lambda.model: Compute the expected information for discrimination (expected...
In pmckeigue/wevid: Weight of evidence for quantifying performance of a binary classifier
Description
Usage
Arguments
Value
Examples
Description
Compute the expected information for discrimination (expected weight of evidence)
from the model-based densities
Usage
1
lambda.model(densities)
Arguments
densities
Adjusted densities computed by
Wdensities.fromraw.
Value
The model-based expected information for discrimination.
Examples
1
2
3
4
5
data("cleveland") # load example dataset
W <- with(cleveland, weightsofevidence(posterior.p, prior.p))
densities.unadj <- Wdensities.unadjusted(cleveland$y, W)
densities.adj <- Wdensities.fromraw(densities.unadj)
lambda.model(densities.adj)
pmckeigue/wevid documentation built on May 29, 2019, 5:41 a.m.
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Description Usage Arguments Value Examples
Description
Compute the expected information for discrimination (expected weight of evidence) from the model-based densities
Usage
1 | lambda.model(densities)
|
Arguments
densities |
Adjusted densities computed by
|
Value
The model-based expected information for discrimination.
Examples
1 2 3 4 5 | data("cleveland") # load example dataset
W <- with(cleveland, weightsofevidence(posterior.p, prior.p))
densities.unadj <- Wdensities.unadjusted(cleveland$y, W)
densities.adj <- Wdensities.fromraw(densities.unadj)
lambda.model(densities.adj)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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