# Function returns a tibble of quantiles and confidence interval of quantiles for a range of probabilities. '

`getCIforQuantiles.Rd`

Function returns a tibble of quantiles and confidence interval of quantiles for a range of probabilities. '

## Usage

```
getCIforQuantiles(
vecDat = NA,
vecQuantiles = NA,
method = c("Nyblom", "Exact", "Bootstrap"),
ci = 0.95,
R = 9999,
type = NULL
)
```

## Arguments

- vecDat
Vector of the data

- vecQuantiles
Vector of the quantiles you want

- method
Method you want to use to get the confidence intervals

- ci
Confidence interval (default .95)

- R
Number of bootstrap replications if using bootstrap method

- type
Type of quantile (default is 8)

## Value

A tibble of the results with variables: prob: the quantile required, i.e. .05, .5 would give the median, .95 the 95th (per)centile n: total n nOK: number of non-missing values nMiss: number of missing values quantile: the quantile for that value of prob LCL: lower confidence limit for that quantile UCL: upper confidence limt for that quantile

## Background

Quantiles, like percentiles, can be a very useful way of understanding a distribution, in MH research, typically a distribution of scores, often either help-seeking sample distribution or a non-help-seeking sample.

For example, you might want to see if a score you have for someone is above the 95% (per)centile, i.e. above the .95 quantile of the non-help-seeking sample distribution.

The catch is that, like any sample statistic, a quantile/percentile is generally being used as an estimate of a population value. That is to say we are really interested in whether the score we have is above the 95% percentile of a non-help-seeking population. That means that the quantiles/percentiles from a sample are only approximate guides to the population values.

As usual, a confidence interval (CI) around an observed quantile is a way of seeing how precisely, assuming random sampling of course, the sample quantile can guide us about the population quantile.

Again as usual, the catch is that there are different ways of compute the CI around a quantile. This function is a wrapper around three methods from Michael Höhle's quantileCI package:

the Nyblom method

the "exact" method

the bootstrap method

As I understand the quantileCI package the classic paper by Nyblom, generally the Nyblom method will be as good or better than the exact method and much faster than bootstrapping so the Nyblom method is the default here but I have put the others in for completeness.

## References

Nyblom, J. (1992). Note on interpolated order statistics. Statistics & Probability Letters, 14(2), 129–131. https://doi.org/10.1016/0167-7152(92)90076-H

https://github.com/mhoehle/quantileCI

## See also

Other CI functions, quantile functions:
`plotQuantileCIsfromDat()`

## Examples

```
if (FALSE) {
### will need tidyverse to run
library(tidyverse)
### will need quantileCI to run
### if you don't have quantileCI package you need to get it from github:
# devtools::install_github("hoehleatsu/quantileCI")
library(quantileCI)
### use the exact method
getCIforQuantiles(1:1000, c(.1, .5, .95), "e", ci = .95, R = 9999, type = 8)
### use the Nyblom method
getCIforQuantiles(1:1000, c(.1, .5, .95), "n", ci = .95, R = 9999, type = 8)
### use the bootstrap method
getCIforQuantiles(1:1000, c(.1, .5, .95), "b", ci = .95, R = 9999, type = 8)
}
```