README.md
In SparseBiplots: 'HJ-Biplot' using Different Ways of Penalization Plotting with 'ggplot2'

SparseBiplots

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 code

Install SparseBiplots from CRAN: ```{r Install from CRAN} install.packages("SparseBiplots")


* Install *SparseBiplots* from **GitHub** using [devtools](https://github.com/r-lib/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)