A class to hold data for dimensionality reduction and methods.
Usage
# S4 method for dimRedData
as.data.frame(x, meta.prefix = "meta.", data.prefix = "")
# S4 method for dimRedData
getData(object)
# S4 method for dimRedData
getMeta(object)
# S4 method for dimRedData
nrow(x)
# S4 method for dimRedData,ANY,ANY,ANY
[(x, i)
# S4 method for dimRedData
ndims(object)Arguments
- x
Of class dimRedData
- meta.prefix
Prefix for the columns of the meta data names.
- data.prefix
Prefix for the columns of the variable names.
- object
Of class dimRedData.
- i
a valid index for subsetting rows.
Details
The class hast two slots, data and meta. The
data slot contains a numeric matrix with variables in
columns and observations in rows. The meta slot may contain
a data.frame with additional information. Both slots need to
have the same number of rows or the meta slot needs to
contain an empty data.frame.
See examples for easy conversion from and to data.frame.
For plotting functions see plot.dimRedData.
Methods (by generic)
as.data.frame(dimRedData): convert to data.framegetData(dimRedData): Get the data slot.getMeta(dimRedData): Get the meta slot.nrow(dimRedData): Get the number of observations.x[i: Subset rows.ndims(dimRedData): Extract the number of Variables from the data.
Slots
dataof class
matrix, holds the data, observations in rows, variables in columnsmetaof class
data.frame, holds meta data such as classes, internal manifold coordinates, or simply additional data of the data set. Must have the same number of rows as thedataslot or be an empty data frame.
See also
Other dimRedData:
as.dimRedData()
Other dimRedData:
as.dimRedData()
Examples
## Load an example data set:
s3d <- loadDataSet("3D S Curve")
## Create using a constructor:
### without meta information:
dimRedData(iris[, 1:4])
#> An object of class "dimRedData"
#> Slot "data":
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> [1,] 5.1 3.5 1.4 0.2
#> [2,] 4.9 3.0 1.4 0.2
#> [3,] 4.7 3.2 1.3 0.2
#> [4,] 4.6 3.1 1.5 0.2
#> [5,] 5.0 3.6 1.4 0.2
#> [6,] 5.4 3.9 1.7 0.4
#> [7,] 4.6 3.4 1.4 0.3
#> [8,] 5.0 3.4 1.5 0.2
#> [9,] 4.4 2.9 1.4 0.2
#> [10,] 4.9 3.1 1.5 0.1
#> [11,] 5.4 3.7 1.5 0.2
#> [12,] 4.8 3.4 1.6 0.2
#> [13,] 4.8 3.0 1.4 0.1
#> [14,] 4.3 3.0 1.1 0.1
#> [15,] 5.8 4.0 1.2 0.2
#> [16,] 5.7 4.4 1.5 0.4
#> [17,] 5.4 3.9 1.3 0.4
#> [18,] 5.1 3.5 1.4 0.3
#> [19,] 5.7 3.8 1.7 0.3
#> [20,] 5.1 3.8 1.5 0.3
#> [21,] 5.4 3.4 1.7 0.2
#> [22,] 5.1 3.7 1.5 0.4
#> [23,] 4.6 3.6 1.0 0.2
#> [24,] 5.1 3.3 1.7 0.5
#> [25,] 4.8 3.4 1.9 0.2
#> [26,] 5.0 3.0 1.6 0.2
