Plotting a Binomial GLM using ggplot-CodePudding

I have created the following Binomial GLM model and wish to plot it using ggplot. The issue i am having is how I plot multiple variables as the x and y axis.

notUsing, using are being predicted using age education wantsMore here. How do i add these as arguments into the aes() of ggplot as there are multiple variables?

This should be clearer below.

model created;

my_model = glm(cbind(notUsing, using) ~ age   education   wantsMore,
           data = contraceptive2,
           family = binomial(link = "logit"))

plotting the model; (Hopefully this will make it easier to see where i am failing. I need to replay the x and y for my data, but as they contain multiple variables i dont know how to do this)

#make predictions
my_model_preds = predict(my_model, contraceptive2,  se.fit = TRUE, type = 'response')

#ggplot of the model
ggplot(contraceptive2, aes(x, y))  
  geom_point()  
  geom_line(aes(x, my_model_preds), col = 'blue')

part of data if needed;

head(contraceptive2)
    age education wantsMore notUsing using
1   <25       low       yes       53     6
2   <25       low        no       10     4
3   <25      high       yes      200    52
4   <25      high        no       50    10
5 25-29       low       yes       60    14
6 25-29       low        no       19    10
7  25-29      high       yes      155    54
8  25-29      high        no       65    27
9  30-39       low       yes      112    33
10 30-39       low        no       77    80
11 30-39      high       yes      118    46
12 30-39      high        no       68    78
13 40-49       low       yes       35     6
14 40-49       low        no       46    48
15 40-49      high       yes        8     8
16 40-49      high        no       12    31

CodePudding user response：

With the supplied data, we can see that, because two of your independent variables are binary, it is possible to plot the whole model using error bars with facets and colors:

df <- with(contraceptive2,
     expand.grid(age = unique(age), education = unique(education),
                 wantsMore = unique(wantsMore)))

fits <- predict(my_model, newdata = df, se.fit = TRUE)

# Get odds from modrl
df$prediction <- exp(fits$fit) 
df$upper <- exp(fits$fit   1.96 * fits$se.fit)
df$lower <- exp(fits$fit - 1.96 * fits$se.fit)

# Convert odds to probabilities
df$prediction <- df$prediction / (1   df$prediction)
df$upper <- df$upper / (1   df$upper)
df$lower <- df$lower / (1   df$lower)

# Plot probabilities
ggplot(df, aes(age, prediction))  
  geom_errorbar(aes(ymin = lower, ymax = upper, colour = wantsMore), 
                width = 0.25, size = 1, position = position_dodge(width = 0.4))  
  geom_point(aes(fill = wantsMore), shape = 21, size = 3, 
             position = position_dodge(width = 0.4))  
  facet_grid(~education)  
  theme_light(base_size = 16)  
  scale_y_continuous(name = "Probability of not using", limits = c(0, 1),
                     labels = scales::percent)