# plotBinconf: Function outputting a plot of confidence interval around a... In cpsyctc/CECPfuns: Package of Utility Functions for Psychological Therapies, Mental Health and Well-being Work (Created by Chris Evans and Clara Paz)

## Description

Function outputting a plot of confidence interval around a proportion for a range of sample sizes

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```plotBinconf(proportion, minN, maxN, step = 1, conf = 0.95, fixYlim = FALSE) plotCIProportion( proportion, minN, maxN, step = 1, conf = 0.95, fixYlim = FALSE ) ```

## Arguments

 `proportion` numeric: the proportion sought (actual proportion will be nearest possible for each n) `minN` numeric: the smallest sample size, n, to estimate and plot `maxN` numeric: the largest n `step` numeric: the steps to use between minN and maxN, defaults to 1 but set higher if plotting a wide range of n `conf` numeric: confidence interval width, usually .95 `fixYlim` logical: if FALSE, ggplot finds sensible y limits, if TRUE, y axis runs from 0 to 1

## Value

a ggplot object, by default that will print but you can save it and modify it as you like

## Background

This little function just plots confidence intervals (CIs) for a proportion for a range of sample sizes. I wrote it after writing `classifyScoresVectorByRCI` which will give CIs around observed proportions and I thought that for people not entirely familiar with and comfortable with CIs it might be useful for them to be able to see a plot of how intervals around observed proportions change with sample size. #'

## History/development log

Started before 5.iv.21

## Author(s)

Chris Evans

Other confidence interval functions: `plotCIcorrelation()`
Other demonstration functions: `plotCIcorrelation()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32``` ```## Not run: ### 95% CI around proportion .5 for n from 10 to 70 plotBinconf(.5, 10, 70, conf = .95) # don't have to declare conf, defaults to .95 ### notice that the observed proportion wiggles up and down as n increases as ### you can only have integer counts so functions gets nearest to the desired ### proportion, here .5, possible for that n, so for n = 10, we can have perfect .5 ### but for n = 11 6/11 is .545454.. ### 90% CI around proportion .5 for n from 10 to 70 plotBinconf(.5, 10, 70, conf = .90) ### 90% CI around proportion .5 for n from 100 to 200 plotBinconf(.5, 10, 70, conf = .90) ### same but fixing y limits to 0 and 1 plotBinconf(.5, 10, 70, conf = .90, fixYlim = TRUE) ### default 95% CI, exporting to tmpPlot and then changing plot plotBinconf(.5, 10, 70) -> tmpPlot tmpPlot + ggtitle("95% CI around proportion .5 for n from 10 to 70") + theme_bw() ### other inputs plotBinconf(0, .95, 10, 70) plotBinconf(1, .95, 10, 70) plotBinconf(.7, .95, 100, 200) plotBinconf(.3, .95, 100, 700, 5) ## End(Not run) ```