Utility functions for factors and compositional data

Utility functions for factors and compositional data.

Usage

compare_sets(x, y)
find_max(x)
find_min(x)
reclass(x, map, all = FALSE, allow_NA = FALSE)
redistribute(x, source, target = NULL)

Arguments

x, y: any type for compare_sets, matrix for find_max, find_min, and redistribute, a factor for reclass.
map: a reclassification matrix with 2 columns (1st: original levels, 2nd: output levels mapped to original levels).
all: logical, whether all levels from mapping matrix should be applied on the return object.
allow_NA: logical, whether NAs are allowed as part of map.
source: numeric or character, single column index for input matrix x.
target: numeric or character, column index or indices for input matrix x.

Value

A matrix compare_sets.

A data frame for find_max and find_min.

A reclassified factor for reclass.

A matrix for redistribute where the source column values are redistributed among the target columns proportionally.

Author

Peter Solymos <solymos@ualberta.ca>

Examples

## numeric vector
compare_sets(1:10, 8:15)
#>        xlength ylength intersect union xbutnoty ybutnotx
#> labels      10       8         3    15        7        5
#> unique      10       8         3    15        7        5
## factor with 'zombie' labels
compare_sets(factor(1:10, levels=1:10), factor(8:15, levels=1:15))
#>        xlength ylength intersect union xbutnoty ybutnotx
#> labels      10      15        10    15        0        5
#> unique      10       8         3    15        7        5

(mat <- matrix(rnorm(10*5), 10, 5))
#>             [,1]        [,2]        [,3]        [,4]        [,5]
#>  [1,]  1.1484116 -1.86301149 -0.09744510 -0.13399701  2.75541758
#>  [2,] -1.8218177 -0.52201251 -0.93584735 -1.91008747  0.04653138
#>  [3,] -0.2473253 -0.05260191 -0.01595031 -0.27923724  0.57770907
#>  [4,] -0.2441996  0.54299634 -0.82678895 -0.31344598  0.11819487
#>  [5,] -0.2827054 -0.91407483 -1.51239965  1.06730788 -1.91172049
#>  [6,] -0.5536994  0.46815442  0.93536319  0.07003485  0.86208648
#>  [7,]  0.6289820  0.36295126  0.17648861 -0.63912332 -0.24323674
#>  [8,]  2.0650249 -1.30454355  0.24368546 -0.04996490 -0.20608719
#>  [9,] -1.6309894  0.73777632  1.62354888 -0.25148344  0.01917759
#> [10,]  0.5124269  1.88850493  0.11203808  0.44479712  0.02956075
(m <- find_max(mat))
#>    index      value
#> 1     X5 2.75541758
#> 2     X5 0.04653138
#> 3     X5 0.57770907
#> 4     X2 0.54299634
#> 5     X4 1.06730788
#> 6     X3 0.93536319
#> 7     X1 0.62898204
#> 8     X1 2.06502490
#> 9     X3 1.62354888
#> 10    X2 1.88850493
## column indices
as.integer(m$index)
#>  [1] 5 5 5 2 4 3 1 1 3 2
find_min(mat)
#>    index       value
#> 1     X2 -1.86301149
#> 2     X4 -1.91008747
#> 3     X4 -0.27923724
#> 4     X3 -0.82678895
#> 5     X5 -1.91172049
#> 6     X1 -0.55369938
#> 7     X4 -0.63912332
#> 8     X2 -1.30454355
#> 9     X1 -1.63098940
#> 10    X5  0.02956075