#> [27,] 5.0 3.4 1.6 0.4
#> [28,] 5.2 3.5 1.5 0.2
#> [29,] 5.2 3.4 1.4 0.2
#> [30,] 4.7 3.2 1.6 0.2
#> [31,] 4.8 3.1 1.6 0.2
#> [32,] 5.4 3.4 1.5 0.4
#> [33,] 5.2 4.1 1.5 0.1
#> [34,] 5.5 4.2 1.4 0.2
#> [35,] 4.9 3.1 1.5 0.2
#> [36,] 5.0 3.2 1.2 0.2
#> [37,] 5.5 3.5 1.3 0.2
#> [38,] 4.9 3.6 1.4 0.1
#> [39,] 4.4 3.0 1.3 0.2
#> [40,] 5.1 3.4 1.5 0.2
#> [41,] 5.0 3.5 1.3 0.3
#> [42,] 4.5 2.3 1.3 0.3
#> [43,] 4.4 3.2 1.3 0.2
#> [44,] 5.0 3.5 1.6 0.6
#> [45,] 5.1 3.8 1.9 0.4
#> [46,] 4.8 3.0 1.4 0.3
#> [47,] 5.1 3.8 1.6 0.2
#> [48,] 4.6 3.2 1.4 0.2
#> [49,] 5.3 3.7 1.5 0.2
#> [50,] 5.0 3.3 1.4 0.2
#> [51,] 7.0 3.2 4.7 1.4
#> [52,] 6.4 3.2 4.5 1.5
#> [53,] 6.9 3.1 4.9 1.5
#> [54,] 5.5 2.3 4.0 1.3
#> [55,] 6.5 2.8 4.6 1.5
#> [56,] 5.7 2.8 4.5 1.3
#> [57,] 6.3 3.3 4.7 1.6
#> [58,] 4.9 2.4 3.3 1.0
#> [59,] 6.6 2.9 4.6 1.3
#> [60,] 5.2 2.7 3.9 1.4
#> [61,] 5.0 2.0 3.5 1.0
#> [62,] 5.9 3.0 4.2 1.5
#> [63,] 6.0 2.2 4.0 1.0
#> [64,] 6.1 2.9 4.7 1.4
#> [65,] 5.6 2.9 3.6 1.3
#> [66,] 6.7 3.1 4.4 1.4
#> [67,] 5.6 3.0 4.5 1.5
#> [68,] 5.8 2.7 4.1 1.0
#> [69,] 6.2 2.2 4.5 1.5
#> [70,] 5.6 2.5 3.9 1.1
#> [71,] 5.9 3.2 4.8 1.8
#> [72,] 6.1 2.8 4.0 1.3
#> [73,] 6.3 2.5 4.9 1.5
#> [74,] 6.1 2.8 4.7 1.2
#> [75,] 6.4 2.9 4.3 1.3
#> [76,] 6.6 3.0 4.4 1.4
#> [77,] 6.8 2.8 4.8 1.4
#> [78,] 6.7 3.0 5.0 1.7
#> [79,] 6.0 2.9 4.5 1.5
#> [80,] 5.7 2.6 3.5 1.0
#> [81,] 5.5 2.4 3.8 1.1
#> [82,] 5.5 2.4 3.7 1.0
#> [83,] 5.8 2.7 3.9 1.2
#> [84,] 6.0 2.7 5.1 1.6
#> [85,] 5.4 3.0 4.5 1.5
#> [86,] 6.0 3.4 4.5 1.6
#> [87,] 6.7 3.1 4.7 1.5
#> [88,] 6.3 2.3 4.4 1.3
#> [89,] 5.6 3.0 4.1 1.3
#> [90,] 5.5 2.5 4.0 1.3
#> [91,] 5.5 2.6 4.4 1.2
#> [92,] 6.1 3.0 4.6 1.4
#> [93,] 5.8 2.6 4.0 1.2
#> [94,] 5.0 2.3 3.3 1.0
#> [95,] 5.6 2.7 4.2 1.3
#> [96,] 5.7 3.0 4.2 1.2
#> [97,] 5.7 2.9 4.2 1.3
#> [98,] 6.2 2.9 4.3 1.3
#> [99,] 5.1 2.5 3.0 1.1
#> [100,] 5.7 2.8 4.1 1.3
#> [101,] 6.3 3.3 6.0 2.5
#> [102,] 5.8 2.7 5.1 1.9
#> [103,] 7.1 3.0 5.9 2.1
#> [104,] 6.3 2.9 5.6 1.8
#> [105,] 6.5 3.0 5.8 2.2
#> [106,] 7.6 3.0 6.6 2.1
#> [107,] 4.9 2.5 4.5 1.7
#> [108,] 7.3 2.9 6.3 1.8
#> [109,] 6.7 2.5 5.8 1.8
#> [110,] 7.2 3.6 6.1 2.5
#> [111,] 6.5 3.2 5.1 2.0
#> [112,] 6.4 2.7 5.3 1.9
#> [113,] 6.8 3.0 5.5 2.1
#> [114,] 5.7 2.5 5.0 2.0
#> [115,] 5.8 2.8 5.1 2.4
#> [116,] 6.4 3.2 5.3 2.3
#> [117,] 6.5 3.0 5.5 1.8
#> [118,] 7.7 3.8 6.7 2.2
#> [119,] 7.7 2.6 6.9 2.3
#> [120,] 6.0 2.2 5.0 1.5
#> [121,] 6.9 3.2 5.7 2.3
#> [122,] 5.6 2.8 4.9 2.0
#> [123,] 7.7 2.8 6.7 2.0
#> [124,] 6.3 2.7 4.9 1.8
#> [125,] 6.7 3.3 5.7 2.1
#> [126,] 7.2 3.2 6.0 1.8
