ggplot GLM fitted curve without interaction

Question

I want to add the fitted function from GLM on a ggplot. By default, it automatically create the plot with interaction. I am wondering, if I can plot the fitted function from the model without interaction. For example,

dta <- read.csv("http://www.ats.ucla.edu/stat/data/poisson_sim.csv")
dta <- within(dta, {
  prog <- factor(prog, levels=1:3, labels=c("General", "Academic", "Vocational"))
  id <- factor(id)
})

plt <- ggplot(dta, aes(math, num_awards, col = prog)) + 
    geom_point(size = 2) + 
    geom_smooth(method = "glm", , se = F, 
        method.args = list(family = "poisson"))

print(plt)

gives the plot with interaction,

However, I want the plot from the model,

`num_awards` = ß0 + ß1*`math` + ß2*`prog` + error

I tried to get this this way,

mod <- glm(num_awards ~ math + prog, data = dta, family = "poisson")

fun.gen <- function(awd) exp(mod$coef[1] + mod$coef[2] * awd)
fun.acd <- function(awd) exp(mod$coef[1] + mod$coef[2] * awd + mod$coef[3])
fun.voc <- function(awd) exp(mod$coef[1] + mod$coef[2] * awd + mod$coef[4])

ggplot(dta, aes(math, num_awards, col = prog)) +
    geom_point() +
    stat_function(fun = fun.gen, col = "red") +
    stat_function(fun = fun.acd, col = "green") +
    stat_function(fun = fun.voc, col = "blue") +
    geom_smooth(method = "glm", se = F, 
        method.args = list(family = "poisson"), linetype = "dashed")

The output plot is

Is there any simple way in ggplot to do this efficiently?

Answer 1

There's no way that I know of to trick geom_smooth() into doing this, but you can do a little better than you've done. You still have to fit the model yourself and add the lines, but you can use the predict() method to generate the predictions and load them into a data frame with the same structure as the original data ...

mod <- glm(num_awards ~ math + prog, data = dta, family = "poisson")
## generate prediction frame
pframe <- with(dta,
             expand.grid(math=seq(min(math),max(math),length=51),
                         prog=levels(prog)))
## add predicted values (on response scale) to prediction frame
pframe$num_awards <- predict(mod,newdata=pframe,type="response")

ggplot(dta, aes(math, num_awards, col = prog)) +
   geom_point() +
   geom_smooth(method = "glm", se = FALSE,
       method.args = list(family = "poisson"), linetype = "dashed")+
   geom_line(data=pframe)  ## use prediction data here
          ## (inherits aesthetics etc. from main ggplot call)

(the only difference here is that the way I've done it the predictions span the full horizontal range for all groups, as if you had specified fullrange=TRUE in geom_smooth()).

In principle it seems as though the sjPlot package should be able to handle this sort of thing, but it looks like the relevant bit of code for doing this plot type is hard-coded to assume a binomial GLM ... oh well.