stats::a_quantil
--
alpha-quantile of discrete datastats::a_quantil
(a, ..)
returns the
alpha-quantile of discrete data.
stats::a_quantil(a, x1, x2, ..)
stats::a_quantil(a, [x1, x2, ..])
stats::a_quantil(a, s <, c>)
a |
- | the alpha value: a rational number or a real float from the interval (0,1). |
x1, x2, .. |
- | the statistical data: real numerical values. |
s |
- | a sample of domain type stats::sample . |
c |
- | an integer representing a column index of the sample
s . This column provides the data x1 ,
x2 etc. |
an arithmetical expression. FAIL
is returned, if the
data are empty.
stats::geometric
,
stats::harmonic
,
stats::mean
, stats::median
, stats::modal
, stats::quadratic
, stats::stdev
, stats::variance
ceil
(k) is the next integer
larger than k.stats::a_quantil
sorts internally.PI +
sqrt(2)
, exp(-5)
etc. are converted to floating
point numbers.c
is optional, if the data are given
by a stats::sample
object containing only one non-string column. Cf. example 3.stats::median
.We calculate the 1/4-quantile of a sequence of five values:
>> stats::a_quantil(1/4, 3, 8, 5, 9/2, 11)
9/2
Alternatively, the data may be passed as a list:
>> stats::a_quantil(1/4, [3, 8, 5, 9/2, 11])
9/2
We create a sample:
>> stats::sample([[4, 7, 5], [3, 6, 17], [8, 2, 2]])
4 7 5 3 6 17 8 2 2
The 1/2-quantile (the median
) of the second column is
calculated:
>> stats::a_quantil(1/2, %, 2)
6
We create a sample consisting of one string column and one non-string column:
>> stats::sample([["1996", 1242], ["1997", 1353], ["1998", 1142]])
"1996" 1242 "1997" 1353 "1998" 1142
The 0.3-quantile of the second column is calculated. In this case this column does not have to be specified, since it is the only non-string column in the sample:
>> stats::a_quantil(0.3, %)
1142