Plotting binomial glm with interactions in numeric variables - r

Question

I want to know if is possible to plotting binomial glm with interactions in numeric variables. In my case:
##Data set artificial
set.seed(20)
d <- data.frame(
mating=sample(0:1, 200, replace=T),
behv = scale(rpois(200,10)),
condition = scale(rnorm(200,5))
)
#Binomial GLM ajusted
model<-glm(mating ~ behv + condition, data=d, family=binomial)
summary(model)
In a situation where behv and condition are significant in the model
#Plotting first for behv
x<-d$behv ###Take behv values
x2<-rep(mean(d$condition),length(d_p[,1])) ##Fixed mean condition
# Points
plot(d$mating~d$behv)
#Curve
curve(exp(model$coefficients[1]+model$coefficients[2]*x+model$coefficients[3]*x2)
/(1+exp(model$coefficients[1]+model$coefficients[2]*x+model$coefficients[3]*x2)))
But doesn't work!! There is another correct approach?
Thanks

Answer 1

It seems like your desired output is a plot of the conditional means (or best-fit line). You can do this by computing predicted values with the predict function.
I'm going to change your example a bit, to get a nicer looking result.
d$mating <- ifelse(d$behv > 0, rbinom(200, 1, .8), rbinom(200, 1, .2))
model <- glm(mating ~ behv + condition, data = d, family = binomial)
summary(model)
Now, we make a newdata dataframe with your desired values:
newdata <- d
newdata$condition <- mean(newdata$condition)
newdata$yhat <- predict(model, newdata, type = "response")
Finally, we sort newdata by the x-axis variable (if not, we'll get lines that zig-zag all over the plot), and then plot:
newdata <- newdata[order(newdata$behv), ]
plot(newdata$mating ~ newdata$behv)
lines(x = newdata$behv, y = newdata$yhat)
Output: