CODEWITHSUNDEEP
r
user3389884
user3389884

Reputation: 3

How to create a ROC in R using predicted value from SAS?

I have a dataset from SAS, it is scored data with two columns, y and yhat. y is binary (0,1), yhat is scored value, model is logistic regression. I want create roc in r for this SAS model and compare it with other models in R. I have no clue regarding how to accomplish this? Any suggestions? Thanks.

How to create a ROC in R using predicted value from SAS?

Upvotes: 0

Views: 252

Answers (2)

rawr
rawr

Reputation: 20811

Very simply if you know how ROC curves work. You want to be able to classify people into your dichotomous outcomes, 0 or 1 I am using below, using the predicted values from your model.

So if you were to select a cut-off for your predicted values at 0.5, say anyone above this threshold is considered positive/1/diseased/etc, and anyone below as a 0/unaffected.

That's great, but can that be improved? So the thought here is that if we go through a bunch of cutoff points, which one will be the most accurate in classifying people into our dichotomous outcomes, that is, comparing the predicted values from the model to the actual classifications that we know.

# some data
dat <- data.frame(pred = rep(0:1, each = 50),
                  predict = c(runif(50), runif(50, .5, 1.5)))

# a matrix of the cutoffs, specificity, and sensitivity
p1 <- matrix(0, nrow = 19, ncol = 3)
i <- 1

# for each cutoff value, create a 2x2 table and calculate your sens/spec
for (p in seq(min(dat$predict), .95, 0.05)) {
  t1 <- table(dat$predict > p, dat$pred)
  p1[i, ] <- c(p, (t1[2, 2]) / sum(t1[ , 2]), (t1[1, 1]) / sum(t1[ , 1]))
  i <- i + 1
}

# and plot
plot(1 - p1[ , 3], p1[ , 2], type = 'l',
     xlab = '1 - spec', ylab = 'sens', 
     main = 'ROC', cex.main = .8)

There are some packages out there, ROCR is one I have used, but this takes me a couple minutes to program, is very simple to understand, and is in base R.

enter image description here

Upvotes: 1

Christopher Louden
Christopher Louden

Reputation: 7592

You can use the ROCR package like this:

## computing a simple ROC curve (x-axis: fpr, y-axis: tpr)
library(ROCR)
pred <- prediction( SASdataset$predictions, SASdataset$labels)
perf <- performance(pred, "tpr", "fpr")
plot(perf)

Upvotes: 1

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