measure_variance_explained: Measure the information loss of reduction using the variance...
In partition: Agglomerative Partitioning Framework for Dimension Reduction
Description
Usage
Arguments
Value
See Also
Description
Metrics are functions that tell how much information would be
lost for a given reduction in the data. reduce. as_measure() is a
helper function to create new metrics to be used in partitioners.
partitioners can be created with as_partitioner().
measure_variance_explained() assesses information loss by calculating the
variance explained by the first component of a principal components analysis.
Usage
1
measure_variance_explained(.partition_step)
Arguments
.partition_step
a partition_step object
Value
a partition_step object
See Also
Other metrics:
as_measure(),
measure_icc(),
measure_min_icc(),
measure_min_r2(),
measure_std_mutualinfo()
partition documentation built on Oct. 5, 2021, 9:06 a.m.
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Description Usage Arguments Value See Also
Description
Metrics are functions that tell how much information would be
lost for a given reduction in the data. reduce. as_measure() is a
helper function to create new metrics to be used in partitioners.
partitioners can be created with as_partitioner().
measure_variance_explained() assesses information loss by calculating the
variance explained by the first component of a principal components analysis.
Usage
1 | measure_variance_explained(.partition_step)
|
Arguments
.partition_step |
a |
Value
a partition_step object
See Also
Other metrics:
as_measure(),
measure_icc(),
measure_min_icc(),
measure_min_r2(),
measure_std_mutualinfo()
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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