#> [127,] 6.2 2.8 4.8 1.8
#> [128,] 6.1 3.0 4.9 1.8
#> [129,] 6.4 2.8 5.6 2.1
#> [130,] 7.2 3.0 5.8 1.6
#> [131,] 7.4 2.8 6.1 1.9
#> [132,] 7.9 3.8 6.4 2.0
#> [133,] 6.4 2.8 5.6 2.2
#> [134,] 6.3 2.8 5.1 1.5
#> [135,] 6.1 2.6 5.6 1.4
#> [136,] 7.7 3.0 6.1 2.3
#> [137,] 6.3 3.4 5.6 2.4
#> [138,] 6.4 3.1 5.5 1.8
#> [139,] 6.0 3.0 4.8 1.8
#> [140,] 6.9 3.1 5.4 2.1
#> [141,] 6.7 3.1 5.6 2.4
#> [142,] 6.9 3.1 5.1 2.3
#> [143,] 5.8 2.7 5.1 1.9
#> [144,] 6.8 3.2 5.9 2.3
#> [145,] 6.7 3.3 5.7 2.5
#> [146,] 6.7 3.0 5.2 2.3
#> [147,] 6.3 2.5 5.0 1.9
#> [148,] 6.5 3.0 5.2 2.0
#> [149,] 6.2 3.4 5.4 2.3
#> [150,] 5.9 3.0 5.1 1.8
#>
#> Slot "meta":
#> data frame with 0 columns and 0 rows
#>
### with meta information:
dimRedData(iris[, 1:4], iris[, 5])
#> An object of class "dimRedData"
#> Slot "data":
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> [1,] 5.1 3.5 1.4 0.2
#> [2,] 4.9 3.0 1.4 0.2
#> [3,] 4.7 3.2 1.3 0.2
#> [4,] 4.6 3.1 1.5 0.2
#> [5,] 5.0 3.6 1.4 0.2
#> [6,] 5.4 3.9 1.7 0.4
#> [7,] 4.6 3.4 1.4 0.3
#> [8,] 5.0 3.4 1.5 0.2
#> [9,] 4.4 2.9 1.4 0.2
#> [10,] 4.9 3.1 1.5 0.1
#> [11,] 5.4 3.7 1.5 0.2
#> [12,] 4.8 3.4 1.6 0.2
#> [13,] 4.8 3.0 1.4 0.1
#> [14,] 4.3 3.0 1.1 0.1
#> [15,] 5.8 4.0 1.2 0.2
#> [16,] 5.7 4.4 1.5 0.4
#> [17,] 5.4 3.9 1.3 0.4
#> [18,] 5.1 3.5 1.4 0.3
#> [19,] 5.7 3.8 1.7 0.3
#> [20,] 5.1 3.8 1.5 0.3
#> [21,] 5.4 3.4 1.7 0.2
#> [22,] 5.1 3.7 1.5 0.4
#> [23,] 4.6 3.6 1.0 0.2
#> [24,] 5.1 3.3 1.7 0.5
#> [25,] 4.8 3.4 1.9 0.2
#> [26,] 5.0 3.0 1.6 0.2
#> [27,] 5.0 3.4 1.6 0.4
#> [28,] 5.2 3.5 1.5 0.2
#> [29,] 5.2 3.4 1.4 0.2
#> [30,] 4.7 3.2 1.6 0.2
#> [31,] 4.8 3.1 1.6 0.2
#> [32,] 5.4 3.4 1.5 0.4
#> [33,] 5.2 4.1 1.5 0.1
#> [34,] 5.5 4.2 1.4 0.2
#> [35,] 4.9 3.1 1.5 0.2
#> [36,] 5.0 3.2 1.2 0.2
#> [37,] 5.5 3.5 1.3 0.2
#> [38,] 4.9 3.6 1.4 0.1
#> [39,] 4.4 3.0 1.3 0.2
#> [40,] 5.1 3.4 1.5 0.2
#> [41,] 5.0 3.5 1.3 0.3
#> [42,] 4.5 2.3 1.3 0.3
#> [43,] 4.4 3.2 1.3 0.2
#> [44,] 5.0 3.5 1.6 0.6
#> [45,] 5.1 3.8 1.9 0.4
#> [46,] 4.8 3.0 1.4 0.3
#> [47,] 5.1 3.8 1.6 0.2
#> [48,] 4.6 3.2 1.4 0.2
#> [49,] 5.3 3.7 1.5 0.2
#> [50,] 5.0 3.3 1.4 0.2
#> [51,] 7.0 3.2 4.7 1.4
#> [52,] 6.4 3.2 4.5 1.5
#> [53,] 6.9 3.1 4.9 1.5
#> [54,] 5.5 2.3 4.0 1.3
#> [55,] 6.5 2.8 4.6 1.5
#> [56,] 5.7 2.8 4.5 1.3
#> [57,] 6.3 3.3 4.7 1.6
#> [58,] 4.9 2.4 3.3 1.0
#> [59,] 6.6 2.9 4.6 1.3
#> [60,] 5.2 2.7 3.9 1.4
#> [61,] 5.0 2.0 3.5 1.0
#> [62,] 5.9 3.0 4.2 1.5
#> [63,] 6.0 2.2 4.0 1.0
#> [64,] 6.1 2.9 4.7 1.4
#> [65,] 5.6 2.9 3.6 1.3
#> [66,] 6.7 3.1 4.4 1.4
#> [67,] 5.6 3.0 4.5 1.5
#> [68,] 5.8 2.7 4.1 1.0
