Predicted values for logistic regression from glm and stat_smooth in ggplot2 are different

Question

I\'m trying to make this logistic regression graph in ggplot2.

df <- structure(list(y = c(2L, 7L, 776L, 19L, 12L, 26L, 7L, 12L, 8L,
24L, 20L,          


        

Answer 1

Modify your LD.summary to include a new column with group (or appropriate label).

LD.summary$group <- c('LD25','LD50','LD75')

Then modify your geom_segment commands to have a col=LD.summary$group in it (and remove the colour="red"), which plots each segment in a different colour and adds a legend:

geom_segment( aes(...,col=LD.summary$group) )

Also, to avoid having to do the LD.summary$xxx all the time, feed in data=LD.summary to your geom_segment:

geom_segment(data=LD.summary, aes(x=0, y=Pi,xend=LD, yend=Pi, colour=group) )

As to why the graphs are not exactly the same, in the base R graph the x axis goes from ~20 onwards, whereas in ggplot it goes from zero onwards. This is because your second geom_segment starts at x=0. To fix you could change x=0 to x=min(df$x).

To get your y axis label use + scale_y_continuous('Estimated probability').

In summary:

LD.summary$group <- c('LD25','LD50','LD75')
p <- ggplot(data = df, aes(x = x, y = y/n)) +
            geom_point() +
            stat_smooth(method = "glm", family = "binomial") +
            scale_y_continuous('Estimated probability')    # <-- add y label
p <- p + geom_segment(data=LD.summary, aes( # <-- data=Ld.summary
                            x = LD
                          , y = 0
                          , xend = LD
                          , yend = Pi
                          , col = group     # <- colours
                         )
                       )    
p <- p + geom_segment(data=LD.summary, aes( # <-- data=Ld.summary
                            x = min(df$x)   # <-- don't plot all the way to x=0
                          , y = Pi
                          , xend = LD
                          , yend = Pi
                          , col = group     # <- colours
                         )
                       )
print(p)

which yields: