Multidimensional Unfolding with some adaptations for vegetation analysis
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)An object of class "Unfolding"
The original proximities matrix
The matrix of environmental variables
Initial transformation of the abundances : "None", "Gaussian", "Column Percent", "Gaussian Columns", "Inverse Square Root", "Divide by Column Maximum")
offset is the quantity added to the zeros of the table
A matrix of weights for each cell of the table
Should fit a constrained analysis
Transformation of the environmental variables
Init configuration for the algorithm
Type of model to be fitted: "Identity", "Ratio", "Interval" or "Ordinal".
"Matrix", "Columns" to condition to the whole matrix or to each column
Algorithm to fit the model: "SMACOF", "GD", "Genefold"
Optimization method for gradient descent
Dimension of the solution
Maximum number of iterations in the algorithm
Tolerace for the algorithm
First penalization parameter
Second penalization parameter
Should the results be plotted?
Jose Luis Vicente Villardon
ological data
Ver Articulos
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)
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