# Thread: Scoring Dataset (easy question)

1. ## Scoring Dataset (easy question)

I have a dataset "Xs" and also some model coefficients. I would like to score the dataset using the coefficients. The dataset has 7 items and there are 8 coefficients counting the intercept. What is the best way to perform this task.

Dataset: Xs [602,7]

Coefficients listed below:
B0 = -0.6341
B1 = 1.968
B2 = 1.919
B3 = 0.142
B4 = -0.052
B5 = 0.300
B6 = 0.010
B7 = 0.013

So, I would like a vector from something like this:

log_odds = Bo + B1(X1) + B2(X2),...,+ B7(X7).

After I have that vector I am going to calculate predicted probabilities from it, then calculate Sensitivities, 1 - Specificities, and then the AUC and plot curve using the Y vector I have.

2. ## Re: Scoring Dataset (easy question)

There's probably a more elegant way, but since you only have 7 predictors, it's not too bad to write it out manually (also helps confirm explicitly it's doing what you want)

Code:
``````#Fake Coefficients
coef = data.frame(b0=-1,b1=2,b2=3)

#Fake Data
datas = data.frame(x1=c(1,2,3,4,5),x2=c(5,4,3,2,1))

#Scored Dataset
score = data.frame(score=(coef[,1]) + (coef[,2]*datas[,1]) + (coef[,3]*datas[,2]))

> coef
b0 b1 b2
1 -1  2  3
> datas
x1 x2
1  1  5
2  2  4
3  3  3
4  4  2
5  5  1
> score
score
1    16
2    15
3    14
4    13
5    12``````

3. ## The Following User Says Thank You to jamesmartinn For This Useful Post:

hlsmith (08-09-2017)

4. ## Re: Scoring Dataset (easy question)

Thanks JM.

Leaving for the day, but I added the following to get the ROC curve, will update more tomorrow.

Code:
``````odds <- exp(score)
prob <- odds / (1 + odds)
prob
install.packages("ROCR")
install.packages("pROC")
require(ROCR)
require(pROC)
predob = prediction(prob, Ys)
perf = performance(predob, "tpr", "fpr")
plot(perf)``````

5. ## The Following User Says Thank You to hlsmith For This Useful Post:

jamesmartinn (08-10-2017)

6. ## Re: Scoring Dataset (easy question)

I am sure I will clean this up at some point, but the following is my current code to plot an ROC curve based on a binomial model's coefficients. I am planning in the future to compare curves for multiple modeling approaches in the same graph, but I don't currently have time to play around with it. I will update this thread if I remember and make any progress.

Code:
``````odds <- exp(score)
prob <- odds / (1 + odds)
prob
install.packages("ROCR")
install.packages("pROC")
require(ROCR)
require(pROC)
rocplot <- function(prob, Ys) {
predob = prediction(prob, Ys)
perf = performance(predob, "tpr", "fpr")
plot(perf)
area <- auc(Ys, prob\$score)
area <- format(round(area, 4), nsmall = 4)
text(x=0.8, y=0.1, labels = paste("AUC =", area))
# the reference x=y line
segments(x0=0, y0=0, x1=1, y1=1, col="gray", lty=2)
}``````

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