`QuantileCI.Rd`

Calculates the confidence interval for any quantile. Although bootstrapping might be a good approach for getting senisble confidence intervals there's sometimes need to have a nonparameteric alternative. This function offers one.

- x
a (non-empty) numeric vector of data values.

- probs
numeric vector of probabilities with values in

*[0,1]*. (Values up to`2e-14`

outside that range are accepted and moved to the nearby endpoint.)- conf.level
confidence level of the interval

- sides
a character string specifying the side of the confidence interval, must be one of

`"two.sided"`

(default),`"left"`

or`"right"`

(abbreviations allowed).`"left"`

would be analogue to a`"greater"`

hypothesis in a`t.test`

.- na.rm
logical. Should missing values be removed? Defaults to

`FALSE`

.- method
defining the type of interval that should be calculated (one out of

`"exact"`

,`"boot"`

). Default is`"exact"`

. See Details.- R
The number of bootstrap replicates. Usually this will be a single positive integer. See

`boot.ci`

for details.

The `"exact"`

method corresponds to the way the confidence interval for the median is calculated in SAS.

The boot confidence interval type is calculated by means of `boot.ci`

with default type `"basic"`

.

if probs was of length 1 a numeric vector with 3 elements:

- est
est

- lwr.ci
lower bound of the confidence interval

- upr.ci
upper bound of the confidence interval

or, if probs was a vector, a matrix with 3 columns consisting of estimate, lower ci, upper ci
`est, lwr.ci, upr.ci`

```
QuantileCI(d.pizza$price, probs=0.25, na.rm=TRUE)
#> est lwr.ci upr.ci
#> 30.98 29.98 33.98
#> attr(,"conf.level")
#> [1] 0.9502599
QuantileCI(d.pizza$price, na.rm=TRUE)
#> est lwr.ci upr.ci
#> 0% 8.792 NA 8.792
#> 25% 30.980 29.9800 33.980
#> 50% 46.764 44.9700 47.970
#> 75% 63.180 61.0056 65.655
#> 100% 134.334 124.4340 NA
#> attr(,"conf.level")
#> [1] 1.0000000 0.9502599 0.9503030 0.9502599 1.0000000
QuantileCI(d.pizza$price, conf.level=0.99, na.rm=TRUE)
#> est lwr.ci upr.ci
#> 0% 8.792 NA 8.792
#> 25% 30.980 29.980 35.176
#> 50% 46.764 44.970 47.970
#> 75% 63.180 59.660 66.360
#> 100% 134.334 124.434 NA
#> attr(,"conf.level")
#> [1] 1.0000000 0.9901407 0.9902694 0.9901407 1.0000000
# multiple probs
QuantileCI(1:100, method="exact" , probs = c(0.25, 0.75, .80, 0.95))
#> est lwr.ci upr.ci
#> 25% 25.75 17 34
#> 75% 75.25 67 84
#> 80% 80.20 73 89
#> 95% 95.05 90 99
#> attr(,"conf.level")
#> [1] 0.9512948 0.9512948 0.9532735 0.9514464
QuantileCI(1:100, method="boot" , probs = c(0.25, 0.75, .80, 0.95))
#> est lwr.ci upr.ci
#> 25% 25.75 16.25 34.00
#> 75% 75.25 67.50 85.25
#> 80% 80.20 73.20 89.20
#> 95% 95.05 92.05 100.90
```