Sparse Biplots are modern variant of HJ-Biplot which attempts to find sparse loadings, i.e., weight vectors with only a few nonzero values. Introducing a penalty parameter helps improve the interpretation of the model because the axis are formed as a linear combination of only some of the original variables.
This package provides functions to perform the HJ-Biplot (Galindo, 1986) and modifications introducing Ridge, LASSO and Elastic Net penalty:
HJBiplot: performs the HJ-Biplot .Ridge_HJBiplot: performs the HJ-Biplot introducing
Ridge penalty.LASSO_HJBiplot: performs the HJ-Biplot introducing
LASSO penalty.ElasticNet_HJBiplot: performs the HJ-Biplot introducing
Elastic Net penalty. For this use the spca package.Plot_Biplot: create the plot of the results obtained
with any of the above functions. It generates elegant data visualization
using ggplot2 with
little codeInstall SparseBiplots from CRAN:
{r Install from CRAN} install.packages("SparseBiplots")
Install SparseBiplots from GitHub using
devtools:
{r Install from GitHub} install.packages("devtools") devtools::install_github("mitzicubillamontilla/SparseBiplots")
X: data frame which provides the data to be analyzed.
All the variables must be numeric.Transform.Data: character indicating whether the
columns have to be scaled or centered. Allowed values are:
"center" and "scale" (default).lambda: tuning parameter for each penalty. In Elastic
Net it refers to LASSO penalty.àlpha: tuning parameter of the Ridge shrinkage (just
allowed with Elastic Net penalty).operator: operator used to solve the norm L1 (just
allowed with LASSO penalty). Allowed values are:
'Hard-Thresholding' and
'Soft-Thresholding'.ind_name: logical value which indicates if prints the
name for each row of X (FALSE by default).vec_name: logical value which indicates if prints the
name for each column of X (TRUE by default).loadings: loadings matrix.coord_ind: row coordinates matrix (individuals).coord_var: column coordinates matrix (variables).eigenvalues: approximated eigenvalues vector.explvar: vector containing the proportion of variance
explained by the k axis obtained.n_ceros: matrix which indicates the number of loadings
equal to cero in each axis (output exclusive just for LASSO and Elastic
net penalties)X: list containing the output of one of the functions
of the package.axis: vector with lenght 2 which contains the axis
ploted in x and y axis.hide: vector specifying the elements to be hidden on
the plot. Default value is “none”. Other allowed values are “ind” and
“var”.labels: it indicates the label for points. If it is
“auto” the labels are the row names of the coordinates of individuals.
If it isn’t auto it would be a vector containing the labels.ind.shape: points shapes. It can be a number to
indicate the shape of all the points or a factor to indicate different
shapes.ind.color: points colors. It can be a character
indicating the color of all the points or a factor to use different
colors.ind.size: numeric value indicating the size of
points.ind.label: logical name indicating if prints the row
names.ind.label.size: numeric value indicating the size of
the label of points.var.col: character indicating the color of the
arrows.var.size: size of arrow.var.label: logical name indicating if prints the column
names.var.label.size: numeric value indicating the size of
the labels of variables.var.label.angle: logical value indicating if print the
vector names with orentation of the angle of the vector.{r Fit HJ-Biplot} library("SparseBiplots") hj.biplot <- HJBiplot(mtcars)
It fit the HJ-Biplot on the R data mtcars returning a list
that contains the results of the fiting (hj_mtcars). To
create the plot use the function Plot_Biplot over the list
obtained.
{r Visualize HJ-Biplot} Plot_Biplot(hj.biplot, ind.label = TRUE)
