somRes.plotting {SOMbrero} | R Documentation |
Plotting somRes
results
Description
Useful details on how to produce graphics to help interpreting a somRes
object.
Important: the graphics availables for the different types of SOM are marked with a N, a K or a R.
(N = numerical SOM, K = korresp SOM and R = relational SOM).
Graphics on the observations: what = "obs"
For the cases what = "obs"
and what = "add"
, if a neuron is empty,
nothing will be plotted at its location.
The possible values for type
are:
"hitmap"
(K, R)plots proportional areas according to the number of observations per neuron. It is the default plot when
what="obs"
."color"
(N)can have one more argument,
variable
, the name or index of the variable to be considered (default,1
, the first variable). Neurons are filled using the given colors according to the average value level of the observations for the chosen variable."lines"
(N)plots a line for each observation in every neuron, between variables. A vector of variables (names or indexes) can be provided with the argument
variable
."meanline"
(N)plots, for each neuron, the average value level of the observations, with lines and points. One point represents a variable. By default, all variables of the dataset used to train the algorithm are plotted but a vector of variables (names or indexes) can be provided with the argument
variable
."barplot"
(N)is similar to
"meanline"
but using barplots. Then, a bar represents a variable."boxplot"
(N)plots boxplots for the observations in every neuron, by variable. Like
"lines"
,"meanline"
and"barplot"
a vector of variables (names or indexes) can be provided with the argumentvariable
."names"
(N, K, R)prints on the grid the element names (i.e., the row names or row and column names in the case of
korresp
) in the neuron to which it belongs.
Graphic on the energy: what = "energy"
(N, K, R)
This graphic is only available if some intermediate backups have been registered
(i.e., with the argument nb.save
of trainSOM
or initSOM
resulting in x$parameters$nb.save>1
). Graphic plots the evolution of the
level of the energy according to the registered steps.
Graphics on the prototypes: what = "prototypes"
The possible values for type
are:
"lines"
(N, K, R)has the same behavior as the
"lines"
case described in the observations section, but according to the prototypes level."barplot"
(N, K, R)has the same behavior as the
"barplot"
case described in the observations section, but according to the prototypes level."color"
(N, K)has the same behavior as the
"color"
case described in the observations section, but according to the prototypes level."3d"
(N)case is similar to the
"color"
case, but in 3 dimensions, with x and y the coordinates of the grid and z the value of the prototypes for the considered variable. This function can take two more arguments:maxsize
(default to2
) andminsize
(default to0.5
) for the size of the points representing neurons."smooth.dist"
(N, K, R)depicts the average distance between a prototypes and its neighbors on a map where x and y are the coordinates of the prototypes on the grid.
"poly.dist"
(N, K, R)also represents the distances between prototypes but with polygons plotted for each neuron. The closest from the border the polygon point is, the closest the pairs of prototypes are. The color used for filling the polygon shows the number of observations in each neuron. A white polygon means that there is no observation. With the default colors, a red polygon means a high number of observations.
"umatrix"
(N, K, R)is another way of plotting distances between prototypes. The grid is plotted and filled with
my.palette
colors according to the mean distance between the current neuron and the neighboring neurons. With the default colors, red indicates proximity."mds"
(N, K, R)plots the number of the neuron on a map according to a Multi Dimensional Scaling (MDS) projection on a two dimensional space.
"grid.dist"
(N, K, R)plots on a 2 dimension map all distances. The number of points on this picture is equal to
\frac{\textrm{number of neurons}\times(\textrm{number of neurons}-1)}{2}
. On the x axis corresponds to the prototype distances whereas the y axis depicts the grid distances.
Graphics on an additional variable: what="add"
The case what="add"
considers an additional variable, which has to be
given to the argument variable
. Its length must match the number of
observations in the original data.
When the algorithm's type is korresp
, no graphic is available for
what = "add"
.
The possible values for type
are:
"color"
(N, R)has the same behavior as the
"color"
case described in the observations section. Then, the additional variable must be a numerical vector."lines"
(N, R)has the same behavior as the
"lines"
case described in the observations section. Then, the additional variable must be a numerical matrix or a data frame."boxplot"
(N, R)has the same behavior as the
"boxplot"
case described in the observations section. Then, the additional variable must be either a numeric vector or a numeric matrix/data frame."barplot"
(N, R)has the same behavior as the
"barplot"
case described in the observations section. Then, the additional variable must be either a numeric vector or a numeric matrix/data frame."pie"
(N)requires the argument
variable
to be a vector, which will be passed to the functionas.factor
, and plots one pie for each neuron according to this factor. By default, the size of the pie is proportional to the number of observations affected to its neuron but this can be changed with the argumentproportional = FALSE
."names"
(N, R)has the same behavior as the
"names"
case described in the observations section. Then, the names to be printed are the elements of the variable given to thevariable
argument. This case can take one more argument:size
(default to4
) for the size of the words."words"
(N, R)needs the argument
variable
be a numeric matrix or adata.frame
: names of the columns will be used as words and the values express the frequency of a given word in the observation. Then, for each neuron of the grid, the words will be printed with sizes proportional to the sum of their values in the neuron. If thevariable
given is a contingency table, it will plot directly the frequency of the words in the neurons."graph"
(N, R)requires that the argument
variable
is anigraph
object (seelibrary("igraph")
. According to the existing edges in the graph and to the clustering obtained with the SOM algorithm, a clustered graph will be produced where a vertex between two vertices represents a neuron and the width of an edge is proportional to the number of edges in the given graph between the vertices affected to the corresponding neurons. The option can handle two more arguments:pie.graph
andpie.variable
. These are used to display the vertex as pie charts. For this case,pie.graph
must be set toTRUE
and a factor vector is supplied bypie.variable
.
Further arguments via ...
Further arguments, their reference functions and the plot.somRes
cases
are summarized in the following list:
-
plot.igraph
is called by the cases:-
what = "add"
/type = "graph"
-
what = "add"
/type = "projgraph"
(for a superclass object)
-
-
persp
is called by the casewhat = "prototypes"
/type = "3d"
-
ggplot
is called in all the other cases.
In complement to ggplot,
-
geom_text_wordcloud
is called by the cases:-
type = "names"
-
what = "add"
/type = "words"
-
-
geom_contour_fill
is called by the casewhat = "prototypes"
/type = "smooth.dist"
Author(s)
Élise Maigné elise.maigne@inrae.fr
Madalina Olteanu madalina.olteanu@univ-paris1.fr
Nathalie Vialaneix nathalie.vialaneix@inra.fr
Examples
### Numerical SOM
# run the SOM algorithm on the numerical data of 'iris' data set
iris.som <- trainSOM(x.data = iris[,1:4], nb.save = 2)
####### energy plot
plot(iris.som, what = "energy") # energy
####### plots on observations
plot(iris.som, what = "obs", type = "hitmap")
## Not run:
plot(iris.som, what = "obs", type = "lines")
plot(iris.som, what = "obs", type = "barplot")
plot(iris.som, what = "obs", type = "boxplot")
plot(iris.som, what = "obs", type = "meanline")
plot(iris.som, what = "obs", type = "color", variable = 1)
plot(iris.som, what = "obs", type = "names")
## End(Not run)
####### plots on prototypes
plot(iris.som, what = "prototypes", type = "3d", variable = "Sepal.Length")
## Not run:
plot(iris.som, what = "prototypes", type = "lines")
plot(iris.som, what = "prototypes", type = "barplot")
plot(iris.som, what = "prototypes", type = "umatrix")
plot(iris.som, what = "prototypes", type = "color", variable = "Petal.Length")
plot(iris.som, what = "prototypes", type = "smooth.dist")
plot(iris.som, what = "prototypes", type = "poly.dist")
plot(iris.som, what = "prototypes", type = "grid.dist")
plot(iris.som, what = "prototypes", type = "mds")
## End(Not run)
####### plots on an additional variable: the flower species
plot(iris.som, what = "add", type = "pie", variable = iris$Species)
## Not run:
plot(iris.som, what = "add", type = "names", variable = iris$Species)
plot(iris.som, what = "add", type = "words", variable = iris[,1:2])
## End(Not run)