The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.