Building a list of nonlinear regressions using ggplot

Question

As an output graph of non-linear regression analysis at this link

https://stats.stackexchange.com/questions/209087/non-linear-regression-mixed-model

With this dataset:

zz <-(" iso temp diam Itiquira 22 5.0 Itiquira 22 4.7 Itiquira 22 5.4 Itiquira 25 5.8 Itiquira 25 5.4 Itiquira 25 5.0 Itiquira 28 4.9 Itiquira 28 5.2 Itiquira 28 5.2 Itiquira 31 4.2 Itiquira 31 4.0 Itiquira 31 4.1 Londrina 22 4.5 Londrina 22 5.0 Londrina 22 4.4 Londrina 25 5.0 Londrina 25 5.5 Londrina 25 5.3 Londrina 28 4.6 Londrina 28 4.3 Londrina 28 4.9 Londrina 31 4.4 Londrina 31 4.1 Londrina 31 4.4 Sinop 22 4.5 Sinop 22 5.2 Sinop 22 4.6 Sinop 25 5.7 Sinop 25 5.9 Sinop 25 5.8 Sinop 28 6.0 Sinop 28 5.5 Sinop 28 5.8 Sinop 31 4.5 Sinop 31 4.6 Sinop 31 4.3" ) df <- read.table(text=zz, header = TRUE) 

And this rigged model with four parameters:

Thank you: Optimum temperature

yours: diameter at optimal

thq: Curvature

thc: asymmetry

 library(nlme) df <- groupedData(diam ~ temp | iso, data = df, order = FALSE) n0 <- nlsList(diam ~ thy * exp(thq * (temp - thx)^2 + thc * (temp - thx)^3), data = df, start = c(thy = 5.5, thq = -0.01, thx = 25, thc = -0.001)) > n0 # Call: # Model: diam ~ thy * exp(thq * (temp - thx)^2 + thc * (temp - thx)^3) | iso # Coefficients: thy thq thx thc # Itiquira 5.403118 -0.007258245 25.28318 -0.0002075323 # Londrina 5.298662 -0.018291649 24.40439 0.0020454476 # Sinop 5.949080 -0.012501783 26.44975 -0.0002945292 # Degrees of freedom: 36 total; 24 residual # Residual standard error: 0.2661453 

Is there a way to build the set values ​​in ggplot, like a specific smooth () function?

I think I found ... (based on http://rforbiochemists.blogspot.com.br/2015/06/plotting-two-enzyme-plots-with-ggplot.html )

 ip <- ggplot(data=daf, aes(x=temp, y=diam, colour = iso)) + geom_point() + facet_wrap(~iso) ip + geom_smooth(method = "nls", method.args = list(formula = y ~ thy * exp(thq * (x-thx)^2 + thc * (x - thx)^3), start = list(thy=5.4, thq=-0.01, thx=25, thc=0.0008)), se = F, size = 0.5, data = subset(daf, iso=="Itiquira")) + geom_smooth(method = "nls", method.args = list(formula = y ~ thy * exp(thq * (x-thx)^2 + thc * (x - thx)^3), start = list(thy=5.4, thq=-0.01, thx=25, thc=0.0008)), se = F, size = 0.5, data = subset(daf, iso=="Londrina")) + geom_smooth(method = "nls", method.args = list(formula = y ~ thy * exp(thq * (x-thx)^2 + thc * (x - thx)^3), start = list(thy=5.4, thq=-0.01, thx=25, thc=0.0008)), se = F, size = 0.5, data = subset(daf, iso=="Sinop")) 

Answer 1

Responding to this with a slightly more fundamental approach ggplot (combining output in one data frame, the structure of which corresponds to the structure of the source data). Unfortunately, finding confidence intervals in nls forecasts is not so simple (finding solutions related to loading or the delta method):

 tempvec <- seq(22,30,length.out=51) pp <- predict(n0,newdata=data.frame(temp=tempvec)) ## combine predictions with info about species, temp pdf <- data.frame(iso=names(pp), temp=rep(tempvec,3), diam=pp) 

Create a schedule:

 library(ggplot2) ggplot(df,aes(temp,diam,colour=iso))+ stat_sum()+ geom_line(data=pdf)+ facet_wrap(~iso)+ theme_bw()+ scale_size(range=c(1,4))+ scale_colour_brewer(palette="Dark2")+ theme(legend.position="none", panel.spacing=grid::unit(0,"lines"))