Compositional Matrix

An S4 class to represent compositional data.
Slots

totals: A numeric vector to store the absolute row sums (before the closure of the compositions).
samples: A character vector to store the sample identifiers (allows duplicates in case of repeated measurements).
groups: A character vector to store the group names (if any).
Note

This class inherits from matrix.
Author

N. Frerebeau
Examples

## Coerce to compositional data
data("hongite")
coda <- as_composition(hongite)

## codaccess
dim(coda) # Get the matrix dimensions
#> [1] 25  5
row(coda) # Get the row indexes
#>       [,1] [,2] [,3] [,4] [,5]
#>  [1,]    1    1    1    1    1
#>  [2,]    2    2    2    2    2
#>  [3,]    3    3    3    3    3
#>  [4,]    4    4    4    4    4
#>  [5,]    5    5    5    5    5
#>  [6,]    6    6    6    6    6
#>  [7,]    7    7    7    7    7
#>  [8,]    8    8    8    8    8
#>  [9,]    9    9    9    9    9
#> [10,]   10   10   10   10   10
#> [11,]   11   11   11   11   11
#> [12,]   12   12   12   12   12
#> [13,]   13   13   13   13   13
#> [14,]   14   14   14   14   14
#> [15,]   15   15   15   15   15
#> [16,]   16   16   16   16   16
#> [17,]   17   17   17   17   17
#> [18,]   18   18   18   18   18
#> [19,]   19   19   19   19   19
#> [20,]   20   20   20   20   20
#> [21,]   21   21   21   21   21
#> [22,]   22   22   22   22   22
#> [23,]   23   23   23   23   23
#> [24,]   24   24   24   24   24
#> [25,]   25   25   25   25   25
col(coda, as.factor = TRUE) # Get the column indexes
#>       [,1] [,2] [,3] [,4] [,5]
#>  [1,] A    B    C    D    E   
#>  [2,] A    B    C    D    E   
#>  [3,] A    B    C    D    E   
#>  [4,] A    B    C    D    E   
#>  [5,] A    B    C    D    E   
#>  [6,] A    B    C    D    E   
#>  [7,] A    B    C    D    E   
#>  [8,] A    B    C    D    E   
#>  [9,] A    B    C    D    E   
#> [10,] A    B    C    D    E   
#> [11,] A    B    C    D    E   
#> [12,] A    B    C    D    E   
#> [13,] A    B    C    D    E   
#> [14,] A    B    C    D    E   
#> [15,] A    B    C    D    E   
#> [16,] A    B    C    D    E   
#> [17,] A    B    C    D    E   
#> [18,] A    B    C    D    E   
#> [19,] A    B    C    D    E   
#> [20,] A    B    C    D    E   
#> [21,] A    B    C    D    E   
#> [22,] A    B    C    D    E   
#> [23,] A    B    C    D    E   
#> [24,] A    B    C    D    E   
#> [25,] A    B    C    D    E   
#> Levels: A B C D E
nrow(coda) # Get the number of rows
#> [1] 25
ncol(coda) # Get the number of columns
#> [1] 5
dimnames(coda) # Get the dimension names
#> [[1]]
#>  [1] "H1"  "H2"  "H3"  "H4"  "H5"  "H6"  "H7"  "H8"  "H9"  "H10" "H11" "H12"
#> [13] "H13" "H14" "H15" "H16" "H17" "H18" "H19" "H20" "H21" "H22" "H23" "H24"
#> [25] "H25"
#> 
#> [[2]]
#> [1] "A" "B" "C" "D" "E"
#> 
rownames(coda) <- LETTERS[1:25] # Set the row names
rownames(coda) # Get the rownames
#>  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
#> [20] "T" "U" "V" "W" "X" "Y"
colnames(coda) <- letters[21:25] # Set the column names
colnames(coda) # Get the column names
#> [1] "u" "v" "w" "x" "y"

## Subset
coda[[1, 1]] # Get the first value
#> [1] 0.488
coda[1] # Get the first value
#> [1] 0.488
coda[, ] # Get all values
#> <CompositionMatrix: 25 x 5>
#>           u         v          w          x          y
#> A 0.4880000 0.3170000 0.03800000 0.06400000 0.09300000
#> B 0.4820000 0.2380000 0.09000000 0.09200000 0.09800000
#> C 0.3700000 0.0910000 0.34200000 0.09500000 0.10200000
#> D 0.5090000 0.2380000 0.07200000 0.10100000 0.08000000
#> E 0.4420000 0.3830000 0.02900000 0.07700000 0.06900000
#> F 0.5230000 0.2620000 0.04200000 0.12500000 0.04800000
#> G 0.4460000 0.3300000 0.04600000 0.12200000 0.05600000
#> H 0.3460000 0.0520000 0.42900000 0.09600000 0.07700000
#> I 0.4120000 0.1170000 0.26700000 0.09600000 0.10800000
#> J 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000
#> K 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000
#> L 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000
#> M 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000
#> N 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000
#> O 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000
#> P 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000
#> Q 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000
#> R 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306
#> S 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000
#> T 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000
#> U 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000
#> V 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000
#> W 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000
#> X 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000
#> Y 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000
coda[1, , drop = FALSE] # Get the first row
#> <CompositionMatrix: 1 x 5>
#>       u     v     w     x     y
#> A 0.488 0.317 0.038 0.064 0.093
coda[, 1:3] # Get the first three column
#> <CompositionMatrix: 25 x 3>
#>           u         v          w
#> A 0.4880000 0.3170000 0.03800000
#> B 0.4820000 0.2380000 0.09000000
#> C 0.3700000 0.0910000 0.34200000
#> D 0.5090000 0.2380000 0.07200000
#> E 0.4420000 0.3830000 0.02900000
#> F 0.5230000 0.2620000 0.04200000
#> G 0.4460000 0.3300000 0.04600000
#> H 0.3460000 0.0520000 0.42900000
#> I 0.4120000 0.1170000 0.26700000
#> J 0.4260000 0.4660000 0.00700000
#> K 0.4990000 0.1950000 0.11400000
#> L 0.4520000 0.3730000 0.02700000
#> M 0.3270000 0.0850000 0.38900000
#> N 0.4140000 0.1290000 0.23400000
#> O 0.4620000 0.1750000 0.15800000
#> P 0.3230000 0.0730000 0.40900000
#> Q 0.4320000 0.4430000 0.01000000
#> R 0.4954955 0.3233233 0.03103103
#> S 0.4230000 0.1580000 0.20400000
#> T 0.4460000 0.1150000 0.23800000
#> U 0.4580000 0.1660000 0.16800000
#> V 0.4990000 0.2500000 0.06800000
#> W 0.4860000 0.3400000 0.02500000
#> X 0.4550000 0.1660000 0.17600000
#> Y 0.4590000 0.2490000 0.09700000
Slots

Note

See also

Author

Examples
