summary.hotspots: Summarizing hot spot and outlier cutoffs

In hotspots: Hot Spots

Summarizing hot spot and outlier cutoffs

Description

summary method for class "hotspots".

Usage

## S3 method for "hotspots" objects
## S3 method for class 'hotspots'
summary(object, ...)

## S3 method for "summary.hotspots" objects
## S3 method for class 'summary.hotspots'
print(x, digits = max(3, getOption("digits") - 3), p_round = 1, top = 0, ...)

Arguments

object	"hotspots" object
x	"summary.hotspots" object
digits	the number of significant digits to use when printing
p_round	the number of decimal places to print for percentages when printing
top	the number of the most disproportionate (highest or lowest) data values to print with their percent contributions to the total
...	further arguments passed to or from other methods

Details

The importance of hot spots within the data is evaluated by reporting the number of hot spots, the percentage of values that are hot spots, and the percent of the sum of values attributable to hot spots. The percent of the sum of values is likely only relevant if the data are either all positive or all negative. A warning is given if they are not.

Value

A summary.hotspots object is a list containing all of the objects in a hotspots object as well as the following:

num_phs	number of positive hot spots or outliers in data
percent_phs	percent of values identified as positive hot spots or outliers
percent_phs_sum	percent of the sum of the values attributable to positive hot spots or outliers
num_nhs	number of negative hot spots or outliers in data
percent_nhs	percent of values identified as negative hot spots or outliers
percent_nhs_sum	percent of the sum of the values attributable to negative hot spots or outliers
m	A list of summary statistics pertaining to the data (mean, median, min, max, scale (determined by the argument 'var.est in the hotspots function), and coefficient of variation (scale/median)

)

disprop

vector of levels of disproportionality as calculated by disprop

Author(s)

Anthony Darrouzet-Nardi

See Also

hotspots, plot.hotspots, disprop

Examples

rln100.sum <- summary(hotspots(rlnorm(101), tail = "both"))
rln100.sum 
print(rln100.sum, top = 10, p_round = 0)

hotspots documentation built on April 4, 2025, 2:21 a.m.