#> [69,] 6.2 2.2 4.5 1.5
#> [70,] 5.6 2.5 3.9 1.1
#> [71,] 5.9 3.2 4.8 1.8
#> [72,] 6.1 2.8 4.0 1.3
#> [73,] 6.3 2.5 4.9 1.5
#> [74,] 6.1 2.8 4.7 1.2
#> [75,] 6.4 2.9 4.3 1.3
#> [76,] 6.6 3.0 4.4 1.4
#> [77,] 6.8 2.8 4.8 1.4
#> [78,] 6.7 3.0 5.0 1.7
#> [79,] 6.0 2.9 4.5 1.5
#> [80,] 5.7 2.6 3.5 1.0
#> [81,] 5.5 2.4 3.8 1.1
#> [82,] 5.5 2.4 3.7 1.0
#> [83,] 5.8 2.7 3.9 1.2
#> [84,] 6.0 2.7 5.1 1.6
#> [85,] 5.4 3.0 4.5 1.5
#> [86,] 6.0 3.4 4.5 1.6
#> [87,] 6.7 3.1 4.7 1.5
#> [88,] 6.3 2.3 4.4 1.3
#> [89,] 5.6 3.0 4.1 1.3
#> [90,] 5.5 2.5 4.0 1.3
#> [91,] 5.5 2.6 4.4 1.2
#> [92,] 6.1 3.0 4.6 1.4
#> [93,] 5.8 2.6 4.0 1.2
#> [94,] 5.0 2.3 3.3 1.0
#> [95,] 5.6 2.7 4.2 1.3
#> [96,] 5.7 3.0 4.2 1.2
#> [97,] 5.7 2.9 4.2 1.3
#> [98,] 6.2 2.9 4.3 1.3
#> [99,] 5.1 2.5 3.0 1.1
#> [100,] 5.7 2.8 4.1 1.3
#> [101,] 6.3 3.3 6.0 2.5
#> [102,] 5.8 2.7 5.1 1.9
#> [103,] 7.1 3.0 5.9 2.1
#> [104,] 6.3 2.9 5.6 1.8
#> [105,] 6.5 3.0 5.8 2.2
#> [106,] 7.6 3.0 6.6 2.1
#> [107,] 4.9 2.5 4.5 1.7
#> [108,] 7.3 2.9 6.3 1.8
#> [109,] 6.7 2.5 5.8 1.8
#> [110,] 7.2 3.6 6.1 2.5
#> [111,] 6.5 3.2 5.1 2.0
#> [112,] 6.4 2.7 5.3 1.9
#> [113,] 6.8 3.0 5.5 2.1
#> [114,] 5.7 2.5 5.0 2.0
#> [115,] 5.8 2.8 5.1 2.4
#> [116,] 6.4 3.2 5.3 2.3
#> [117,] 6.5 3.0 5.5 1.8
#> [118,] 7.7 3.8 6.7 2.2
#> [119,] 7.7 2.6 6.9 2.3
#> [120,] 6.0 2.2 5.0 1.5
#> [121,] 6.9 3.2 5.7 2.3
#> [122,] 5.6 2.8 4.9 2.0
#> [123,] 7.7 2.8 6.7 2.0
#> [124,] 6.3 2.7 4.9 1.8
#> [125,] 6.7 3.3 5.7 2.1
#> [126,] 7.2 3.2 6.0 1.8
#> [127,] 6.2 2.8 4.8 1.8
#> [128,] 6.1 3.0 4.9 1.8
#> [129,] 6.4 2.8 5.6 2.1
#> [130,] 7.2 3.0 5.8 1.6
#> [131,] 7.4 2.8 6.1 1.9
#> [132,] 7.9 3.8 6.4 2.0
#> [133,] 6.4 2.8 5.6 2.2
#> [134,] 6.3 2.8 5.1 1.5
#> [135,] 6.1 2.6 5.6 1.4
#> [136,] 7.7 3.0 6.1 2.3
#> [137,] 6.3 3.4 5.6 2.4
#> [138,] 6.4 3.1 5.5 1.8
#> [139,] 6.0 3.0 4.8 1.8
#> [140,] 6.9 3.1 5.4 2.1
#> [141,] 6.7 3.1 5.6 2.4
#> [142,] 6.9 3.1 5.1 2.3
#> [143,] 5.8 2.7 5.1 1.9
#> [144,] 6.8 3.2 5.9 2.3
#> [145,] 6.7 3.3 5.7 2.5
#> [146,] 6.7 3.0 5.2 2.3
#> [147,] 6.3 2.5 5.0 1.9
#> [148,] 6.5 3.0 5.2 2.0
#> [149,] 6.2 3.4 5.4 2.3
#> [150,] 5.9 3.0 5.1 1.8
#>
#> Slot "meta":
#> meta
#> 1 setosa
#> 2 setosa
#> 3 setosa
#> 4 setosa
#> 5 setosa
#> 6 setosa
#> 7 setosa
#> 8 setosa
#> 9 setosa
#> 10 setosa
#> 11 setosa
#> 12 setosa
#> 13 setosa
#> 14 setosa
#> 15 setosa
#> 16 setosa
#> 17 setosa
#> 18 setosa
#> 19 setosa
#> 20 setosa
#> 21 setosa
#> 22 setosa
#> 23 setosa
#> 24 setosa
#> 25 setosa
#> 26 setosa
#> 27 setosa
#> 28 setosa
#> 29 setosa
#> 30 setosa
#> 31 setosa
#> 32 setosa
