lopo_pool {qfasar}

Pool lopo results to original prey types

Description

If lopo is used to perform a leave-one-prey-out analysis with a partitioned prey library (make_prey_part), lopo_pool pools the partitioned results back to the original unpartitioned prey types.

Usage

lopo_pool(est, n_conv, type_ss, pre, post)

Arguments

est

The estimation matrix of a leave-one-prey-out analysis performed by the function lopo, returned as the est object.

n_conv

An integer vector denoting the number of signature estimates in the partitioned prey types that converged, returned by a call to lopo as the n_conv object.

type_ss

An integer vector with the number of signatures (sample size) in each of the partitioned prey types, returned by a call to make_prey_part as the type_ss object.

pre

The pre-multiplication matrix returned by a call to make_prey_part as the pool_pre object.

post

The post-multiplication matrix returned by a call to make_prey_part as the pool_post object.

Value

A list containing the following elements, all of which are organized on the basis of the original unpartitioned prey types:

est

A square matrix containing the mean distribution of leave-one-prey-out estimates among all prey types.

mean_correct

The mean proportion correctly estimated across prey types, unweighted by prey-type sample sizes.

total_correct

The proportion of all signatures correctly estimated.

n_conv

An integer vector containing the number of estimates that converged.

err_code

An integer error code (0 if no error is detected).

err_message

A string containing a brief summary of the results.

Details

The statistics computed by lopo and lopo_pool are one measure of the distinctiveness of prey types within a prey library. However, it is important to be aware that such statistics are not necessarily informative of the ability of QFASA to accurately estimate predator diets, as Bromaghin et al. (2015, 2016a, 2016b) found that QFASA performance depends strongly on the interaction between characteristics of a prey library, the specific diet of a predator, and the accuracy of the calibration coefficients. Consequently, the user is warned not to misinterpret or misrepresent these statistics.

References

Bromaghin, J.F., S.M. Budge, and G.W. Thiemann. 2016b. Should fatty acid signature proportions sum to 1 for diet estimation? Ecological Research 31:597-606.

Bromaghin, J.F., S.M. Budge, G.W. Thiemann, and K.D. Rode. 2016a. Assessing the robustness of quantitative fatty acid signature analysis to assumption violations. Methods in Ecology and Evolution 7:51-59.

Bromaghin, J.F., K.D. Rode, S.M. Budge, and G.W. Thiemann. 2015. Distance measures and optimization spaces in quantitative fatty acid signature analysis. Ecology and Evolution 5:1249-1262.

Examples

lopo_pool(est = matrix(c(0.90, 0.05, 0.30, 0.02,
                         0.04, 0.84, 0.09, 0.03,
                         0.02, 0.06, 0.35, 0.57,
                         0.05, 0.10, 0.15, 0.70), nrow = 4, byrow = TRUE),
          n_conv = c(2, 8, 8, 11),
          type_ss = c(2, 8, 8, 12),
          pre = matrix(c(0.2, 0.8, 0.0, 0.0,
                         0.0, 0.0, 0.4, 0.6), nrow = 2, byrow = TRUE),
          post = matrix(c(1, 1, 0, 0,
                          0, 0, 1, 1), ncol = 2)
          )