Area Under the Curve - Variable and Log Transformed VariableHow to calculate sample size for comparing the area under the curve of two models?pattern of ROC curve and choice of AUCArea Under Curve ROC penalizes somehow models with too many explanatory variables?Area Under the ROC Curve, a simple questionArea Under the ROC Curve: Comparing identification performance between two values of the same variabletwo questions; how to interpret the AUROC (area under the ROC curve)Area Under the Curve physical meaningArea Under The Receiver Operating - incompatible explanationsQ: Possible to optimize for area under the precision-recall curve in glmnet logistic regression?How to distinguish overfitting and underfitting from the ROC AUC curve?
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Area Under the Curve - Variable and Log Transformed Variable
How to calculate sample size for comparing the area under the curve of two models?pattern of ROC curve and choice of AUCArea Under Curve ROC penalizes somehow models with too many explanatory variables?Area Under the ROC Curve, a simple questionArea Under the ROC Curve: Comparing identification performance between two values of the same variabletwo questions; how to interpret the AUROC (area under the ROC curve)Area Under the Curve physical meaningArea Under The Receiver Operating - incompatible explanationsQ: Possible to optimize for area under the precision-recall curve in glmnet logistic regression?How to distinguish overfitting and underfitting from the ROC AUC curve?
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I have a situation where I am fitting two simple logistic regression models - one model with the variable of interest included as the only predictor, and the other model with the log of the variable of interest included as the only predictor. Both models have the same Area Under the Curve, and I would like to know how to explain why this occurs. I am sure this is not due to chance, but rather has something to do with how AUC is calculated, and it's interpretation.
logistic roc auc
asked 9 hours ago
APKAPK
1278
$endgroup$
add a comment |
$begingroup$
I have a situation where I am fitting two simple logistic regression models - one model with the variable of interest included as the only predictor, and the other model with the log of the variable of interest included as the only predictor. Both models have the same Area Under the Curve, and I would like to know how to explain why this occurs. I am sure this is not due to chance, but rather has something to do with how AUC is calculated, and it's interpretation.
logistic roc auc
asked 9 hours ago
APKAPK
1278
$endgroup$
add a comment |
$begingroup$
I have a situation where I am fitting two simple logistic regression models - one model with the variable of interest included as the only predictor, and the other model with the log of the variable of interest included as the only predictor. Both models have the same Area Under the Curve, and I would like to know how to explain why this occurs. I am sure this is not due to chance, but rather has something to do with how AUC is calculated, and it's interpretation.
logistic roc auc
asked 9 hours ago
APKAPK
1278
$endgroup$
I have a situation where I am fitting two simple logistic regression models - one model with the variable of interest included as the only predictor, and the other model with the log of the variable of interest included as the only predictor. Both models have the same Area Under the Curve, and I would like to know how to explain why this occurs. I am sure this is not due to chance, but rather has something to do with how AUC is calculated, and it's interpretation.
logistic roc auc
logistic roc auc
asked 9 hours ago
APKAPK
1278
asked 9 hours ago
APKAPK
1278
edited 9 hours ago
APK
asked 9 hours ago
APKAPK
1278
asked 9 hours ago
APKAPK
1278
asked 9 hours ago
APKAPK
1278
1278
add a comment |
add a comment |
1 Answer
1
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oldest
votes
$begingroup$
It is because the AUC is invariant to monotonic changes of variable, of which the log-transform is a special case. The AUC is the probability that a randomly selected case has a higher risk than a control. While the raw difference in risk may not be the same for those two models, the case will still have a higher risk when calculated using either the log-transformed predictor or the untransformed predictor.
It should give us some pause and doubt about AUC that it makes no use whatsoever of the actual risk predicted by the model, but rather the ordering of groups according to a predicted risk (be it arbitrary or otherwise). The axes on a ROC are just sensitivity and 1-specificity.
answered 9 hours ago
AdamOAdamO
33.8k263140
$endgroup$
$begingroup$
Thanks AdamO. Great explanation.
$endgroup$
– APK
8 hours ago
add a comment |
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$begingroup$
It is because the AUC is invariant to monotonic changes of variable, of which the log-transform is a special case. The AUC is the probability that a randomly selected case has a higher risk than a control. While the raw difference in risk may not be the same for those two models, the case will still have a higher risk when calculated using either the log-transformed predictor or the untransformed predictor.
It should give us some pause and doubt about AUC that it makes no use whatsoever of the actual risk predicted by the model, but rather the ordering of groups according to a predicted risk (be it arbitrary or otherwise). The axes on a ROC are just sensitivity and 1-specificity.
answered 9 hours ago
AdamOAdamO
33.8k263140
$endgroup$
$begingroup$
Thanks AdamO. Great explanation.
$endgroup$
– APK
8 hours ago
add a comment |
$begingroup$
It is because the AUC is invariant to monotonic changes of variable, of which the log-transform is a special case. The AUC is the probability that a randomly selected case has a higher risk than a control. While the raw difference in risk may not be the same for those two models, the case will still have a higher risk when calculated using either the log-transformed predictor or the untransformed predictor.
It should give us some pause and doubt about AUC that it makes no use whatsoever of the actual risk predicted by the model, but rather the ordering of groups according to a predicted risk (be it arbitrary or otherwise). The axes on a ROC are just sensitivity and 1-specificity.
answered 9 hours ago
AdamOAdamO
33.8k263140
$endgroup$
$begingroup$
Thanks AdamO. Great explanation.
$endgroup$
– APK
8 hours ago
add a comment |
$begingroup$
It is because the AUC is invariant to monotonic changes of variable, of which the log-transform is a special case. The AUC is the probability that a randomly selected case has a higher risk than a control. While the raw difference in risk may not be the same for those two models, the case will still have a higher risk when calculated using either the log-transformed predictor or the untransformed predictor.
It should give us some pause and doubt about AUC that it makes no use whatsoever of the actual risk predicted by the model, but rather the ordering of groups according to a predicted risk (be it arbitrary or otherwise). The axes on a ROC are just sensitivity and 1-specificity.
answered 9 hours ago
AdamOAdamO
33.8k263140
$endgroup$
It is because the AUC is invariant to monotonic changes of variable, of which the log-transform is a special case. The AUC is the probability that a randomly selected case has a higher risk than a control. While the raw difference in risk may not be the same for those two models, the case will still have a higher risk when calculated using either the log-transformed predictor or the untransformed predictor.
It should give us some pause and doubt about AUC that it makes no use whatsoever of the actual risk predicted by the model, but rather the ordering of groups according to a predicted risk (be it arbitrary or otherwise). The axes on a ROC are just sensitivity and 1-specificity.
answered 9 hours ago
AdamOAdamO
33.8k263140
answered 9 hours ago
AdamOAdamO
33.8k263140
answered 9 hours ago
AdamOAdamO
33.8k263140
answered 9 hours ago
AdamOAdamO
33.8k263140
33.8k263140
$begingroup$
Thanks AdamO. Great explanation.
$endgroup$
– APK
8 hours ago
add a comment |
$begingroup$
Thanks AdamO. Great explanation.
$endgroup$
– APK
8 hours ago
$begingroup$
Thanks AdamO. Great explanation.
$endgroup$
– APK
8 hours ago
$begingroup$
Thanks AdamO. Great explanation.
$endgroup$
– APK
8 hours ago
add a comment |
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