ggcloud_variables {GDAtools} | R Documentation |
Plot of the cloud of variables
Description
Plots a Multiple Correspondence Analysis cloud of variables.
Usage
ggcloud_variables(resmca, axes = c(1,2), points = "all",
min.ctr = NULL, max.pval = 0.01, face = "pp",
shapes = TRUE, prop = NULL, textsize = 3, shapesize = 3,
col = NULL, col.by.group = TRUE, alpha = 1,
segment.alpha = 0.5, vlab = TRUE, sep = ".", legend = "right",
force = 1, max.overlaps = Inf)
Arguments
resmca |
object of class |
axes |
numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2). |
points |
character string. If 'all' all categories are plotted (default); if 'besth' only those who contribute most to horizontal axis are plotted; if 'bestv' only those who contribute most to vertical axis are plotted; if 'besthv' only those who contribute most to horizontal or vertical axis are plotted; if 'best' only those who contribute most to the plane are plotted. |
min.ctr |
Numerical value between 0 and 100. The minimum contribution (in percent) for a category to be displayed if the |
max.pval |
Numerical value between 0 and 100. The maximal p-value derived from test-values for a category to be displayed if the |
face |
character string. Changes the face of the category labels when their contribution is greater than |
shapes |
Logical. Should shapes be plotted for categories (in addition to labels) ? Default is TRUE. |
prop |
If NULL, the size of the labels (if shapes=FALSE) or the shapes (if shapes=TRUE) is constant. If 'n', the size is proportional the the weights of categories; if 'ctr1', the size is proportional to the contributions of the categories on the first dimension of the plot; if 'ctr2', the size is proportional to the contributions of the categories on the second dimension of the plot; if 'ctr12', the size is proportional to the contributions of the categories on the plane ; if 'ctr.cloud', the size is proportional to the total contributions of the categories on the whole cloud; if 'cos1', the size is proportional to the quality of representation (squared cosines) of the categories on the first dimension of the plot; if 'cos2', the size is proportional to the quality of representation of the categories on the second dimension of the plot; if 'cos12', the size is proportional to the quality of representation of the categories on the plane; if 'vtest1', the size is proportional to the test-values of the categories on the first dimension of the plot; if 'vtest2', the size is proportional to the test-values of the categories on the second dimension of the plot. |
textsize |
Size of the labels of categories if shapes=TRUE, or if shapes=FALSE and prop=NULL. Default is 3. |
shapesize |
Size if the shapes of categories if shapes=TRUE and prop=FALSE. Default is 3. |
col |
Character string. Color name for the shapes and labels of the categories. If NULL (default), the default |
col.by.group |
Logical. If |
alpha |
Transparency of the shapes and labels of categories. Default is 1. |
segment.alpha |
Transparency of the line segment beside labels of categories. Default is 0.5. |
vlab |
Logical. Should the variable names be used as a prefix for the labels of the categories. Default is TRUE. |
sep |
Character string used as a separator if vlab=TRUE. |
legend |
the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right. |
force |
Force of repulsion between overlapping text labels. Defaults to 1. If 0, labels are not repelled at all. |
max.overlaps |
Exclude text labels that overlap too many things. Defaults to Inf, which means no labels are excluded. |
Value
a ggplot2
object
Note
If col
argument is NULL, shapes or labels are colored according to the variables, using the default ggplot2
palette. The palette can be customized using any scale_color_*
function, such as scale_color_brewer()
, scale_color_grey()
or scale_color_manual()
.
If resmca
is of type stMCA
or multiMCA
and points
is not equal to "all"
, test-values are used instead of contributions (which are not available for these MCA variants) to select the most important categories ; if points
is equal to best
, only categories with high test-values for horizontal axis or vertical axis are plotted.
Author(s)
Anton Perdoncin, Nicolas Robette
References
Le Roux B. and Rouanet H., Multiple Correspondence Analysis, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).
Le Roux B. and Rouanet H., Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis, Kluwer Academic Publishers, Dordrecht (June 2004).
See Also
Examples
# specific MCA of Music example data set
data(Music)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA")
mca <- speMCA(Music[,1:5], excl = junk)
# cloud of variables
ggcloud_variables(mca)
# cloud of variables with only categories contributing the most
ggcloud_variables(mca, points = "best", prop = "n")
# cloud of variables with other plotting options
ggcloud_variables(mca, shapes = FALSE, legend = "none",
col = "black", face = "ui")