#> 33 setosa
#> 34 setosa
#> 35 setosa
#> 36 setosa
#> 37 setosa
#> 38 setosa
#> 39 setosa
#> 40 setosa
#> 41 setosa
#> 42 setosa
#> 43 setosa
#> 44 setosa
#> 45 setosa
#> 46 setosa
#> 47 setosa
#> 48 setosa
#> 49 setosa
#> 50 setosa
#> 51 versicolor
#> 52 versicolor
#> 53 versicolor
#> 54 versicolor
#> 55 versicolor
#> 56 versicolor
#> 57 versicolor
#> 58 versicolor
#> 59 versicolor
#> 60 versicolor
#> 61 versicolor
#> 62 versicolor
#> 63 versicolor
#> 64 versicolor
#> 65 versicolor
#> 66 versicolor
#> 67 versicolor
#> 68 versicolor
#> 69 versicolor
#> 70 versicolor
#> 71 versicolor
#> 72 versicolor
#> 73 versicolor
#> 74 versicolor
#> 75 versicolor
#> 76 versicolor
#> 77 versicolor
#> 78 versicolor
#> 79 versicolor
#> 80 versicolor
#> 81 versicolor
#> 82 versicolor
#> 83 versicolor
#> 84 versicolor
#> 85 versicolor
#> 86 versicolor
#> 87 versicolor
#> 88 versicolor
#> 89 versicolor
#> 90 versicolor
#> 91 versicolor
#> 92 versicolor
#> 93 versicolor
#> 94 versicolor
#> 95 versicolor
#> 96 versicolor
#> 97 versicolor
#> 98 versicolor
#> 99 versicolor
#> 100 versicolor
#> 101 virginica
#> 102 virginica
#> 103 virginica
#> 104 virginica
#> 105 virginica
#> 106 virginica
#> 107 virginica
#> 108 virginica
#> 109 virginica
#> 110 virginica
#> 111 virginica
#> 112 virginica
#> 113 virginica
#> 114 virginica
#> 115 virginica
#> 116 virginica
#> 117 virginica
#> 118 virginica
#> 119 virginica
#> 120 virginica
#> 121 virginica
#> 122 virginica
#> 123 virginica
#> 124 virginica
#> 125 virginica
#> 126 virginica
#> 127 virginica
#> 128 virginica
#> 129 virginica
#> 130 virginica
#> 131 virginica
#> 132 virginica
#> 133 virginica
#> 134 virginica
#> 135 virginica
#> 136 virginica
#> 137 virginica
#> 138 virginica
#> 139 virginica
#> 140 virginica
#> 141 virginica
#> 142 virginica
#> 143 virginica
#> 144 virginica
#> 145 virginica
#> 146 virginica
#> 147 virginica
#> 148 virginica
#> 149 virginica
#> 150 virginica
#>
### using slot names:
dimRedData(data = iris[, 1:4], meta = iris[, 5])
#> An object of class "dimRedData"
#> Slot "data":
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> [1,] 5.1 3.5 1.4 0.2
#> [2,] 4.9 3.0 1.4 0.2
#> [3,] 4.7 3.2 1.3 0.2
#> [4,] 4.6 3.1 1.5 0.2
#> [5,] 5.0 3.6 1.4 0.2
#> [6,] 5.4 3.9 1.7 0.4
#> [7,] 4.6 3.4 1.4 0.3
#> [8,] 5.0 3.4 1.5 0.2
#> [9,] 4.4 2.9 1.4 0.2
#> [10,] 4.9 3.1 1.5 0.1
#> [11,] 5.4 3.7 1.5 0.2
#> [12,] 4.8 3.4 1.6 0.2
#> [13,] 4.8 3.0 1.4 0.1
#> [14,] 4.3 3.0 1.1 0.1
#> [15,] 5.8 4.0 1.2 0.2
#> [16,] 5.7 4.4 1.5 0.4
#> [17,] 5.4 3.9 1.3 0.4
