Use `nlsfit` within geom_smooth to add exponential

Question

I would like to use the nlsfit from the easynls package with ggplot2 if at all possible.

This is what I have done so far:

1. Set up subset data:

   library('ggplot2')
   library('easynls')
   
   x <- seq(25,97)
   y <- c(0.014, 0.016, 0.015, 0.016, 0.018, 0.019, 0.023, 0.019, 0.021, 0.017, 0.018, 0.016, 0.016, 0.020, 0.018, 0.019, 0.022, 0.023, 0.027, 0.027, 0.028, 0.031, 0.029, 0.032, 0.030, 0.030, 0.030, 0.033, 0.039, 0.038, 0.039, 0.046, 0.042, 0.043, 0.050, 0.054, 0.059, 0.064, 0.062, 0.058, 0.063, 0.069, 0.071, 0.069, 0.073, 0.071, 0.070, 0.077, 0.086, 0.077, 0.090, 0.086, 0.098, 0.108, 0.112, 0.116, 0.129, 0.120, 0.128, 0.141, 0.150, 0.143, 0.148, 0.150, 0.162, 0.162, 0.168, 0.152, 0.151, 0.161, 0.169, 0.189, 0.184)
   data <- data.frame(x,y)
   

2. Run NLSfit on sample data

   nlsfit = nlsfit(data.frame(x,y), model=6, start=c(250,0.05))
   nlsfit
   # $Model
   # [1] "y~a*exp(b*x)"
   
   # $Parameters
   #                              y
   # coefficient a           0.0061
   # coefficient b           0.0358
   # p-value t.test for a    0.0000
   # p-value t.test for b    0.0000
   # r-squared               0.9793
   # adjusted r-squared      0.9790
   # AIC                  -500.0812
   # BIC                  -493.2098
   

3. Plot using plot() with a line

   plot(x, y)
   a <- nlsfit$Parameters[1,]
   b <- nlsfit$Parameters[2,]
   lines(x, a*exp(x*b), col="steelblue")
   

4. Attempt to use nls with ggplot2 (this works - but the fit isn't as good on the full dataset)...

   ggplot(data, aes(x=x, y=y)) + geom_point(
          ) + geom_smooth(method="nls", formula=y~a*exp(x*b),
          method.args=list(start=c(a=250,b=0.05)), se=FALSE)
   

5. Attempt to nlsfit with ggplot2 -- doesn't work

   # Below doesn't work
   ggplot(data, aes(x=x, y=y)) + geom_point(
          ) + geom_smooth(method="nlsfit", formula=y~a*exp(x*b),
          method.args=list(data.frame(x, y),
                           model=6, start=c(250,0.05)), se=FALSE)
   
   # Warning message:
   # Computation failed in `stat_smooth()`:
   # unused arguments (formula, weights = weight, list(x = 25:97, y = c(0.014, 0.016, 0.015, 0.016, 0.018, 0.019, 0.023, 0.019, 0.021, 0.017, 0.018, 0.016, 0.016, 0.02, 0.018, 0.019, 0.022, 0.023, 0.027, 0.027, 0.028, 0.031, 0.029, 0.032, 0.03, 0.03, 0.03, 0.033, 0.039, 0.038, 0.039, 0.046, 0.042, 0.043, 0.05, 0.054, 0.059, 0.064, 0.062, 0.058, 0.063, 0.069, 0.071, 0.069, 0.073, 0.071, 0.07, 0.077, 0.086, 0.077, 0.09, 0.086, 0.098, 0.108, 0.112, 0.116, 0.129, 0.12, 0.128, 0.141, 0.15, 0.143, 0.148, 0.15, 0.162,
   # 0.162, 0.168, 0.152, 0.151, 0.161, 0.169, 0.189, 0.184)))
   

Is this possible - would appreciate any help. Thanks.

Answer 1

You can try stat_function to make the last part work:

a <- nlsfit$Parameters[row.names(nlsfit$Parameters) == 'coefficient a',]
b <- nlsfit$Parameters[row.names(nlsfit$Parameters) == 'coefficient b',]
ggplot(data, aes(x=x, y=y)) + geom_point() + 
  stat_function(fun=function(x) a*exp(b*x), colour = "blue")