# Summarize subgroup statistics Estimates subgroup statistics after self-organizing map analysis ```r numero.summary(results, topology, data, capacity = 10) ``` ## Arguments - `results`: A list object that contains the self-organizing map and its statistical colorings. - `topology`: A SOM topology with additional labels that indicate selected regions. - `data`: A matrix or a data frame. - `capacity`: Maximum number of subgroups to compare. ## Details The input `results` must contain the output from `numero.evaluate()` or similar. The input argument `topology` must be a definition of a SOM with additional columns as in the output from `numero.subgroup()`. The function first looks for row names in `data` that are also included in `results`. The rows are then divided into subgroups according to the district assignments in `results` and the region labels in `topology`. ## Returns A data frame of summary statistics, see `nroSummary()` for details. The data frame also contains additional information on which variables were used for the training of the SOM. The attribute 'layout' is added to the output. It indicates the location on the map and the subgroup name and label for each data row that were included in the analysis. ## Author(s) Ville-Petteri Makinen ## Examples ```r # 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, data = trdata) # Evaluate map statistics for all variables. stats <- numero.evaluate(model = qc, data = dataset) # Define subgroups. x <- stats$planes[,"uALB"] tops <- which(x >= quantile(x, 0.75, na.rm=TRUE)) bottoms <- which(x <= quantile(x, 0.25, na.rm=TRUE)) elem <- data.frame(stats$map$topology, stringsAsFactors = FALSE) elem$REGION <- "MiddleAlb" elem$REGION[tops] <- "HighAlb" elem$REGION[bottoms] <- "LowAlb" elem$REGION.label <- "M" elem$REGION.label[tops] <- "H" elem$REGION.label[bottoms] <- "L" # Compare subgroups. cmp <- numero.summary(results = stats, topology = elem, data = dataset) ```

numero.summary function