phe_proportion: Calculate Proportions using phe_proportion

Description Usage Arguments Value Notes References See Also Examples

View source: R/Proportions.R

Description

Calculates proportions with confidence limits using Wilson Score method [1,2].

Usage

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phe_proportion(data, x, n, type = "full", confidence = 0.95, multiplier = 1)

Arguments

data

a data.frame containing the data to calculate proportions for, pre-grouped if proportions required for group aggregates; unquoted string; no default

x

field name from data containing the observed numbers of cases in the sample meeting the required condition (the numerator for the proportion); unquoted string; no default

n

field name from data containing the number of cases in the sample (the denominator for the proportion); unquoted string; no default

type

defines the data and metadata columns to be included in output; can be "value", "lower", "upper", "standard" (for all data) or "full" (for all data and metadata); quoted string; default = "full"

confidence

the required level of confidence expressed as a number between 0.9 and 1 or a number between 90 and 100 or can be a vector of 0.95 and 0.998, for example, to output both 95% and 99.8% CIs; numeric; default 0.95

multiplier

the multiplier used to express the final values (eg 100 = percentage); numeric; default 1

Value

When type = "full", returns the original data.frame with the following appended: proportion, lower confidence limit, upper confidence limit, confidence level, statistic and method

Notes

Wilson Score method [1,2] is applied using the wilson_lower and wilson_upper functions.

The percentage argument was deprecated in v1_1_0, please use multiplier argument instead

References

[1] Wilson EB. Probable inference, the law of succession, and statistical inference. J Am Stat Assoc; 1927; 22. Pg 209 to 212.
[2] Newcombe RG, Altman DG. Proportions and their differences. In Altman DG et al. (eds). Statistics with confidence (2nd edn). London: BMJ Books; 2000. Pg 46 to 48.

See Also

Other PHEindicatormethods package functions: phe_dsr(), phe_isr(), phe_life_expectancy(), phe_mean(), phe_quantile(), phe_rate(), phe_sii(), phe_smr()

Examples

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# ungrouped data frame
df <- data.frame(area = rep(c("Area1","Area2","Area3","Area4"), each=3),
                 numerator = c(NA,82,9,48, 6500,8200,10000,10000,8,7,750,900),
                 denominator = rep(c(100,10000,10000,10000), each=3))

phe_proportion(df, numerator, denominator)
phe_proportion(df, numerator, denominator, confidence=99.8)
phe_proportion(df, numerator, denominator, type="standard")
phe_proportion(df, numerator, denominator, confidence = c(0.95, 0.998))

# grouped data frame
library(dplyr)
dfg <- df %>% group_by(area)
phe_proportion(dfg, numerator, denominator, multiplier=100)

PHEindicatormethods documentation built on July 1, 2020, 6:01 p.m.