VARSEDIM: Variable selection to discriminate many taxonomic groups

In VARSEDIG: An Algorithm for Morphometric Characters Selection and Statistical Validation in Morphological Taxonomy

Variable selection to discriminate many taxonomic groups

Description

This function performs an algorithm for morphometric characters selection and statistical validation in morphological taxonomy among many taxonomic groups.

Usage

VARSEDIM(data, variables, group, method="overlap", stepwise=TRUE,
VARSEDIG=TRUE, minimum=TRUE, kernel="gaussian", cor=TRUE, ellipse=FALSE,
convex=TRUE, file="Plots VARSEDIG.pdf", na="NA", dec=",", row.names=FALSE)

Arguments

data	Data file.
variables	Variables to be selected.
group	Variable with the groups to be discriminated.
method	Three different methods for prioritizing the variables according to their capacity for discrimination can be used. If the method is "overlap", a density curve is obtained for each variable and the overlap of the area under the curve between the two groups of the variable group is estimated for all variables. Those variables with lower overlap should have better discrimination capacities and, hence, all variables are ordered from lowest to highest overlap; in other words from the highest to lowest discrimination capacity. If the method is "Monte-Carlo", a Monte-Carlo test is performed comparing all values of group 1 with group 2, and all values of group 2 with 1. The variables are prioritized from the variable with the lowest mean of all p-values (highest discrimination capacity) to the variable with the highest mean of all p-values (lowest discrimination capacity). If the method is "logistic regression", then a binomial logistic regression is calculated and if the argument stepwise=TRUE (default option), then only significant variables are selected for further analyses with the regression performed by steps using the Akaike Information Criterion (AIC).
stepwise	If TRUE, the logistic regression is applied by steps, in order to eliminate those variables that are not significant. The Akaike information criterion (AIC) is used to define what are the variables that are excluded.
VARSEDIG	If it is TRUE, the variables are added for the estimation of polar coordinates in the priority order according to the method "overlap", "Monte-Carlo", or "logistic regression" and the variable is selected if it significantly contributes to discriminate between both groups. See details section for further information.
minimum	If it is TRUE, the algorithm is designed to find a significant discrimination between both groups with the minimum possible number of significant variables. Therefore, only the variables with higher discrimination capacity are selected. If it is FALSE, the algorithm selects all significant variables,and not only those with higher discrimination capacity. This argument is only valid with the methods "Monte-Carlo" and "overlap" and it is useful in those cases that discrimination between the groups is difficult and requires to include as many as variables as possible.
kernel	A character string giving the smoothing kernel to be used for estimating the overlap of the area under the curve between groups. This must be one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine". For further details about the estimation of the density curve see the details section of the function density of base stats package.
cor	If it is TRUE the variables are ordered according to the correlation between them when estimating the polar coordinates. Therefore, the next variable to another variable is the one that has a greater positive correlation.
ellipse	If it is TRUE the ellipses with the levels of significance to the 0.5 (inner ellipse) and 0.95 (outer ellipse) of each category of the variable group is depicted. These levels of significance can be modified by entering the function scatterplot using the argument SCATTERPLOT and modifying the argument levels=c(0.5,0.95).
convex	If it is TRUE the convex hull is depicted for each category.
file	PDF FILE. Filename with the plots of the function VARSEDIG.
na	CSV FILE. Text that is used in the cells without data.
dec	CSV FILE. It defines if the comma "," is used as decimal separator or the dot ".".
row.names	CSV FILE. Logical value that defines if identifiers are put in rows or a vector with a text for each of the rows.

Details

The difference with the function VARSEDIG is that all the different taxa of the variable group are compared with each other, instead of just comparing two taxa. It uses the same algorithm described in the function VARSEDIG.

Value

It is obtained a PDF file with the plots of the function VARSEDIG.

Author(s)

Cástor Guisande González, Universidad de Vigo, Spain.

Examples

## Not run: 
data(characiformes)
VARSEDIM(data=characiformes, variables= c("M2", "M3", "M4",  "M5", "M6",
"M7", "M8", "M9", "M10", "M11", "M12", "M13", "M14", "M15", "M16", "M17",
"M18", "M19", "M20", "M21", "M22", "M23", "M24", "M25", "M26", "M27", "M28"),
group="Genus")

## End(Not run)

VARSEDIG documentation built on April 1, 2022, 5:06 p.m.