make.wfm {LoopAnalyst}

Compute the Weighted Feedback Matrix

Description

Validates a community matrix and computes its associated weighted feedback matrix.

Usage

make.wfm(CM, status=FALSE, digits=1, sign=FALSE)

Arguments

CM

A valid community matrix.

status

Switches on an element-by-element progress indicator when set to TRUE. Set to FALSE by default.

digits

Indicates precision for elements in the weighted feedback matrix. By default, this is set to 1 significant digit.

sign

Switch to provide output as the signed value of the adjoint matrix elements divided by the absolute feedback matrix elements. The default value is FALSE

Details

The supplied matrix is validated as a community matrix, and the weighted feedback matrix is computed. Each element is equal to the absolute value of the corresponding element of the adjoint of the negative community matrix divided by the corresponding element of the total feedback matrix T. Resulting values range from 0 to 1.0, with values of magnitude of 0.5 or greater indicating that positive or negative feedback is expected to dominate (as per the sign of the adjoint value). Values of 1 indicate unambiguous effects of feedback, regardless of the quantitative magnitude of the system's linkages. Unresolvably ambiguous effects are represented by values between 0 and 0.5. The sign implementation differs from Dambacher's.
NOTE: weighted feedback, adjoint and absolute feeback matrices are transposed relative to the community effect matrix.

Value

The weighted feedback matrix for a community matrix

Author(s)

Alexis Dinno ([e-mail])

Please contact me with any questions, bug reports or suggestions for improvement. Fixing bugs will be facilitated by sending along:

[1]	a copy of your relevant R data file (de-labeled or anonymized is fine),
[2]	a copy of the command syntax used, and
[3]	a copy of the exact output of the command.

https://alexisdinno.com/LoopAnalyst/

References

Dambacher, J. M. and Li, H. W. and Rossignol, P. A. (2002) Relevance of community structure in assessing indeterminacy of ecological predictions. Ecology, 83(5),1372–1385. <doi:10.2307/3071950>.

Dambacher, J. M., et al. (2003) Qualitative stability and ambiguity in model ecosystems. The American Naturalist, 161(6),876–888. <doi:10.1086/367590>.

See Also

make.cem, make.adjoint, make.T, weighted.predictions.

Examples

## compute weighted feedback matrix
data(cm.dambacher)
make.wfm(cm.dambacher)