#> [18,] 5.1 3.5 1.4 0.3
#> [19,] 5.7 3.8 1.7 0.3
#> [20,] 5.1 3.8 1.5 0.3
#> [21,] 5.4 3.4 1.7 0.2
#> [22,] 5.1 3.7 1.5 0.4
#> [23,] 4.6 3.6 1.0 0.2
#> [24,] 5.1 3.3 1.7 0.5
#> [25,] 4.8 3.4 1.9 0.2
#> [26,] 5.0 3.0 1.6 0.2
#> [27,] 5.0 3.4 1.6 0.4
#> [28,] 5.2 3.5 1.5 0.2
#> [29,] 5.2 3.4 1.4 0.2
#> [30,] 4.7 3.2 1.6 0.2
#> [31,] 4.8 3.1 1.6 0.2
#> [32,] 5.4 3.4 1.5 0.4
#> [33,] 5.2 4.1 1.5 0.1
#> [34,] 5.5 4.2 1.4 0.2
#> [35,] 4.9 3.1 1.5 0.2
#> [36,] 5.0 3.2 1.2 0.2
#> [37,] 5.5 3.5 1.3 0.2
#> [38,] 4.9 3.6 1.4 0.1
#> [39,] 4.4 3.0 1.3 0.2
#> [40,] 5.1 3.4 1.5 0.2
#> [41,] 5.0 3.5 1.3 0.3
#> [42,] 4.5 2.3 1.3 0.3
#> [43,] 4.4 3.2 1.3 0.2
#> [44,] 5.0 3.5 1.6 0.6
#> [45,] 5.1 3.8 1.9 0.4
#> [46,] 4.8 3.0 1.4 0.3
#> [47,] 5.1 3.8 1.6 0.2
#> [48,] 4.6 3.2 1.4 0.2
#> [49,] 5.3 3.7 1.5 0.2
#> [50,] 5.0 3.3 1.4 0.2
#> [51,] 7.0 3.2 4.7 1.4
#> [52,] 6.4 3.2 4.5 1.5
#> [53,] 6.9 3.1 4.9 1.5
#> [54,] 5.5 2.3 4.0 1.3
#> [55,] 6.5 2.8 4.6 1.5
#> [56,] 5.7 2.8 4.5 1.3
#> [57,] 6.3 3.3 4.7 1.6
#> [58,] 4.9 2.4 3.3 1.0
#> [59,] 6.6 2.9 4.6 1.3
#> [60,] 5.2 2.7 3.9 1.4
#> [61,] 5.0 2.0 3.5 1.0
#> [62,] 5.9 3.0 4.2 1.5
#> [63,] 6.0 2.2 4.0 1.0
#> [64,] 6.1 2.9 4.7 1.4
#> [65,] 5.6 2.9 3.6 1.3
#> [66,] 6.7 3.1 4.4 1.4
#> [67,] 5.6 3.0 4.5 1.5
#> [68,] 5.8 2.7 4.1 1.0
#> [69,] 6.2 2.2 4.5 1.5
#> [70,] 5.6 2.5 3.9 1.1
#> [71,] 5.9 3.2 4.8 1.8
#> [72,] 6.1 2.8 4.0 1.3
#> [73,] 6.3 2.5 4.9 1.5
#> [74,] 6.1 2.8 4.7 1.2
#> [75,] 6.4 2.9 4.3 1.3
#> [76,] 6.6 3.0 4.4 1.4
#> [77,] 6.8 2.8 4.8 1.4
#> [78,] 6.7 3.0 5.0 1.7
#> [79,] 6.0 2.9 4.5 1.5
#> [80,] 5.7 2.6 3.5 1.0
#> [81,] 5.5 2.4 3.8 1.1
#> [82,] 5.5 2.4 3.7 1.0
#> [83,] 5.8 2.7 3.9 1.2
#> [84,] 6.0 2.7 5.1 1.6
#> [85,] 5.4 3.0 4.5 1.5
#> [86,] 6.0 3.4 4.5 1.6
#> [87,] 6.7 3.1 4.7 1.5
#> [88,] 6.3 2.3 4.4 1.3
#> [89,] 5.6 3.0 4.1 1.3
#> [90,] 5.5 2.5 4.0 1.3
#> [91,] 5.5 2.6 4.4 1.2
#> [92,] 6.1 3.0 4.6 1.4
#> [93,] 5.8 2.6 4.0 1.2
#> [94,] 5.0 2.3 3.3 1.0
#> [95,] 5.6 2.7 4.2 1.3
#> [96,] 5.7 3.0 4.2 1.2
#> [97,] 5.7 2.9 4.2 1.3
#> [98,] 6.2 2.9 4.3 1.3
#> [99,] 5.1 2.5 3.0 1.1
#> [100,] 5.7 2.8 4.1 1.3
#> [101,] 6.3 3.3 6.0 2.5
#> [102,] 5.8 2.7 5.1 1.9
#> [103,] 7.1 3.0 5.9 2.1
#> [104,] 6.3 2.9 5.6 1.8
#> [105,] 6.5 3.0 5.8 2.2
#> [106,] 7.6 3.0 6.6 2.1
#> [107,] 4.9 2.5 4.5 1.7
#> [108,] 7.3 2.9 6.3 1.8
#> [109,] 6.7 2.5 5.8 1.8
#> [110,] 7.2 3.6 6.1 2.5
#> [111,] 6.5 3.2 5.1 2.0
#> [112,] 6.4 2.7 5.3 1.9
#> [113,] 6.8 3.0 5.5 2.1
#> [114,] 5.7 2.5 5.0 2.0
#> [115,] 5.8 2.8 5.1 2.4
#> [116,] 6.4 3.2 5.3 2.3
#> [117,] 6.5 3.0 5.5 1.8
#> [118,] 7.7 3.8 6.7 2.2
