dot-calc_subject_balance_divergence: Characterizes closeness of actual group sizes to what is...
In ldamatch: Selection of Statistically Similar Research Groups
Description
Usage
Arguments
Value
See Also
Description
Characterizes closeness of actual group sizes to what is expected.
Usage
1
.calc_subject_balance_divergence(table_condition, props)
Arguments
table_condition
The number of different condition values,
usually created by calling table(condition).
props
Either the desired proportions (percentage) of the
sample for each condition as a named vector,
or the names of the conditions
for which we prefer to preserve the subjects,
in decreasing order of preference. If not specified, the
(full) sample proportions are used.
This is preferred among configurations with the same
taken into account by the other methods to some extent.
For example, c(A = 0.4, B = 0.4, C = 0.2) means that
we would like the number of subjects in groups A, B, and
C to be around 40%, 40%, and 20% of the total number of
subjects, respectively. Whereas c("A", "B", "C") means
that if possible, we would like to keep all subjects
in group A, and prefer keeping subjects in B, even if
it results in losing more subjects from C.
Value
KL divergence of the actual group size proportions from the expected ones.
See Also
match_groups for meaning of condition parameter.
ldamatch documentation built on May 23, 2021, 5:06 p.m.
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Description Usage Arguments Value See Also
Description
Characterizes closeness of actual group sizes to what is expected.
Usage
1 | .calc_subject_balance_divergence(table_condition, props)
|
Arguments
table_condition |
The number of different condition values, usually created by calling table(condition). |
props |
Either the desired proportions (percentage) of the sample for each condition as a named vector, or the names of the conditions for which we prefer to preserve the subjects, in decreasing order of preference. If not specified, the (full) sample proportions are used. This is preferred among configurations with the same taken into account by the other methods to some extent. For example, c(A = 0.4, B = 0.4, C = 0.2) means that we would like the number of subjects in groups A, B, and C to be around 40%, 40%, and 20% of the total number of subjects, respectively. Whereas c("A", "B", "C") means that if possible, we would like to keep all subjects in group A, and prefer keeping subjects in B, even if it results in losing more subjects from C. |
Value
KL divergence of the actual group size proportions from the expected ones.
See Also
match_groups for meaning of condition parameter.
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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