rank
Ranks of Data
Description
Returns a vector of the ranks of the input.
Usage
rank(x, na.last = TRUE, ties.method = c("average", "first", "last",
"random", "max", "min"))
Arguments
| x |
a numeric vector.
Missing values (NAs) are allowed.
|
| na.last |
a vector with one element.
- If na.last = TRUE, missing values are placed last.
- If na.last = FALSE, missing values are placed first.
- If na.last = NA, missing values are deleted.
- If na.last = "keep", the rank of any missing value is returned as NA.
|
| ties.method |
a character string. Can be one of the following:
- "average" indicates that all ties of a specific value are
assigned the same rank value: the average of the positions of the
ties in the sorted vector.
- "first" indicates that the ties are assigned ranks from
the sequence position.first:position.last, where
position.first is the first position of the ties in the
sorted vector and position.last is the last.
- "last" indicates that the ties are assigned ranks from
the sequence position.last:position.first.
- "random" indicates that the ties are assigned the
ranks sample(position.first:position.last).
- "max" indicates that the ties are assigned the rank
position.last.
- "min" indicates that the ties are assigned the rank
position.first. This rank is used in many reports of sport
results.
|
Details
The treatment of missing values is controlled by na.last.
For information about estimating the rank of a matrix, see svd,
qr or chol.
Value
returns the ranks. That is, the ith value is the rank of
x[i]. In case of ties, rank is assigned depending on the
ties.method.
Warning
If na.last=NA, the ith value of the result corresponds
to the ith element of the vector that is the result of
removing NAs from x.
See Also
Examples
# Create sample objects:
testscores <- matrix(sample(30:98, 60, replace = TRUE), 30, 2)
diffgeom <- testscores[,1]
complexanal <- testscores[,2]
rank(diffgeom)
# Spearman's rank correlation between two sets of testscores:
cor(rank(diffgeom), rank(complexanal))
# Create sample objects with missing values:
a <- c(4, 2, 5, 1, 4, NA, 6)
b <- c(4, 2, 5, NA, 4, 5, 6)
# This does not correctly compute the Spearman's rank correlation of the
# non-missing data:
cor(rank(a), rank(b))
# [1] 0.1651446
# This does compute the Spearman's rank correlation of non-missing data:
cor(rank(a[!is.na(a*b)]),rank(b[!is.na(a*b)])) #
# [1] 1