| Type: | Package |
| Title: | New Data Visualisations for SOMs Networks |
| Version: | 0.4.0 |
| Description: | The aim of this package is to offer more variability of graphics based on the self-organizing maps. |
| License: | MIT + file LICENSE |
| LazyData: | true |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.4.0) |
| Imports: | dplyr, magrittr, tidyr, ggplot2, kohonen, assertthat, data.table, entropy, tibble |
| Suggests: | devtools, knitr, rmarkdown |
| URL: | https://github.com/oldlipe/ggsom |
| RoxygenNote: | 7.0.0 |
| Collate: | 'ggsom.R' 'ggsom_aes.R' 'ggsom_entropy.R' 'ggsom_plot.R' 'ggsom_utils.R' 'zzz.R' |
| NeedsCompilation: | no |
| Packaged: | 2020-01-15 20:21:31 UTC; felipe |
| Author: | Felipe Carvalho [aut, cre], Rafael Santos [ctb], Karine Reis [ctb] |
| Maintainer: | Felipe Carvalho <lipecaso@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-01-15 20:40:02 UTC |
Visualization in parallelels coordinates in matrix of each attribute
Description
Visualization of the classes corresponding to each neuron of the SOM
Usage
geom_class(object_som, class = NULL, x_o = 3, y_o = 5.5, x_e = 3, y_e = 6.3)
Arguments
object_som |
object of Kohonen package |
class |
categorical vector corresponding to the class of the dataset |
x_o |
x-axis to map the number of observations of each neuron |
y_o |
y-axis to map the number of observations of each neuron |
x_e |
x-axis to map the entropy of each neuron |
y_e |
y-axis to map the entropy of each neuron |
Value
ggplot2 object
Author(s)
Felipe Carvalho, lipecaso@gmail.com
References
'ggplot2' package (https://CRAN.R-project.org/package=ggplot2)
Examples
# Creating SOM object
iris_som <- kohonen::som(X = as.matrix(iris[1:4]),
grid = kohonen::somgrid(xdim = 5,
ydim = 5,
neighbourhood.fct = "gaussian",
topo = "rectangular"),
rlen = 100)
# Creating ggsom class plot
geom_class(iris_som, class = iris$Species,
x_o = 1, y_o = 6,
x_e = 1.1, y_e = 7.4)
ggsom
Description
The aim of this package is to offer more variability of graphics based on the self-organizing maps
kohonen package object modeling
Description
Function to map each SOM neuron with its corresponding class
Usage
ggsom_aes(object_som, class)
Arguments
object_som |
object of kohonen package |
class |
categorical vector corresponding to the class of the dataset |
Value
data.table model used in visualizations
Author(s)
Felipe Carvalho, lipecaso@gmail.com
References
'Kohonen'package (https://CRAN.R-project.org/package=kohonen)
Function to obtain the purity of each neuron in the SOM network
Description
Entropy calculation using the maximum likelihood method
Usage
ggsom_entropy(ggsom_aes)
Arguments
ggsom_aes |
kohonen package object modeling |
Value
Data set with the purity attribute added in Tibble
Author(s)
Felipe Carvalho, felipe.carvalho@inpe.br
verifies that the object inherits kohonen object
Description
if object inherits kohonen class return TRUE otherwise FALSE
Usage
is.kohonen(object_som)
Arguments
object_som |
object of Kohonen package |
Value
Boolean value
Author(s)
Felipe Carvalho, lipecaso@gmail.com
References
'Kohonen'package (https://CRAN.R-project.org/package=kohonen)