An S4 class to represent compositional data.
Note
This class inherits from matrix
.
See also
Other classes:
LogRatio-class
,
LogicalMatrix-class
,
NumericMatrix-class
,
OutlierIndex-class
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