numero.subgroup | R Documentation |

Plot self-organizing map colorings and let the user choose multi-district regions as subgroups

numero.subgroup(results, variables, topology = NULL, reference = NULL, gain = 1, detach = FALSE, capacity = 9, automatic = FALSE)

`results` |
A list object that contains the self-organizing map and its statistical colorings. |

`variables` |
A string vector that contains names of variables to show on screen. |

`topology` |
A SOM topology or the output from a previous subgrouping session. |

`reference` |
Reference color ranges and scales. |

`gain` |
Modifier for overall color intensity. |

`detach` |
Use a detached window. |

`capacity` |
Maximum number of subplots to show on screen. |

`automatic` |
If greater than zero, automatic segmentation of the map is triggered, the value sets the number of subgroups. |

The input `results`

must contain the output from
`numero.evaluate()`

or similar.

The input argument `topology`

can be the structure of a SOM or with
additional columns as in the output from `nroPlot()`

.

The input argument `reference`

follows the output format from
`numero.evaluate()`

.

Setting detach to FALSE will also clear all devices whenever the figure is
refreshed. This may be inconvenient when using R from the terminal,
for example; please see the help page of `numero.plot()`

for
using a detached window device instead.

If any districts are left unmarked, they are automatically collected
into a subgroup of their own. If `automatic`

is set, user input
is skipped.

A data frame similar to the format returned by `nroPlot()`

.

# Import data. fname <- system.file("extdata", "finndiane.txt", package = "Numero") dataset <- read.delim(file = fname) # Set identities and manage missing data. dataset <- numero.clean(dataset, identity = "INDEX") # Prepare training variables. trvars <- c("CHOL", "HDL2C", "TG", "CREAT", "uALB") trdata <- numero.prepare(data = dataset, variables = trvars) # Create a self-organizing map. sm <- numero.create(data = trdata) qc <- numero.quality(model = sm) # Evaluate map statistics for all variables. stats <- numero.evaluate(model = qc, data = dataset) # Define subgroups, uncomment to launch interactive window. #elem <- numero.subgroup(results = stats, variables = trvars)

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