quantileCI() R function from [rcompanion]

Quantiles and confidence intervals

Calculates an estimate for a quantile and confidence intervals for a vector of discrete or continuous values


quantileCI(
  x,
  tau = 0.5,
  level = 0.95,
  method = "binomial",
  type = 3,
  digits = 3,
  ...
)

Arguments

x: The vector of observations. Can be an ordered factor as long as type

is 1 or 3.
tau: The quantile to use, e.g. 0.5 for median, 0.25 for 25th percentile.
level: The confidence interval to use, e.g. 0.95 for 95 percent confidence interval.
method: If "binomial", uses the binomial distribution the confidence limits. If "normal", uses the normal approximation to the binomial distribution.
type: The type value passed to the quantile function.
digits: The number of significant figures to use in output.
...: Other arguments, ignored.

Returns

A data frame of summary statistics, quantile estimate, and confidence limits.

Details

Conover recommends the "binomial" method for sample sizes less than or equal to 20. With the current implementation, this method can be used also for larger sample sizes.

Examples


### From Conover, Practical Nonparametric Statistics, 3rd
Hours = c(46.9, 47.2, 49.1, 56.5, 56.8, 59.2, 59.9, 63.2,
          63.3, 63.4, 63.7, 64.1, 67.1, 67.7, 73.3, 78.5)
quantileCI(Hours)

### Example with ordered factor
set.seed(12345)
Pool = factor(c("smallest", "small", "medium", "large", "largest"),
             ordered=TRUE, 
             levels=c("smallest", "small", "medium", "large", "largest"))
Sample = sample(Pool, 24, replace=TRUE)
quantileCI(Sample)