Numero (version 1.2.0)

nroSummary: Estimate subgroup statistics

Description

Combine subgrouping information for districts with the data points that reside in the districts, and estimate statistics for each subgroup and variable.

Usage

nroSummary(data, districts, regions, categlim = 8, capacity = 10)

Arguments

data

A vector of M elements or an M x N matrix of data values.

districts

A vector of M best-matching districts for each row in the data matrix, please see nroMatch for a typical usage case.

regions

An vector of K elements or a data frame of K rows that defines if a district belongs to a larger region (i.e. a subgroup), see details.

categlim

The threshold for the number of unique values before a variable is considered continuous.

capacity

Maximum number of subgroups to compare.

Value

A data frame of summary statistics that contains a row for every combination of subgroups and variables. The chi-squared test is used for comparisons with respect to categorical variables, and rank-regulated t-test and ANOVA are applied to continuous variables. Region labels for each row are stored in the attribute "labels" and a list that contains the subsets of rows in each region is stored in the attribute "subgroups".

Details

The region vector must have K elements where K is the total number of map districts. The value at element [i] indicates the region for the district [i].

The region input can also be a data frame of K rows where the column REGION will be used for assigning district to regions, and REGION.label will be used as the character label as seen on the map, see the output from nroPlot() as an example.

Safeguards are in place to prevent crashes from empty categories; this reduces statistical power slightly when numbers are small.

Examples

# NOT RUN {
# Import data.
fname <- system.file("extdata", "finndiane.txt", package = "Numero")
dataset <- read.delim(file = fname)

# 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)

# Calculate district averages for urinary albumin.
plane <- nroAggregate(topology = sm, districts = matches,
                      data = dataset$uALB)

# Assign subgroups based on urinary albumin.
regns <- rep("HighAlb", length.out=length(plane))
regns[which(plane < quantile(plane, 0.67))] <- "MiddleAlb"
regns[which(plane < quantile(plane, 0.33))] <- "LowAlb"

# Calculate summary statistics.
st <- nroSummary(data = dataset, districts = matches, regions = regns)
print(st[,c("VARIABLE","SUBGROUP","MEAN","P.chisq","P.t","P.anova")])
# }