# plot.PCA: Draw the Principal Component Analysis (PCA) graphs In FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

 plot.PCA R Documentation

## Draw the Principal Component Analysis (PCA) graphs

### Description

Plot the graphs for a Principal Component Analysis (PCA) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables.

### Usage

``````## S3 method for class 'PCA'
plot(x, axes = c(1, 2), choix = c("ind","var","varcor"),
ellipse = NULL, xlim = NULL, ylim = NULL, habillage="none",
col.hab = NULL, col.ind="black", col.ind.sup="blue",
col.quali="magenta", col.quanti.sup="blue", col.var="black",
label = c("all","none","ind","ind.sup","quali","var","quanti.sup"),
invisible = c("none","ind","ind.sup","quali","var","quanti.sup"),
lim.cos2.var = 0., title = NULL, palette=NULL,
autoLab = c("auto","yes","no"), new.plot = FALSE, select = NULL,
unselect = 0.7, shadowtext = FALSE, legend = list(bty = "y", x = "topleft"),
graph.type = c("ggplot","classic"), ggoptions = NULL, ...)
``````

### Arguments

 `x` an object of class PCA `axes` a length 2 vector specifying the components to plot `choix` the graph to plot ("ind" for the individuals, "var" for the variables, "varcor" for a graph with the correlation circle when `scale.unit=FALSE`) `ellipse` boolean (NULL by default), if not null, draw ellipses around the individuals, and use the results of `coord.ellipse` `xlim` range for the plotted 'x' values, defaulting to the range of the finite values of 'x' `ylim` range for the plotted 'y' values, defaulting to the range of the finite values of 'y' `habillage` give no color for the individuals ("none"), a color for each individual ("ind"), or color the individuals among a categorical variable (give the number of the categorical variable) `col.hab` a vector with the color to use for the individuals `col.ind` a color for the individuals only if there is not habillage `col.ind.sup` a color for the supplementary individuals only if there is not habillage `col.quali` a color for the categories of categorical variables only if there is not habillage `col.quanti.sup` a color for the quantitative supplementary variables `col.var` a color for the variables `label` a list of character for the elements which are labelled (by default, all the elements are labelled ("ind", ind.sup", "quali", "var", "quanti.sup")) `invisible` string indicating if some points should not be drawn ("ind", "ind.sup" or "quali" for the individual graph and "var" or "quanti.sup" for the correlation circle graph) `lim.cos2.var` value of the square cosinus under the variables are not drawn `title` string corresponding to the title of the graph you draw (by default NULL and a title is chosen) `palette` the color palette used to draw the points. By default colors are chosen. If you want to define the colors : palette=palette(c("black","red","blue")); or you can use: palette=palette(rainbow(30)), or in black and white for example: palette=palette(gray(seq(0,.9,len=25))) `autoLab` if `autoLab="auto"`, `autoLab` is equal to "yes" if there are less than 50 elements and "no" otherwise; if "yes", the labels of the drawn elements are placed in a "good" way (can be time-consuming if many elements), and if "no" the elements are placed quickly but may overlap `new.plot` boolean, if TRUE, a new graphical device is created; only used when `graph.type="classic"` `select` a selection of the elements that are drawn; see the details section `unselect` may be either a value between 0 and 1 that gives the transparency of the unselected objects (if `unselect=1` the transparceny is total and the elements are not drawn, if `unselect=0` the elements are drawn as usual but without any label) or may be a color (for example `unselect="grey60"`) `shadowtext` boolean; if true put a shadow on the labels (rectangles are written under the labels which may lead to difficulties to modify the graph with another program); only used when `graph.type="classic"` `legend` a list of arguments that defines the legend if needed (when individuals are drawn according to a variable); see the arguments of the function `legend` `graph.type` a character that gives the type of graph used: "ggplot" or "classic" `ggoptions` a list that gives the graph options when grah.type="ggplot" is used. See the optines and the default values in the details section `...` further arguments passed to or from other methods, such as cex, cex.main, ...

### Details

The argument `autoLab = "yes"` is time-consuming if there are many labels that overlap. In this case, you can modify the size of the characters in order to have less overlapping, using for example cex=0.7.
The `select` argument can be used in order to select a part of the elements (individuals if you draw the graph of individuals, or variables if you draw the graph of variables) that are drawn. For example, you can use:
`select = 1:5` and then the elements 1:5 are drawn.
`select = c("name1","name5")` and then the elements that have the names name1 and name5 are drawn.
`select = "coord 10"` and then the 10 elements that have the highest (squared) coordinates on the 2 chosen dimensions are drawn.
`select = "contrib 10"` and then the 10 elements that have the highest contribution on the 2 dimensions of your plot are drawn.
`select = "cos2 5"` and then the 5 elements that have the highest cos2 on the 2 dimensions of your plot are drawn.
`select = "dist 8"` and then the 8 elements that have the highest distance to the center of gravity are drawn.

`ggoptions` is a list that gives some ggplot2 options when the graph.type="ggplot" is used. Use for instance ggoptions(list(size=3,title.size=10,bg.color="orange")) if you want to modify the size of the points and labels, the title size and the background color.
Below you can see the options and the default values:
size = 4, #label size (point size = size/3)
point.shape = 19, #points shape
line.lty = 2, #origin linetypes (0="blank", 1="solid", 2="dashed", 3="dotted",...)
line.lwd = 0, #origin lines width
line.color = "black", #origin lines color
segment.lty = 1, #arrow linetypes (0="blank", 1="solid", 2="dashed", 3="dotted",...)
segment.lwd = 0, #arrow width
circle.lty = 1, #circle linetypes (0="blank", 1="solid", 2="dashed", 3="dotted",...)
circle.lwd = 0, #circle width
circle.color = "black", #circle color
low.col.quanti = "blue", #for quantitative variables, low color to be used
high.col.quanti = "red3", #for quantitative variables, high color to be used

### Value

Returns the individuals factor map and the variables factor map.

### Author(s)

Francois Husson francois.husson@institut-agro.fr

`PCA`

### Examples

``````data(decathlon)
res.pca <- PCA(decathlon, quanti.sup = 11:12, quali.sup = 13)
plot(res.pca, habillage = 13, cex=0.8)
## Not run:
plot(res.pca, habillage = "cos2")
plot(res.pca, habillage = "100m")
plot(res.pca, habillage = c("Competition","100m"))

## End(Not run)
## To automatically draw ellipses around the barycentres of the categorical variables
plotellipses(res.pca)

## Not run:
## Selection of some individuals
plot(res.pca,select="contrib 7") # plot the 7 individuals with the highest contribution
plot(res.pca,select="cos2 0.8")  # plot the individuals with cos2 greater than 0.8
plot(res.pca,select="cos2 5")    # plot the 5 individuals with the highest cos2
plot(res.pca,choix="var",select="cos2 0.6")  # plot the variables with cos2 greater than 0.6

plot(res.pca,habillage="100m",
ggoptions=list(low.col.quanti="grey90",high.col.quanti="grey10"),legend=list(x="bottom"))

## You can modify the ggplot graphs as ususal with ggplot2
require(ggplot2)
gr <- plot(res.pca)
gr + theme(panel.grid.major = element_blank(),
plot.title=element_text(size=14, color="blue"),
axis.title = element_text(size=12, color="red"))

## To draw classical R graphs
plot(res.pca, graph.type = "classic")

## End(Not run)
``````

FactoMineR documentation built on Oct. 13, 2023, 1:06 a.m.