SensitivityToNormMixedMod: Fits a mixed effects model to account for normalization...
In sevvandi/outselect: Algorithm selection for unsupervised outlier detection
Description
Usage
Arguments
Value
Examples
Description
Fits a mixed effects model to account for normalization methods and outlier methods accounting for the dataset variants
Usage
1
SensitivityToNormMixedMod(rocpr = 1, ds = FALSE, pp = FALSE)
Arguments
rocpr
If rocpr=1 then area under ROC curve is used as the performance measure. If rocpr=2 area under PR curve is used.
Value
A list containing the following:
- fit1
The first model without interactions between normalization and outlier method.
- fit2
The second model with interactions between normalization and outlier method.
- aov
The output of an ANOVA test.
Examples
1
2
out <- SensitivityToNormMixedMod(1)
out
sevvandi/outselect documentation built on June 1, 2019, 3:58 a.m.
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Description Usage Arguments Value Examples
Description
Fits a mixed effects model to account for normalization methods and outlier methods accounting for the dataset variants
Usage
1 | SensitivityToNormMixedMod(rocpr = 1, ds = FALSE, pp = FALSE)
|
Arguments
rocpr |
If |
Value
A list containing the following:
- fit1
The first model without interactions between normalization and outlier method.
- fit2
The second model with interactions between normalization and outlier method.
- aov
The output of an ANOVA test.
Examples
1 2 | out <- SensitivityToNormMixedMod(1)
out
|
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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