R Biplot Pca
- Examples
- Create a biplot of pcaoutputall that helps visualise all individuals and variables in the same plot. Notice the directions of the arrows with respect to the point clouds and try to interpret the displayed biplot.
- Principal Component Analysis (PCA) is a useful technique for exploratory data analysis, allowing you to better visualize the variation present in a dataset with many variables. It is particularly helpful in the case of 'wide' datasets, where you have many variables for each sample. In this tutorial, you'll discover PCA in R.
Jan 23, 2017 Principal component analysis (PCA) is routinely employed on a wide range of problems. From the detection of outliers to predictive modeling, PCA has the ability of projecting the observations described by variables into few orthogonal components defined at where the data ‘stretch’ the most, rendering a simplified overview. A biplot shows information on variable loadings, which helps to interpret a PCA. To see a biplot, use the biplot parameter. If TRUE, then variable loadings will be extracted from the princomp or prcomp object. If biplot is a matrix, then it is assumed to be a matrix of variable loadings. A plot is produced on the current graphics device. Signature(x = Pca): Plot a biplot, i.e. Represent both the observations and variables of a matrix of multivariate data on the same plot.
Draw the graph of individuals/variables from the output of Principal Component Analysis (PCA).
The following functions, from factoextra package are use:
- fviz_pca_ind(): Graph of individuals
- fviz_pca_var(): Graph of variables
- fviz_pca_biplot() (or fviz_pca()): Biplot of individuals and variables
The package devtools is required for the installation as factoextra is hosted on github.
Load factoextra :
| Argument | Description |
|---|---|
| X | an object of class PCA [FactoMineR]; prcomp and princomp [stats]; dudi and pca [ade4]. |
| axes | a numeric vector of length 2 specifying the dimensions to be plotted. |
| geom | a text specifying the geometry to be used for the graph. Allowed values are the combination of c(“point”, “arrow”, “text”). Use “point” (to show only points); “text” to show only labels; c(“point”, “text”) or c(“arrow”, “text”) to show both types. |
| label | a text specifying the elements to be labelled. Default value is “all”. Allowed values are “none” or the combination of c(“ind”, “ind.sup”, “quali”, “var”, “quanti.sup”). “ind” can be used to label only active individuals. “ind.sup” is for supplementary individuals. “quali” is for supplementary qualitative variables. “var” is for active variables. “quanti.sup” is for quantitative supplementary variables. |
| invisible | a text specifying the elements to be hidden on the plot. Default value is “none”. Allowed values are the combination of c(“ind”, “ind.sup”, “quali”, “var”, “quanti.sup”). |
| labelsize | font size for the labels. |
| pointsize | the size of points. |
| habillage | an optional factor variable for coloring the observations by groups. Default value is “none”. If X is a PCA object from FactoMineR package, habillage can also specify the supplementary qualitative variable (by its index or name) to be used for coloring individuals by groups (see ?PCA in FactoMineR). |
| addEllipses | logical value. If TRUE, draws ellipses around the individuals when habillage != “none”. |
| ellipse.level | the size of the concentration ellipse in normal probability. |
| col.ind,col.var | colors for individuals and variables, respectively. Possible values include also : “cos2”, “contrib”, “coord”, “x” or “y”. In this case, the colors for individuals/variables are automatically controlled by their qualities of representation (“cos2”), contributions (“contrib”), coordinates (x^2 + y^2, “coord”), x values (“x”) or y values (“y”). To use automatic coloring (by cos2, contrib, ….), make sure that habillage =“none”. |
| col.ind.sup | color for supplementary individuals. |
| alpha.ind,alpha.var | controls the transparency of individual and variable colors, respectively. The value can variate from 0 (total transparency) to 1 (no transparency). Default value is 1. Possible values include also : “cos2”, “contrib”, “coord”, “x” or “y”. In this case, the transparency for the individual/variable colors are automatically controlled by their qualities (“cos2”), contributions (“contrib”), coordinates (x^2 + y^2 , “coord”), x values(“x”) or y values(“y”). To use this, make sure that habillage =“none”. |
| select.ind,select.var | a selection of individuals/variables to be drawn. Allowed values are NULL or a list containing the arguments name, cos2 or contrib:
|
| jitter | a parameter used to jitter the points in order to reduce overplotting. It’s a list containing the objects what, width and height (Ex.; jitter = list(what, width, height)). what: the element to be jittered. Possible values are “point” or “p”; “label” or “l”; “both” or “b”. width: degree of jitter in x direction (ex: 0.2). height: degree of jitter in y direction (ex: 0.2). |
| col.quanti.sup | a color for the quantitative supplementary variables. |
| col.circle | a color for the correlation circle. |
| … | Arguments to be passed to the function fviz_pca_biplot(). |
Principal component analysis
A principal component analysis (PCA) is performed using the built-in R function prcomp() and iris data:
fviz_pca_var(): Graph of variables
fviz_pca_biplot(): Biplot of individuals of variables
This analysis has been performed using R software (ver. 3.2.1) and factoextra (ver. 1.0.3)
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Biplot for Principal Components (objects of class 'Pca')
Produces a biplot from an object (derived from) Pca-class.
- Keywords
- multivariate, hplot
Usage
Arguments
an object of class (derived from) 'Pca'.
length 2 vector specifying the components to plot. Only the default is a biplot in the strict sense.
The variables are scaled by lambda ^ scale and the observations are scaled by lambda ^ (1-scale) where lambda are the singular values as computed by the Principal Components function. Normally 0 <= scale <= 1, and a warning will be issued if the specified scale is outside this range.
optional arguments to be passed to the internal graphical functions.
Side Effects
a plot is produced on the current graphics device.
Methods
signature(x = Pca): Plot a biplot, i.e. represent both the observations and variables of a matrix of multivariate data on the same plot. See also biplot.princomp.
References
Gabriel, K. R. (1971). The biplot graphical display of matrices with applications to principal component analysis. Biometrika, 58, 453--467.
See Also
Pca-class, PcaClassic, PcaRobust-class.
Aliases
- biplot
- biplot-methods
- biplot,ANY-method
- biplot,Pca-method
Examples
R Biplot Pca Group
Documentation reproduced from package rrcov, version 1.5-5, License: GPL (>= 2)