plot.External.Binary.Logistic.Biplot: Plots an External Logistic Biplot for binary data

View source: R/plot.External.Binary.Logistic.Biplot.R

plot.External.Binary.Logistic.BiplotR Documentation

Plots an External Logistic Biplot for binary data

Description

Plot of an External Binary Logistic Biplot with many arguments controling different aspects of the representation

Usage

## S3 method for class 'External.Binary.Logistic.Biplot'
plot(x, F1 = 1, F2 = 2, 
                    ShowAxis = FALSE, margin = 0.1,
                    PlotVars = TRUE, PlotInd = TRUE, WhatRows = NULL,
                    WhatCols = NULL, LabelRows = TRUE, LabelCols = TRUE,
                    RowLabels = NULL, ColLabels = NULL, RowColors = NULL,
                    ColColors = NULL, Mode = "s", TickLength = 0.01,
                    RowCex = 0.8, ColCex = 0.8, SmartLabels = FALSE,
                    MinQualityRows = 0, MinQualityCols = 0, dp = 0,
                    PredPoints = 0, SizeQualRows = FALSE, ShowBox = FALSE,
                    SizeQualCols = FALSE, ColorQualRows = FALSE,
                    ColorQualCols = FALSE, PchRows = NULL, PchCols = NULL,
                    PlotClus = FALSE, TypeClus = "ch", ClustConf = 1,
                    Significant = FALSE, alpha = 0.05, Bonferroni = FALSE,
                    PlotSupVars = TRUE, ...)
                    

Arguments

x

An object of type External.Binary.Logistic.Biplot

F1

Latent factor to represent at the X axis

F2

Latent factor to represent at the Y axis

ShowAxis

Should the axis be plotted?

margin

Margin for the labels in some of the biplot modes (percentage of the plot width). Default is 0. Increase the value if the labels are not completely plotted.

PlotVars

Should Variables be plotted

PlotInd

Should Individuals be plotted

WhatRows

A binary vector (0 and 1) that indicates if each individual row should be plotted or not

WhatCols

A binary vector (0 and 1) that indicates if each individual column should be plotted or not

LabelRows

Should Variables be labelled

LabelCols

Should Individuals be labelled

RowLabels

A vector of Labels for the rows if you do not want to use the data labels

ColLabels

A vector of Labels for the columns if you do not want to use the data labels

RowColors

A vector of colors for the rows

ColColors

A vector of colors for the rows

Mode

Mode of the biplot: "p", "a", "b", "ah" and "s". See details.

TickLength

Lenght of the tick marks. Depends on the scale of the graph.

RowCex

A scalar or a vector containing the sizes of the poitns ans labels for the rows. Default value is 0.8 if the sizes are not provided.

ColCex

A scalar or a vector containing the sizes of the poitns ans labels for the columns. Default value is 0.8 if the sizes are not provided.

SmartLabels

Plot the labels in a smart way

MinQualityRows

Minimum quality of representation for a row or individual to be plotted

MinQualityCols

Minimum quality of representation for a column or variable to be plotted

dp

"Drop Points" on the variables, a vector with integers. The row points are projected on the directions of the variables listed in the vector.

PredPoints

A vector with integers. The row points listed in the vector are projected onto all the variables.

SizeQualRows

Should the size of the row points be related to their qualities of representation (predictiveness)?

ShowBox

Should abox around the point be displayed?

SizeQualCols

Should the size of the column points be related to their qualities of representation (predictiveness)?

ColorQualRows

Should the color of the row points be related to their qualities of representation (predictiveness)?

ColorQualCols

Should the color of the column points be related to their qualities of representation (predictiveness)?

PchRows

Symbol for the row points. See help(points) for details.

PchCols

Symbol for the column points. See help(points) for details.

PlotClus

Should the clusters be plotted?

TypeClus

Type of plot for the clusters. ("ch"- Convex Hull, "el"- Ellipse or "st"- Star)

ClustConf

Percent of points included in the cluster. only the ClusConf percent of the points nearest to the center will be used to calculate the cluster

Significant

If TRUE, only the significant variables are plotted

alpha

Significance Level

Bonferroni

Should the Bonferroni correction be used

PlotSupVars

Should supplementary variables be plotted

...

Any other graphical parameter you want to use

Details

The logistic regression equation predicts the probability that a caracter will be present in an individual. Geometrically the y´s can be represented as point in the reduced dimension space and the b's are the vectors showing the directions that best predict the probability of presence of each allele . For a com-plete explanation of the geometrical properties of the ELB see Vicente-Villardón et al (2006). The prediction of the probabilities is made in the same way as in a linear Biplot, i. e., the projection of a genotype point on the direction of an variable vector predicts the probability of presence of that variable in the individual. To facilitate the interpretation of the graph, fixed prediction probabilities points are situated on each allele vector. To simplify the graph, in our ap-plication, a vector joining the points for 0.5 and 0.75 are placed; this shows the cut point for prediction of presence and the direction of increasing probabilities. The length of the vector can be interpreted as an inverse measure of the discriminatory power of the alleles or bands, in the sense that shorter vectors correspond to alleles that better differentiate individuals. Two alleles pointing in the same direction are highly correlated, two alleles pointing in opposite directions are negatively correlated, and two alleles forming an angle close to 90º are not correlated. A more complete scale with probabilities from 0.1 to 0.9 can also be plotted with this function. For each variable, the ordination diagram can be divided into two separate regions predicting presence or absence, the two regions are separated by the line that is perpendicular to the variable vector in the Biplot and cuts the vector in the point predicting 0.5. The variables associated to the configuration are those that predict the presences adequately. In a practical situation not all the variables are associated to the ordination. Due to the high number usually studied, it is convenient to situate on the graph only those that are related to the configuration, i. e., those that have an adequate goodness of fit after adjusting the logistic regression.

Value

No value returned

Author(s)

Jose Luis Vicente Villardon

References

Demey, J., Vicente-Villardon, J. L., Galindo, M.P. AND Zambrano, A. (2008) Identifying Molecular Markers Associated With Classification Of Genotypes Using External Logistic Biplots. Bioinformatics, 24(24): 2832-2838.

Vicente-Villardon, J. L., Galindo, M. P. and Blazquez, A. (2006) Logistic Biplots. In Multiple Correspondence Analysis And Related Methods. Grenacre, M & Blasius, J, Eds, Chapman and Hall, Boca Raton.

See Also

ExternalBinaryLogisticBiplot

Examples

data(spiders)
dist=BinaryProximities(spiders)
pco=PrincipalCoordinates(dist)
pcobip=ExternalBinaryLogisticBiplot(pco)
plot(pcobip, Mode="s")
pcobip=AddCluster2Biplot(pcobip, NGroups=3, ClusterType="hi")
op <- par(mfrow=c(1,2)) 
plot(pcobip, Mode="s", PlotClus = TRUE)
plot(pcobip$Dendrogram)
par(op)


MultBiplotR documentation built on Nov. 21, 2023, 5:08 p.m.