Combine logistic regression with bar graph for maturity results - r

Question

I am trying to present the results of a logistic regression analysis for the maturity schedule of a fish species. Below is my reproducible code.
#coded with R version R version 3.0.2 (2013-09-25)
#Frisbee Sailing
rm(list=ls())
library(ggplot2)
library(FSA)
#generate sample data 1 mature, 0 non mature
m<-rep(c(0,1),each=25)
tl<-seq(31,80, 1)
dat<-data.frame(m,tl)
# add some non mature individuals at random in the middle of df to
#prevent glm.fit: fitted probabilities numerically 0 or 1 occurred error
tl<-sample(50:65, 15)
m<-rep(c(0),each=15)
dat2<-data.frame(tl,m)
#final dataset
data3<-rbind(dat,dat2)
ggplot can produce a logistic regression graph showing each of the data points employed, with the following code:
#plot logistic model
ggplot(data3, aes(x=tl, y=m)) +
stat_smooth(method="glm", family="binomial", se=FALSE)+
geom_point()
I want to combine the probability of being mature at a given size, which is obtained, and plotted with the following code:
#plot proportion of mature
#clump data in 5 cm size classes
l50<-lencat(~tl,data=data3,startcat=30,w=5)
#table of frequency of mature individuals by size
mat<-with(l50, table(LCat, m))
#proportion of mature
mat_prop<-as.data.frame.matrix(prop.table(mat, margin=1))
colnames(mat_prop)<-c("nm", "m")
mat_prop$tl<-as.factor(seq(30,80, 5))
# Bar plot probability mature
ggplot(mat_prop, aes(x=tl,y=m)) +
geom_bar(stat="bin")
What I've been trying to do, with no success, is to make a graph that combines both, since the axis are the same it should be straightforward, but I cant seem to make t work. I have tried:
ggplot(mat_prop, aes(x=tl,y=m)) +
geom_bar(stat="bin")+
stat_smooth(method="glm", family="binomial", se=FALSE)
but does not work. Any help would be greatly appreciated. I am new so not able to add the resulting graphs to this post.

Answer 1

I see three problems with your code:
Using stat="bin" in your geom_bar() is inconsisten with giving values for the y-axis (y=m). If you bin, then you count the number of x-values in an interval and use that count as y-value, so there is no need to map your data to the y-axis.
The data for the glm-plot is in data3, but your combined plot only uses mat_prop.
The x-axis of the two plots are acutally not quite the same. In the bar plot, you use a factor variable on the x-axis, making the axis discrete, while in the glm-plot, you use a numeric variable, which leads to a continuous x-axis.
The following code gave a graph combining your two plots:
mat_prop$tl<-seq(30,80, 5)
ggplot(mat_prop, aes(x=tl,y=m)) +
geom_bar(stat="identity") +
geom_point(data=data3) +
geom_smooth(data=data3,aes(x=tl,y=m),method="glm", family="binomial", se=FALSE)
I could run it after first sourcing your script to define all the variables. The three problems mentioned above are adressed as follows:
I used geom_bar(stat="identity") in order not to use binning in the bar plot.
I use the data-argument in geom_point and geom_smooth in order to use the correct data (data3) for these parts of the plot.
I redifine mat_prop$tl to make it numeric. It is then consistent with the column tl in data3, which is numeric as well.
(I also added the points. If you don't want them, just remove geom_point(data=data3).)
The plot looks as follows: