`somRes`

results`somRes.plotting.Rd`

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).

`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 argument`variable`

.`"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.`"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 to`2`

) and`minsize`

(default to`0.5`

) 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.

`"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 function`as.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 argument`proportional = 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 the`variable`

argument. This case can take one more argument:`size`

(default to`4`

) for the size of the words.`"words"`

(N, R)needs the argument

`variable`

be a numeric matrix or a`data.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 the`variable`

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 an`igraph`

object (see`library("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`

and`pie.variable`

. These are used to display the vertex as pie charts. For this case,`pie.graph`

must be set to`TRUE`

and a factor vector is supplied by`pie.variable`

.`plot.igraph`

is called by the cases:`what = "add"`

/`type = "graph"`

`what = "add"`

/`type = "projgraph"`

(for a superclass object)

`persp`

is called by the case`what = "prototypes"`

/`type = "3d"`

`ggplot`

is called in all the other cases.`geom_text_wordcloud`

is called by the cases:`type = "names"`

`what = "add"`

/`type = "words"`

`geom_contour_fill`

is called by the case`what = "prototypes"`

/`type = "smooth.dist"`

`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.

`what = "prototypes"`

The possible values for ** type** are:

`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:

Further arguments, their reference functions and the `plot.somRes`

cases
are summarized in the following list:

In complement to ggplot,

### 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") # energyif (FALSE) { 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")} ####### plots on prototypes plot(iris.som, what = "prototypes", type = "3d", variable = "Sepal.Length")if (FALSE) { 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")} ####### plots on an additional variable: the flower species plot(iris.som, what = "add", type = "pie", variable = iris$Species)