Unfolding {MultBiplotR}

Multidimensional Unfolding

Description

Multidimensional Unfolding with some adaptations for vegetation analysis

Usage

Unfolding(A, ENV = NULL, TransAbund = "Gaussian Columns", offset = 0.5, 
weight = "All_1", Constrained = FALSE, 
TransEnv = "Standardize columns", 
InitConfig = "SVD", model = "Ratio", 
condition = "Columns", Algorithm = "SMACOF", 
OptimMethod = "CG", r = 2, maxiter = 100, 
tolerance = 1e-05, lambda = 1, omega = 0, plot = FALSE)

Arguments

A

The original proximities matrix

ENV

The matrix of environmental variables

TransAbund

Initial transformation of the abundances : "None", "Gaussian", "Column Percent", "Gaussian Columns", "Inverse Square Root", "Divide by Column Maximum")

offset

offset is the quantity added to the zeros of the table

weight

A matrix of weights for each cell of the table

Constrained

Should fit a constrained analysis

TransEnv

Transformation of the environmental variables

InitConfig

Init configuration for the algorithm

model

Type of model to be fitted: "Identity", "Ratio", "Interval" or "Ordinal".

condition

"Matrix", "Columns" to condition to the whole matrix or to each column

Algorithm

Algorithm to fit the model: "SMACOF", "GD", "Genefold"

OptimMethod

Optimization method for gradient descent

r

Dimension of the solution

maxiter

Maximum number of iterations in the algorithm

tolerance

Tolerace for the algorithm

lambda

First penalization parameter

omega

Second penalization parameter

plot

Should the results be plotted?

Details

ological data

Value

An object of class "Unfolding"

Author(s)

Jose Luis Vicente Villardon

References

Ver Articulos

Examples

unf=Unfolding(SpidersSp, ENV=SpidersEnv, model="Ratio", Constrained = FALSE, condition="Matrix")
plot(unf, PlotTol=TRUE, PlotEnv = FALSE)
plot(unf, PlotTol=TRUE, PlotEnv = TRUE)
cbind(unf$QualityVars, unf$Var_Fit)
unf2=Unfolding(SpidersSp, ENV=SpidersEnv, model="Ratio", Constrained = TRUE, condition="Matrix")
plot(unf2, PlotTol=FALSE, PlotEnv = TRUE, mode="s")
cbind(unf2$QualityVars, unf2$Var_Fit)