#> [119,] 7.7 2.6 6.9 2.3
#> [120,] 6.0 2.2 5.0 1.5
#> [121,] 6.9 3.2 5.7 2.3
#> [122,] 5.6 2.8 4.9 2.0
#> [123,] 7.7 2.8 6.7 2.0
#> [124,] 6.3 2.7 4.9 1.8
#> [125,] 6.7 3.3 5.7 2.1
#> [126,] 7.2 3.2 6.0 1.8
#> [127,] 6.2 2.8 4.8 1.8
#> [128,] 6.1 3.0 4.9 1.8
#> [129,] 6.4 2.8 5.6 2.1
#> [130,] 7.2 3.0 5.8 1.6
#> [131,] 7.4 2.8 6.1 1.9
#> [132,] 7.9 3.8 6.4 2.0
#> [133,] 6.4 2.8 5.6 2.2
#> [134,] 6.3 2.8 5.1 1.5
#> [135,] 6.1 2.6 5.6 1.4
#> [136,] 7.7 3.0 6.1 2.3
#> [137,] 6.3 3.4 5.6 2.4
#> [138,] 6.4 3.1 5.5 1.8
#> [139,] 6.0 3.0 4.8 1.8
#> [140,] 6.9 3.1 5.4 2.1
#> [141,] 6.7 3.1 5.6 2.4
#> [142,] 6.9 3.1 5.1 2.3
#> [143,] 5.8 2.7 5.1 1.9
#> [144,] 6.8 3.2 5.9 2.3
#> [145,] 6.7 3.3 5.7 2.5
#> [146,] 6.7 3.0 5.2 2.3
#> [147,] 6.3 2.5 5.0 1.9
#> [148,] 6.5 3.0 5.2 2.0
#> [149,] 6.2 3.4 5.4 2.3
#> [150,] 5.9 3.0 5.1 1.8
#>
#> Slot "meta":
#> meta
#> 1 setosa
#> 2 setosa
#> 3 setosa
#> 4 setosa
#> 5 setosa
#> 6 setosa
#> 7 setosa
#> 8 setosa
#> 9 setosa
#> 10 setosa
#> 11 setosa
#> 12 setosa
#> 13 setosa
#> 14 setosa
#> 15 setosa
#> 16 setosa
#> 17 setosa
#> 18 setosa
#> 19 setosa
#> 20 setosa
#> 21 setosa
#> 22 setosa
#> 23 setosa
#> 24 setosa
#> 25 setosa
#> 26 setosa
#> 27 setosa
#> 28 setosa
#> 29 setosa
#> 30 setosa
#> 31 setosa
#> 32 setosa
#> 33 setosa
#> 34 setosa
#> 35 setosa
#> 36 setosa
#> 37 setosa
#> 38 setosa
#> 39 setosa
#> 40 setosa
#> 41 setosa
#> 42 setosa
#> 43 setosa
#> 44 setosa
#> 45 setosa
#> 46 setosa
#> 47 setosa
#> 48 setosa
#> 49 setosa
#> 50 setosa
#> 51 versicolor
#> 52 versicolor
#> 53 versicolor
#> 54 versicolor
#> 55 versicolor
#> 56 versicolor
#> 57 versicolor
#> 58 versicolor
#> 59 versicolor
#> 60 versicolor
#> 61 versicolor
#> 62 versicolor
#> 63 versicolor
#> 64 versicolor
#> 65 versicolor
#> 66 versicolor
#> 67 versicolor
#> 68 versicolor
#> 69 versicolor
#> 70 versicolor
#> 71 versicolor
#> 72 versicolor
#> 73 versicolor
#> 74 versicolor
#> 75 versicolor
#> 76 versicolor
#> 77 versicolor
#> 78 versicolor
#> 79 versicolor
#> 80 versicolor
#> 81 versicolor
#> 82 versicolor
#> 83 versicolor
#> 84 versicolor
#> 85 versicolor
#> 86 versicolor
#> 87 versicolor
#> 88 versicolor
#> 89 versicolor
#> 90 versicolor
#> 91 versicolor
#> 92 versicolor
#> 93 versicolor
#> 94 versicolor
#> 95 versicolor
#> 96 versicolor
#> 97 versicolor
#> 98 versicolor
#> 99 versicolor
#> 100 versicolor
#> 101 virginica
#> 102 virginica
#> 103 virginica
#> 104 virginica
#> 105 virginica
#> 106 virginica
#> 107 virginica
#> 108 virginica
#> 109 virginica
#> 110 virginica
#> 111 virginica
