LoopAnalyst (version 1.2-7)

weighted.predictions: Compute the Matrix of Weighted Predictions

Description

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

Usage

weighted.predictions(CM, status=FALSE)

Value

The weighted prediction matrix for a community matrix

Arguments

CM

A valid community matrix.

status

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

Author

Alexis Dinno ()

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/

Details

The supplied matrix is validated as a community matrix, and a weighted predictions matrix is computed. This matrix is equivalent to the transposed community effect matrix with some ambiguous elements resolved using the value of the corresponding feedback matrix. Such values are represented enclosed in parentheses. In keeping with the paper by Levins, Dambacher and Rossignol (expression 42 in the paper cited below), the matrix orientation is congruent with the weighted feedback matrix, and transposed to the community effect matrix.

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. <tools:::Rd_expr_doi("10.2307/3071950")>.

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

See Also

make.cem, make.wfm, make.adjoint, and make.T.

Examples

## compute community effect matrix, and note high prevalence of ambiguous predictions
data(cm.dambacher)
make.cem(cm.dambacher, out=TRUE)
## compute weighted prediction matrix, and note disambiguation of the cem
weighted.predictions(t(cm.dambacher))