measure_icc: Measure the information loss of reduction using intraclass...
In USCbiostats/partition: Agglomerative Partitioning Framework for Dimension Reduction
measure_icc R Documentation
Measure the information loss of reduction using intraclass correlation
coefficient
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_icc() assesses information loss by calculating the
intraclass correlation coefficient for the target variables.
Usage
measure_icc(.partition_step)
Arguments
.partition_step
a partition_step object
Value
a partition_step object
See Also
Other metrics:
as_measure(),
measure_min_icc(),
measure_min_r2(),
measure_std_mutualinfo(),
measure_variance_explained()
USCbiostats/partition documentation built on Nov. 11, 2024, 4:25 p.m.
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| measure_icc | R Documentation |
Measure the information loss of reduction using intraclass correlation coefficient
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_icc() assesses information loss by calculating the
intraclass correlation coefficient for the target variables.
Usage
measure_icc(.partition_step)
Arguments
.partition_step |
a |
Value
a partition_step object
See Also
Other metrics:
as_measure(),
measure_min_icc(),
measure_min_r2(),
measure_std_mutualinfo(),
measure_variance_explained()
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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