make_prey_part: Make prey partition
In qfasar: Quantitative Fatty Acid Signature Analysis in R

Description Usage Arguments Value Details Examples

The function make_prey_part partitions a prey library into clusters based on user specifications informed by the results of a call to the function dimac.

1	make_prey_part(sig, clust, n_clust)

`sig`	A matrix of scaled signatures ready for analysis, intended to be the object `sig_scale` returned by the function `prep_sig`.
`clust`	A data frame containing cluster definitions, intended to be the object `clust` returned by the function `dimac`.
`n_clust`	An integer vector constructed by the user to specify the number of clusters into which each prey type should be partitioned.

A list containing the following elements:

type: A character vector of the partitioned type of each signature.
id: A character vector of the unique sample ID of each signature.
n_types: The number of unique types in the partitioned library.
uniq_types: A character vector of the unique types, sorted alphanumerically.
type_ss: The number of signatures for each unique type.
loc: A vector or matrix giving the first and last locations of the signatures of each type, after being sorted by type and id.
sig_part: A matrix of partitioned signatures ready for analysis, sorted by type and id, in column-major format.
pool_pre: A matrix to pre-multiply diet estimates associated with a partitioned library to pool estimates back to the original prey types.
pool_post: A matrix to post-multiply diet estimates associated with a partitioned library to pool estimates back to the original prey types.
err_code: An integer error code (0 if no error is detected).
err_message: A string contains a brief summary of the execution.

The function make_prey_part partitions a matrix of prey signatures and into a larger number of prey types based on user input (in the vector n_clust) informed by the results of a preceding call to the clustering function dimac. The signatures in sig are presumed to be ready for analysis, which is best accomplished by a call to the function prep_sig.

For each prey type, the column in clust designated by the corresponding integer in n_clust is accessed and used to partition the prey type. For example, if the element of n_clust is 1, the first column of clust is accessed and the prey type is not partitioned. If the element of n_clust is 3, the third column of clust is accessed and the prey type is partitioned into three clusters.

The length of the integer vector n_clust must equal the number of unique types in type. The integers themselves should be between 1 and the number of signatures of each type.

After all prey types have been partitioned, the prey signatures are sorted by type and id. The matrix rep_grp is created to map the new prey types to the original prey types. Multiplying diet estimates corresponding to a partitioned prey library sig_part by rep_grp pools the diet estimates into the original prey types.

Please refer to the vignette and documentation for the functions dimac and prep_sig for additional information.

make_prey_part(sig = matrix(c(0.01, 0.20, 0.30, 0.49,
                              0.05, 0.14, 0.39, 0.42,
                              0.07, 0.21, 0.28, 0.44,
                              0.04, 0.19, 0.34, 0.43,
                              0.12, 0.29, 0.39, 0.20,
                              0.15, 0.28, 0.34, 0.23,
                              0.17, 0.21, 0.31, 0.31,
                              0.18, 0.22, 0.28, 0.32), ncol = 8),
               data.frame(type = c("prey_1", "prey_1", "prey_1", "prey_2",
                                   "prey_2", "prey_2", "prey_2", "prey_2"),
                          id = c("1-1", "1-2", "1-3", "2-1",
                                 "2-2", "2-3", "2-4", "2-5"),
                          clust_1 = c(1, 1, 1, 1, 1, 1, 1, 1),
                          clust_2 = c(1, 2, 1, 2, 1, 1, 2, 2),
                          clust_3 = c(1, 2, 3, 3, 1, 2, 3, 3),
                          clust_4 = c(0, 0, 0, 4, 1, 2, 3, 4)),
               n_clust = c(1, 2))