complexity: Number of combinations at a given complexity layer
In QCA: Qualitative Comparative Analysis
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
This function calculates the number of all possible combinations of conditions
(including all levels for each condition), at a given complexity layer.
Usage
1
complexity(n, layers, noflevels)
Arguments
n
Numeric scalar, the number of input conditions.
layers
Numeric vector, the complexity layer(s) with values from 1 to n.
noflevels
Numeric vector containing the number of levels for each
of the n conditions.
Details
These are the number of combinations which the CCubes algorithm (Dusa, 2018) checks
to determine the prime implicants from a minimization process.
In the bottom-up approach, CCubes first checks for single conditions (combinations of
both presence and absence, or more levels if multi-value), then all possible combinations
of levels for two conditions etc.
The precise equation that partitions the search space into complexity layers is:
∑_{c = 1}^{k} {k\choose c} ∏_{s = 1}^{c} l_s
where l stands for the number of levels for each combination of c
conditions out of k.
Value
A numeric vector.
Author(s)
Adrian Dusa
References
Dusa, A. (2018) “Consistency Cubes: A Fast, Efficient Method for Boolean
Minimization”, R Journal, DOI:
10.32614/RJ-2018-080
Examples
1
2
3
complexity(3) # all layers from 1 to 3
complexity(5, layers = 2)
QCA documentation built on Nov. 29, 2020, 3:01 p.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
This function calculates the number of all possible combinations of conditions (including all levels for each condition), at a given complexity layer.
Usage
1 | complexity(n, layers, noflevels)
|
Arguments
n |
Numeric scalar, the number of input conditions. |
layers |
Numeric vector, the complexity layer(s) with values from 1 to n. |
noflevels |
Numeric vector containing the number of levels for each of the n conditions. |
Details
These are the number of combinations which the CCubes algorithm (Dusa, 2018) checks to determine the prime implicants from a minimization process.
In the bottom-up approach, CCubes first checks for single conditions (combinations of both presence and absence, or more levels if multi-value), then all possible combinations of levels for two conditions etc.
The precise equation that partitions the search space into complexity layers is:
∑_{c = 1}^{k} {k\choose c} ∏_{s = 1}^{c} l_s
where l stands for the number of levels for each combination of c conditions out of k.
Value
A numeric vector.
Author(s)
Adrian Dusa
References
Dusa, A. (2018) “Consistency Cubes: A Fast, Efficient Method for Boolean Minimization”, R Journal, DOI: 10.32614/RJ-2018-080
Examples
1 2 3 | complexity(3) # all layers from 1 to 3
complexity(5, layers = 2)
|
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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