summary.hotspots {hotspots}

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)