geom_smooth does not appear on ggplot

Question

I am working on some viscosity experiments and I'm trying to make an Eyring plot with ν vs. θ. When I create the plot with ggplot2 I can't get my model displayed.

These are the values used:

> theta
[1] 25 30 35 40 45
> nu
[1] 1.448462 1.362730 1.255161 1.167408 1.083005

Here I create the plot with my values from above:

plot <-
  ggplot()+
  geom_point(mapping = aes(theta, nu), colour = "#0072bd", size = 4, shape = 16)+
  theme_bw()+
  labs(
    x = expression(paste(theta,  " ", "[°C]")), 
    y = expression(paste("ln(", nu, ")", " ", "[mPa*s]")))+
  ylim(0, 10)+
  xlim(0, 100)

That's what the plot looks like.

Now, I add my model with geom_smooth()

plot +
  geom_smooth(
    method = "nls", 
    method.args = list(formula = nu~a*exp(b/theta), 
                       start=list(a=1, b=0.1)))

But nothing happens... Not even an error message and the plot looks just the same as before.

I also tried to put the formula directly as a geom_smooth() argument and the start values as well,

plot +
  geom_smooth(
    method = "nls",
    formula = nu~a*exp(b/theta),
    start=list(a=1, b=0.1))

but then I get the

Error:Unknown parameter: start

Can anyone find the mistake I'm making?

Thanks in advance!

Cheers

EDIT

When separating the aesthetics mapping,

plot <-
  ggplot()+
  aes(theta, nu)+
  geom_point(colour = "#0072bd", size = 4, shape = 16)+
  theme_bw()+
  labs(
    x = expression(paste(theta,  " ", "[°C]")), 
    y = expression(paste("ln(", nu, ")", " ", "[mPa*s]")))+
  ylim(0, 10)+
  xlim(0, 100)

I get the following error (and still nothing changes):

Warning message:

1: In min(x) : no non-missing arguments to min; returning Inf 2: In max(x) : no non-missing arguments to min; returning -Inf 3: Computation failed in stat_smooth(): $ operator is invalid for atomic vectors

Answer 1

You have several things going on, many of which were pointed out in the comments.

Once you put your variables in a data.frame for ggplot and define you aesthetics either globally in ggplot or within each geom, the main thing going on is that the formula in geom_smooth expects you to refer to y and x instead of the variable names. geom_smooth will use the variables you mapped to y and x in aes.

The other complication you will run into is outlined here. Because you don't get standard errors from predict.nls, you need to use se = FALSE in geom_smooth.

Here is what your geom_smooth code might look like:

geom_smooth(method = "nls", se = FALSE, 
              method.args = list(formula = y~a*exp(b/x), start=list(a=1, b=0.1)))

And here is the full code and plot.

ggplot(df, aes(theta, nu))+
    geom_point(colour = "#0072bd", size = 4, shape = 16)+
    geom_smooth(method = "nls", se = FALSE,
              method.args = list(formula = y~a*exp(b/x), start=list(a=1, b=0.1))) +
    theme_bw()+
    labs(
        x = expression(paste(theta,  " ", "[°C]")), 
        y = expression(paste("ln(", nu, ")", " ", "[mPa*s]")))+
    ylim(0, 10) +
    xlim(0, 100)

Note that geom_smooth won't fit outside the range of the dataset unless you use fullrange = TRUE instead of the default. This may be pretty questionable if you only have 5 data points.

ggplot(df, aes(theta, nu))+
    geom_point(colour = "#0072bd", size = 4, shape = 16)+
    geom_smooth(method = "nls", se = FALSE, fullrange = TRUE,
              method.args = list(formula = y~a*exp(b/x), start=list(a=1, b=0.1))) +
    theme_bw()+
    labs(
        x = expression(paste(theta,  " ", "[°C]")), 
        y = expression(paste("ln(", nu, ")", " ", "[mPa*s]")))+
    ylim(0, 10) +
    xlim(0, 100)

Answer 2

I just wrote this answer as @lukeA made the comment.

df<- data.frame(theta = c(25, 30, 35, 40, 45),
                nu = c( 1.448462, 1.362730, 1.255161, 1.167408, 1.083005))

myModel <- nls(nu~a*exp(b/theta), data=df, start=list(a=1, b=0.1))

myPredict <- expand.grid(theta = seq(5, 100, by =0.1))  
  #expand.grid here in case your model has more than one variable
  #Caution, extrapolating well beyond the data
myPredict$fit <- predict(myModel, newdata= myPredict) 


plot + geom_line(data = myPredict, aes(x= theta, y= fit))