median
Median Value
Compute the sample median.
Usage
median(x, na.rm = FALSE, …)Arguments
- x
an object for which a method has been defined, or a numeric vector containing the values whose median is to be computed.
- na.rm
a logical value indicating whether
NAvalues should be stripped before the computation proceeds.- …
potentially further arguments for methods; not used in the default method.
Details
This is a generic function for which methods can be written. However,
the default method makes use of is.na, sort and
mean from package base all of which are generic, and so
the default method will work for most classes
(e.g., "Date") for which a median is a reasonable
concept.
Value
The default method returns a length-one object of the same type as
x, except when x is logical or integer of even length,
when the result will be double.
If there are no values or if na.rm = FALSE and there are NA
values the result is NA of the same type as x (or more
generally the result of x[FALSE][NA]).
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
See Also
quantile for general quantiles.
Examples
library(stats)
# NOT RUN {
median(1:4) # = 2.5 [even number]
median(c(1:3, 100, 1000)) # = 3 [odd, robust]
# }
Community examples
The median of a set with an number of values is the middle one, when sorted from smallest to largest. ```{r} x <- c(1, 10, 5, 8, 9) median(x) sort(x)[3] # same ``` If there are an even number of values, the median is half way between the two middle values. ```{r} x <- c(1, 10, 5, 8, 9, 6) median(x) sorted <- sort(x) (sorted[3] + sorted[4]) / 2 # same ``` If there are any missing values, the median is also missing. ```{r} median(c(1, 10, 5, 8, 9, NA)) ``` You can exclude missing values by setting `na.rm = TRUE`. ```{r} median(c(1, 10, 5, 8, 9, NA), na.rm = TRUE) ``` The median is known as a robust estimator of location, since it ignores outliers. The following dataset has 10% taken from a wide distribution that will generate many outliers. The expected mean and median are both zero. Which one is closest? ```{r} x <- c(rnorm(900), rnorm(100, sd = 1000)) mean(x) median(x) ```