Solved – How to calculate the AUC of combined variables using SPSS

auclogisticregressionrocspss

thank you for taking time out to read this.

I have previously ran ROC curves to get the AUCs for single test variables but I do not know how to derive the AUC for combined variables (2 test variables instead of just 1).
I read that I can use logistic regression to get the AUC but I have no clue on how to do it and how to interpret the output.
The system I'm using is SPSS version 22.

Please help.
Thank you

Best Answer

I was advised that I can run a binary logistic regression to get the probability and then run a ROC curve using the probability as the test variable.

The whole process goes like this,

1) Analyse 2) Regression 3) Binary logistic, put in the state variable as the dependent variable, subsequently enter the variables you wish to combine into the covariates, then click on "save" and check the box "probabilities". This process will compute a new variable in your SPSS database, termed "PRE_1". 4) Run the ROC curve using "PRE_1" as test variable. 5) Obtain the result.

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Solved – Calculate LOO-AUC values using glmnet

number of folds - default is 10. Although nfolds can be as large as the sample
size (leave-one-out CV), it is not recommended for large datasets. Smallest
value allowable is nfolds=3

From the package documenation it appears that you indeed can set nfolds equal to the sample size to perform leave-one-out CV.

However, the problem you are facing - as the error message indicates, is that, in order to calculate the AUC ( which really needs a way to rank your test cases) glmnet needs at least 10 obs.

Think about - if no. of test cases is only 1 how are you supposed to rank just one case?

This is only an issue because of the performance measure (auc) you have chosen. Other measures which do not require ranking i.e., those that can be sufficiently calculated using just on one test case ex: Mean squared error will not give you such an error you see.

Solved – How to calculate AUC for any correlation method

The paper does not use AUC at all. And rightly so. The Area Under the (receiver operator characteristic) Curve (AUC) is a measure of binary classification accuracy. The paper is focused on continuous outcomes. Mainly gene expression.

In a contrived sense, the AUC is equivalent to another association measure: the rank based U-statistic. This measures correlation in a probabilistic sense; it is for any two randomly sampled observations, $(X_1, Y_1)$ and $(X_2, Y_2)$ the probability $P(X_2 > X_1 | Y_2 > Y_1)$. However, the U-statistic is a weak measure of correlation at best. It's not really appropriate for genomics where actually modeling the exact functional form of the relationship is often more important.

Best Answer

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