)  24.142765 0.5171966  46.680050 3.597291e-121
theta1.Sp1          -1.887628 0.5714321  -3.303328  1.103850e-03
theta2.(Intercept)  27.601700 1.4718774  18.752717  1.179388e-48
theta2.Sp1         -17.054024 1.5450285 -11.037999  4.113452e-23


If you have repeated measures, you should use nlme::nlme instead.

If you really want to show that both curves are "significantly different", I'd suggest plotting bootstrap confidence bands.