#> 112 virginica
#> 113 virginica
#> 114 virginica
#> 115 virginica
#> 116 virginica
#> 117 virginica
#> 118 virginica
#> 119 virginica
#> 120 virginica
#> 121 virginica
#> 122 virginica
#> 123 virginica
#> 124 virginica
#> 125 virginica
#> 126 virginica
#> 127 virginica
#> 128 virginica
#> 129 virginica
#> 130 virginica
#> 131 virginica
#> 132 virginica
#> 133 virginica
#> 134 virginica
#> 135 virginica
#> 136 virginica
#> 137 virginica
#> 138 virginica
#> 139 virginica
#> 140 virginica
#> 141 virginica
#> 142 virginica
#> 143 virginica
#> 144 virginica
#> 145 virginica
#> 146 virginica
#> 147 virginica
#> 148 virginica
#> 149 virginica
#> 150 virginica
#>
## Convert to a dimRedData objects:
Iris <- as(iris[, 1:4], "dimRedData")
## Convert to data.frame:
head(as(s3d, "data.frame"))
#> meta.x meta.y x y z
#> 1 -3.021469 1.4638981 -0.1072140 1.5029277 1.9779528
#> 2 2.812930 1.6593310 0.3549164 1.6539398 -1.8550226
#> 3 -1.143790 0.6508086 -0.9393959 0.6389731 0.5506825
#> 4 -1.803581 1.5900944 -1.0663543 1.5694958 1.2841629
#> 5 1.051515 1.9486942 0.8672332 1.9302189 -0.4196088
#> 6 2.379555 0.9830578 0.6445032 0.9844853 -1.7174701
head(as.data.frame(s3d))
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': cannot coerce class ‘structure("dimRedData", package = "dimRed")’ to a data.frame
head(as.data.frame(as(iris[, 1:4], "dimRedData")))
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': cannot coerce class ‘structure("dimRedData", package = "dimRed")’ to a data.frame
## Extract slots:
head(getData(s3d))
#> x y z
#> [1,] -0.1072140 1.5029277 1.9779528
#> [2,] 0.3549164 1.6539398 -1.8550226
#> [3,] -0.9393959 0.6389731 0.5506825
#> [4,] -1.0663543 1.5694958 1.2841629
#> [5,] 0.8672332 1.9302189 -0.4196088
#> [6,] 0.6445032 0.9844853 -1.7174701
head(getMeta(s3d))
#> x y
#> 1 -3.021469 1.4638981
#> 2 2.812930 1.6593310
#> 3 -1.143790 0.6508086
#> 4 -1.803581 1.5900944
#> 5 1.051515 1.9486942
#> 6 2.379555 0.9830578
## Get the number of observations:
nrow(s3d)
#> [1] 2000
## Subset:
s3d[1:5, ]
#> An object of class "dimRedData"
#> Slot "data":
#> x y z
#> [1,] -0.1072140 1.5029277 1.9779528
#> [2,] 0.3549164 1.6539398 -1.8550226
#> [3,] -0.9393959 0.6389731 0.5506825
#> [4,] -1.0663543 1.5694958 1.2841629
#> [5,] 0.8672332 1.9302189 -0.4196088
#>
#> Slot "meta":
#> x y
#> 1 -3.021469 1.4638981
#> 2 2.812930 1.6593310
#> 3 -1.143790 0.6508086
#> 4 -1.803581 1.5900944
#> 5 1.051515 1.9486942
#>
## Shuffle data:
s3 <- s3d[nrow(s3d)]
## Get the number of variables:
ndims(s3d)
#> [1] 3