optimal: Searching for optimal traits
In vanderleidebastiani/SYNCSA: Analysis of Functional and Phylogenetic Patterns in Metacommunities

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Maximize trait-convergence assembly patterns (TCAP = roTE), trait-divergence assembly patterns (TDAP = roXE.T), maximize both trait-divergence assembly patterns and trait-convergence assembly patterns (TCAP.TDAP = roXE) or alpha divergence (roRE) For more details, see syncsa.

optimal(
  comm,
  traits = NULL,
  envir = NULL,
  checkdata = TRUE,
  subset.min = 1,
  subset.max = ncol(traits),
  pattern = NULL,
  ro.method = "mantel",
  dist = "euclidean",
  method = "pearson",
  scale = TRUE,
  scale.envir = TRUE,
  ranks = TRUE,
  asym.bin = NULL,
  ord = "metric",
  put.together = NULL,
  na.rm = FALSE,
  notification = TRUE,
  progressbar = FALSE
)

## S3 method for class 'optimal'
print(x, ...)

`comm`	Community data, with species as columns and sampling units as rows. This matrix can contain either presence/absence or abundance data. Alternatively comm can be an object of class metacommunity.data, an alternative way to set all data.frames/matrices. When you use the class metacommunity.data the arguments traits, envir and put.together must be null. See details.
`traits`	Matrix data of species described by traits, with traits as columns and species as rows (Default traits = NULL).
`envir`	Environmental variables for each community, with variables as columns and sampling units as rows (Default envir = NULL).
`checkdata`	Logical argument (TRUE or FALSE) to check if species sequence in the community data follows the same order as the one in the trait and if sampling units in the community data follows the same order as the one in the environmental matrices (Default checkdata = TRUE).
`subset.min`	Minimum of traits in each subset (Default subset.min = 1).
`subset.max`	Maximum of traits in each subset (Default subset.max = ncol(traits)).
`pattern`	Patterns for maximize correlation, "tcap", "tdap", "tcap.tdap" or "rao" (Default pattern = NULL).
`ro.method`	Method to obtain the correlation, "mantel" or "procrustes" (Default ro.method = "mantel").
`dist`	Dissimilarity index, as accepted by vegdist: "manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup" , "binomial" or "chao".
`method`	Correlation method, as accepted by cor: "pearson", "spearman" or "kendall".
`scale`	Logical argument (TRUE or FALSE) to specify if the traits are measured on different scales (Default Scale = TRUE). When scale = TRUE traits are measured on different scales and the matrix T is subjected to standardization within each trait. When scale = FALSE if traits are measured on the same scale and the matrix T is not subjected to standardization. Furthermore, if scale = TRUE the matrix of traits is subjected to standardization within each trait, and Gower Index is used to calculate the degree of belonging to the species, and if scale = FALSE the matrix of traits is not subjected to standardization, and Euclidean distance is calculated to determine the degree of belonging to the species.
`scale.envir`	Logical argument (TRUE or FALSE) to specify if the environmental variables are measured on different scales (Default scale = TRUE). If the enviromental variables are measured on different scales, the matrix is subjected to centralization and standardization within each variable.
`ranks`	Logical argument (TRUE or FALSE) to specify if ordinal variables are convert to ranks (Default ranks = TRUE).
`asym.bin`	Vector listing the asymmetric binary traits, see `gowdis` (Default asym.bin = NULL).
`ord`	Method to be used for ordinal traits, see `gowdis` (Default ord = "metric").
`put.together`	List to specify group traits that are added or removed together (Default put.together = NULL). This argument must be a list, see examples.
`na.rm`	Logical argument (TRUE or FALSE) to specify if pairwise deletion of missing observations when computing dissimilarities (Default na.rm = FALSE).
`notification`	Logical argument (TRUE or FALSE) to specify if notifications of missing observations are shown (Default notification = TRUE).
`progressbar`	Logical argument (TRUE or FALSE) to specify if display a progress bar on the R console (Default progressbar = FALSE).
`x`	An object of class optimal.
`...`	Other parameters for the respective functions.

Package SYNCSA requires that the species and community sequence in the data.frame or matrix must be the same for all dataframe/matrices. The function organize.syncsa organizes the data for the functions of the package, placing the matrices of community, traits and environmental varibles in the same order. The function use of function organize.syncsa is not requered for run the functions, but is recommended. In this way the arguments comm, traits, envir, as well as the argument put.together, can be specified them as normal arguments or by passing them with the object returned by the function organize.syncsa using, in this case only the argument comm. Using the object returned by organize.syncsa, the comm argument is used as an alternative way of entering to set all data.frames/matrices, and therefore the other arguments (traits, envir, and put.together) must be null.

`Subset`	Subset of traits that maximizes the correlation.
`ro`	Correlation for the subset of traits.

IMPORTANT: The sequence species show up in community data matrix MUST be the same as they show up in traits matrix. See details and organize.syncsa.

Vanderlei Julio Debastiani <vanderleidebastiani@yahoo.com.br>

Pillar, V.D.; Duarte, L.d.S. (2010). A framework for metacommunity analysis of phylogenetic structure. Ecology Letters, 13, 587-596.

Pillar, V.D., Duarte, L.d.S., Sosinski, E.E. & Joner, F. (2009). Discriminating trait-convergence and trait-divergence assembly patterns in ecological community gradients. Journal of Vegetation Science, 20, 334:348.

syncsa, organize.syncsa

data(flona)
optimal(flona$community, flona$traits, flona$environment, subset.min = 3,
   subset.max = 5, pattern = "tcap")
optimal(flona$community, flona$traits, flona$environment, subset.min = 3,
   subset.max = 5, pattern = "tdap")
optimal(flona$community, flona$traits, flona$environment, subset.min = 3,
   subset.max = 5, pattern = "tcap.tdap")
put.together <- list(c("fol", "sem"), c("tam", "red"))
put.together
optimal(flona$community, flona$traits, flona$environment, subset.min = 1,
   subset.max = 3, pattern = "tcap", put.together = put.together)