map <- cbind(c("a","b","c","d","e","f","g"),
             c("A","B","B","C","D","D","E"))
#x <- factor(sample(map[1:6,1], 100, replace=TRUE), levels=map[,1])
x <- as.factor(sample(map[1:6,1], 100, replace=TRUE))
x[2] <- NA
table(x, reclass(x, map, all = FALSE), useNA="always")
#>       
#> x       A  B  C  D <NA>
#>   a    22  0  0  0    0
#>   b     0 15  0  0    0
#>   c     0 18  0  0    0
#>   d     0  0 16  0    0
#>   e     0  0  0 19    0
#>   f     0  0  0  9    0
#>   <NA>  0  0  0  0    1
table(x, reclass(x, map, all = TRUE), useNA="always")
#>       
#> x       A  B  C  D  E <NA>
#>   a    22  0  0  0  0    0
#>   b     0 15  0  0  0    0
#>   c     0 18  0  0  0    0
#>   d     0  0 16  0  0    0
#>   e     0  0  0 19  0    0
#>   f     0  0  0  9  0    0
#>   <NA>  0  0  0  0  0    1

map[c(4, 7), 2] <- NA
table(x, reclass(x, map, all = FALSE, allow_NA = TRUE), useNA="always")
#>       
#> x       A  B  D <NA>
#>   a    22  0  0    0
#>   b     0 15  0    0
#>   c     0 18  0    0
#>   d     0  0  0   16
#>   e     0  0 19    0
#>   f     0  0  9    0
#>   <NA>  0  0  0    1
table(x, reclass(x, map, all = TRUE, allow_NA = TRUE), useNA="always")
#>       
#> x       A  B  D <NA>
#>   a    22  0  0    0
#>   b     0 15  0    0
#>   c     0 18  0    0
#>   d     0  0  0   16
#>   e     0  0 19    0
#>   f     0  0  9    0
#>   <NA>  0  0  0    1

(mat2 <- exp(mat) / rowSums(exp(mat)))
#>             [,1]        [,2]       [,3]       [,4]       [,5]
#>  [1,] 0.15146605 0.007455399 0.04357593 0.04201190 0.75549072
#>  [2,] 0.06902728 0.253232229 0.16741439 0.06319543 0.44713066
#>  [3,] 0.14867992 0.180642369 0.18738603 0.14401016 0.33928152
#>  [4,] 0.16325093 0.358699287 0.09116756 0.15232891 0.23455332
#>  [5,] 0.17013039 0.090486058 0.04974306 0.65627407 0.03336642
#>  [6,] 0.07043799 0.195700726 0.31224725 0.13142903 0.29018500
#>  [7,] 0.32238898 0.247083922 0.20505237 0.09070872 0.13476601
#>  [8,] 0.70420292 0.024227737 0.11394628 0.08495118 0.07267188
#>  [9,] 0.02138002 0.228428375 0.55390615 0.08494151 0.11134394
#> [10,] 0.13925597 0.551362081 0.09330978 0.13014952 0.08592266
(rmat2 <- redistribute(mat2, source = 1, target = 2:4))
#>       [,1]       [,2]       [,3]       [,4]       [,5]
#>  [1,]    0 0.01959212 0.11451364 0.11040352 0.75549072
#>  [2,]    0 0.28935958 0.19129855 0.07221120 0.44713066
#>  [3,]    0 0.23309524 0.24179705 0.18582619 0.33928152
#>  [4,]    0 0.45594008 0.11588242 0.19362418 0.23455332
#>  [5,]    0 0.10981357 0.06036801 0.79645199 0.03336642
#>  [6,]    0 0.21726041 0.34664647 0.14590812 0.29018500
#>  [7,]    0 0.39382403 0.32683045 0.14457951 0.13476601
#>  [8,]    0 0.10069262 0.47357085 0.35306465 0.07267188
#>  [9,]    0 0.23405957 0.56756100 0.08703548 0.11134394
#> [10,]    0 0.65045648 0.11008002 0.15354084 0.08592266
colMeans(mat2)
#> [1] 0.1960220 0.2137318 0.1817749 0.1580000 0.2504712
colMeans(rmat2)
#> [1] 0.0000000 0.2704094 0.2548548 0.2242646 0.2504712
stopifnot(abs(sum(mat2) - sum(rmat2)) < 10^-6)