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I am trying to mimic partial dependence plots as explained in R's pdp package (https://journal.r-project.org/archive/2017/RJ-2017-016/RJ-2017-016.pdf). The second page (page 422) of the document describes a loop to calculate partial dependence for a specific variable.

I tried to mimic this on the PimaIndiansDiabetes dataset in R, focusing on the pregnant variable. pregnant ranges from 0 to 17. Here's my code:

library(mlbench)
library(pdp)
library(caret)
library(randomForest)

data("PimaIndiansDiabetes")

#Create 0/1 for target variable
dmy <- dummyVars(~., data=PimaIndiansDiabetes, fullRank = T)
pid <- as.data.frame(predict(dmy, PimaIndiansDiabetes))
pid$diabetes.pos <- as.factor(pid$diabetes.pos)

#Build random forest model
set.seed(4)
model <- randomForest(diabetes.pos~., data=pid, ntree=10, mtry=3)

#Create partial dependence plot for 'pregnant' using pdp package
pdp.pregnant <- partial(model, pred.var="pregnant", prob=T)

#Create partial dependence plot for 'pregnant' using pdp document methodology
pid2 <- pid
part.pregnant <- data.frame(pregnant=numeric(18), yhat=numeric(18))
for(i in 0:17){
  pid2$pregnant <- i
  part.pred <- as.numeric(as.character(predict(model, pid2)))
  part.pregnant[i+1, 1] <- i
  part.pregnant[i+1, 2] <- mean(part.pred)
}

#Plot both - they are different
plot(pdp.pregnant$pregnant, pdp.pregnant$yhat, type='l')
plot(part.pregnant$pregnant, part.pregnant$yhat, type='l')

enter image description here enter image description here As you can see, the plots are different. So my questions are: 1) Why are they different? Did I miss something in my implementation of the methodology? 2) How am I supposed to interpret the effect of pregnant on diabetes.pos according to the plot from the pdp package?

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  • $\begingroup$ It's a classification problem. partial uses by default prob = FALSE to use centered logit as response scale. Setting prob = TRUE uses a probability scale. $\endgroup$
    – Michael M
    Commented Jan 19, 2018 at 15:36
  • $\begingroup$ Thanks, I changed the code above to include prob=T and put in the new plot. The plots are still different, in fact the are almost mirror images of each other. Does this mean the partial function is just treating the 0/1 prediction in the opposite way I am? Is the correct interpretation of the partial dependence of the pregnant variable that as pregnant goes up, the chance of diabetes.pos goes up? $\endgroup$
    – Gaurav Bansal
    Commented Jan 19, 2018 at 15:55
  • 1
    $\begingroup$ I just added an implementation that matches pdp for regression problems at stats.stackexchange.com/q/50560/99938 if you're interested. $\endgroup$
    – alexpghayes
    Commented Aug 27, 2018 at 2:14
  • $\begingroup$ you can change the class using which.class = 2 which sets the predicted class to the 2nd factor rather than the first. they're usually ordered alphabetically. $\endgroup$
    – pedram
    Commented Apr 6, 2019 at 19:34

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