Plot two curves in logistic regression in R

Question

I am running logistic regression in R (glm). I then manage to plot the result. My code is as follow:

 temperature.glm = glm(Response~Temperature, data=mydata,family=binomial)

 plot(mydata$Temperature,mydata$Response, ,xlab="Temperature",ylab="Probability of Response")
 curve(predict(temperature.glm,data.frame(Temperature=x),type="resp"),add=TRUE, col="red")
 points(mydata$Temperature,fitted(temperature.glm),pch=20)
 title(main="Response-Temperature with Fitted GLM Logistic Regression Line") 

My questions are:

1. How could I plot two logistic regression curve in one plot?
2. I got these two coeffiecient from other statistic software. How could I create random data, plug in these two sets of coef(Set 1 and Set 2) and then produce two logistic regression curves?

The models:

                   SET 1
 (Intercept)     -88.4505
 Temperature       2.9677

                  SET 2
 (Intercept)    -88.585533
 Temperature      2.972168

mydata is in 2 columns and ~ 700 rows.

Response Temperature 
1 29.33 
1 30.37 
1 29.52 
1 29.66 
1 29.57 
1 30.04 
1 30.58 
1 30.41 
1 29.61 
1 30.51 
1 30.91 
1 30.74 
1 29.91 
1 29.99 
1 29.99 
1 29.99 
1 29.99 
1 29.99 
1 29.99 
1 30.71 
0 29.56 
0 29.56 
0 29.56 
0 29.56 
0 29.56 
0 29.57 
0 29.51

Answer 1

1. To plot a curve, you just need to define the relationship between response and predictor, and specify the range of the predictor value for which you'd like that curve plotted. e.g.:

   dat <- structure(list(Response = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
     0L, 0L), Temperature = c(29.33, 30.37, 29.52, 29.66, 29.57, 30.04, 
     30.58, 30.41, 29.61, 30.51, 30.91, 30.74, 29.91, 29.99, 29.99, 
     29.99, 29.99, 29.99, 29.99, 30.71, 29.56, 29.56, 29.56, 29.56, 
     29.56, 29.57, 29.51)), .Names = c("Response", "Temperature"), 
     class = "data.frame", row.names = c(NA, -27L))
   
   temperature.glm <- glm(Response ~ Temperature, data=dat, family=binomial)
   
   plot(dat$Temperature, dat$Response, xlab="Temperature", 
        ylab="Probability of Response")
   curve(predict(temperature.glm, data.frame(Temperature=x), type="resp"), 
         add=TRUE, col="red")
   # To add an additional curve, e.g. that which corresponds to 'Set 1':
   curve(plogis(-88.4505 + 2.9677*x), min(dat$Temperature), 
         max(dat$Temperature), add=TRUE, lwd=2, lty=3)
   legend('bottomright', c('temp.glm', 'Set 1'), lty=c(1, 3), 
          col=2:1, lwd=1:2, bty='n', cex=0.8)
   

   In the second curve call above, we are saying that the logistic function defines the relationship between x and y. The result of plogis(z) is equivalent to that obtained when evaluating 1/(1+exp(-z)). The min(dat$Temperature) and max(dat$Temperature) arguments define the range of x for which y should be evaluated. We don't need to tell the function that x refers to temperature; this is implicit when we specify that the response should be evaluated for that range of predictor values.

   

2. As you can see, the curve function allows you to plot a curve without needing to simulate predictor (e.g. temperature) data. If you still need to do this, e.g. to plot some simulated outcomes of Bernoulli trials that conform to a particular model, then you can try the following:

   n <- 100 # size of random sample
   
   # generate random temperature data (n draws, uniform b/w 27 and 33)
   temp <- runif(n, 27, 33)
   
   # Define a function to perform a Bernoulli trial for each value of temp, 
   #   with probability of success for each trial determined by the logistic
   #   model with intercept = alpha and coef for temperature = beta.
   # The function also plots the outcomes of these Bernoulli trials against the 
   #   random temp data, and overlays the curve that corresponds to the model
   #   used to simulate the response data.
   sim.response <- function(alpha, beta) {
     y <- sapply(temp, function(x) rbinom(1, 1, plogis(alpha + beta*x)))  
     plot(y ~ temp, pch=20, xlab='Temperature', ylab='Response')
     curve(plogis(alpha + beta*x), min(temp), max(temp), add=TRUE, lwd=2)    
     return(y)
   }
   

   Examples:

   # Simulate response data for your model 'Set 1'
   y <- sim.response(-88.4505, 2.9677)
   
   # Simulate response data for your model 'Set 2'
   y <- sim.response(-88.585533, 2.972168)
   
   # Simulate response data for your model temperature.glm
   # Here, coef(temperature.glm)[1] and coef(temperature.glm)[2] refer to
   #   the intercept and slope, respectively
   y <- sim.response(coef(temperature.glm)[1], coef(temperature.glm)[2])
   

   The figure below shows the plot produced by the first example above, i.e. results of a single Bernoulli trial for each value of the random vector of temperature, and the curve that describes the model from which the data were simulated.