JKBiplot {LDABiplots}

JKBiplot

Description

This function performs the representation of JK Biplot (Gabriel,1971).

Usage

JKBiplot (X, Transform.Data = 'scale')

Arguments

X

array_like;
A data frame which provides the data to be analyzed. All the variables must be numeric.

Transform.Data

character;
A value indicating whether the columns of X (variables) should be centered or scaled. The options are: "center" if center is TRUE, centering is done by subtracting the column means (omitting NA) of x from their corresponding columns, and if center is FALSE, centering is not done. "scale" the value of scale determines how column scaling is performed (after centering). If scale is a numeric-alike vector with length equal to the number of columns of x, then each column of x is divided by the corresponding value from scale. If scale is TRUE then scaling is done by dividing the (centered) columns of x by their standard deviations if center is TRUE, and the root mean square otherwise. If scale is FALSE, no scaling is done. To scale by standard deviations without centering, use scale(x,center=FALSE,scale=apply(x,2,sd,na.rm=TRUE)),"center_scale" center=TRUE and scale=TRUE,"none" neither center nor scale is done. The default value is "scale".

Details

Algorithm used to construct the JK Biplot. The Biplot is obtained as result of the configuration of markers for individuals and markers for variables in a reference system defined by the factorial axes resulting from the Decomposition in Singular Values (DVS).

Value

JKBiplot returns a list containing the following components:

eigenvalues

array_like;
vector with the eigenvalues.

explvar

array_like;
an vector containing the proportion of variance explained by the first 1, 2,.,k principal components obtained.

loadings

array_like;
the loadings of the principal components.

coord_ind

array_like;
matrix with the coordinates of individuals.

coord_var

array_like;
matrix with the coordinates of variables.

References

• Gabriel, K. R. (1971). The Biplot graphic display of matrices with applications to principal components analysis. Biometrika, 58(3), 453-467.

Examples

 JKBiplot(mtcars)