nroPlot {Numero} | R Documentation

## Plot a self-organizing map

### Description

Create a graphical interface for selecting subgroups from multiple map colorings simultaneously.

### Usage

    nroPlot(elements, colors, labels = NULL, values = NULL, subplot = NULL,
            interactive = FALSE, clear = NULL, file = NULL)

### Arguments

`elements` | A data frame with K rows and six columns that contains the district positions of a self-organizing map.
`colors` | A character vector with K elements or a K x N matrix of hexadecimal color codes as strings.
`labels` | A character vector with K elements or a K x N matrix of district labels.
`values` | A vector with K elements or a K x N matrix of district values.
`subplot` | A two-element vector that sets out the number of rows and columns for a grid layout of multiple colorings.
`clear` | If TRUE, all graphics devices are cleared when the plot is refreshed.
`interactive` | If TRUE, an interactive version of the plot is launched.
`file` | If non-empty, the figure is saved as a Scalable Vector Graphics file instead of plotting on the R device.

### Details

The input `elements` must follow the topology format `nroKohonen()`, but may also contain the columns REGION, REGION.label and REGION.color that specify the name for a subset of districts, the single character labels to be shown on top of those districts, and the color of the labels. The input can also be the list object as in the output format of `nroKohonen()`.

Some non-alphanumeric characters are not supported and will be automatically converted to '_'. Too long labels or column names will be truncated.

The default value for `clear` is TRUE to prevent multiple plot windows from accumulating within the RStudio. If you are running R from the terminal or using detached devices, setting `clear` will retain the current window when refreshing.

### Value

A data frame with K rows that contains the topology and subgrouping information.

### References

Gao S, Mutter S, Casey AE, Mäkinen V-P (2018) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, https://doi.org/10.1093/ije/dyy113

### Examples

    # Import data.
    fname <- system.file("extdata", "finndiane.txt", package = "Numero")
    dataset <- read.delim(file = fname)

    # Detect binary columns.
    dataset <- nroPreprocess(dataset, method = "")

    # Prepare training data.
    trvars <- c("CHOL", "HDL2C", "TG", "CREAT", "uALB")
    trdata <- scale.default(dataset[,trvars])

    # K-means clustering.
    km <- nroKmeans(data = trdata)

    # Self-organizing map.
    sm <- nroKohonen(seeds = km)
    sm <- nroTrain(som = sm, data = trdata)

    # Assign data points into districts.
    matches <- nroMatch(centroids = sm, data = trdata)

    # Select a subset of variables and detect binary data.
    vars <- c("AGE", "MALE", "uALB", "CHOL", "DIAB_KIDNEY", "DECEASED")
    selected <- nroPreprocess(dataset[,vars], method = "")

    # Calculate district averages for seleted variables.
    vars <- c("AGE", "MALE", "uALB", "CHOL", "DIAB_KIDNEY", "DECEASED")
    planes <- nroAggregate(topology = sm, districts = matches, data = selected)

    # Estimate statistics.
    stats <- nroPermute(som = sm, districts = matches, data = selected)

    # Set visuals.
    colrs <- nroColorize(values = planes, amplitudes = stats)
    labls <- nroLabel(topology = sm, values = planes)

    # Plot colorings on screen.
    nroPlot(elements = sm, colors = colrs, labels = labls)

[Package Numero version 1.2.0 Index]