Plotting different models

Comments

• richarddmorey

  January 2019

  The basic steps are:

  1. Make a scatterplot
  2. Fit the model
  3. Extract the model parameters from the object (whatever it is)
  4. Use abline() to plot the lines

  You can do this with either lm() estimates or BayesFactor::posterior() estimates (or whatever). I'll demonstrate below with lm() because it is slightly easier.

  # scatter plot
  plot(len ~ dose, data = ToothGrowth, log = "x", 
       pch = as.integer(ToothGrowth$supp)+20,
       bg = as.integer(ToothGrowth$supp), 
       las = 1)
  
  # Use log10 because I plotted the log of x above
  cf = coef(lm(len ~ log10(dose)*supp, data = ToothGrowth))
  # coef extracts the coefficients
  abline(a = cf["(Intercept)"], b = cf["log10(dose)"])
  abline(a = cf["(Intercept)"] + cf["suppVC"], 
         b = cf["log10(dose)"] + cf["log10(dose):suppVC"], col= "red")
  
  
  ## Now for the no-interaction model
  # scatter plot
  plot(len ~ dose, data = ToothGrowth, log = "x", 
       pch = as.integer(ToothGrowth$supp)+20,
       bg = as.integer(ToothGrowth$supp), 
       las = 1)
  
  # Use log10 because I plotted the log of x above
  cf = coef(lm(len ~ log10(dose) + supp, data = ToothGrowth))
  # coef extracts the coefficients
  abline(a = cf["(Intercept)"], b = cf["log10(dose)"])
  abline(a = cf["(Intercept)"] + cf["suppVC"], 
         b = cf["log10(dose)"], col= "red")
  
• MSB

  January 2019

  For many supported models, you can also the tidyverse approach, with the ggeffects package.