unstrip.RdThis function tries to convert a date.frame or a matrix to a no-frills matrix without labels, and a vector or time-series to a no-frills vector without labels.
unstrip(x)If x is two-dimensional a matrix
without names, if x is one-dimensional a numerical vector
Many of the functions for logspline, oldlogspline,
lspec, polyclass,
hare, heft, and polymars were written in the “before data.frame” era;
unstrip attempts to keep all these functions useful with more advanced input objects.
In particular, many of these functions call unstrip before doing anything else.
data(co2)
unstrip(co2)
#> [1] 315.42 316.31 316.50 317.56 318.13 318.00 316.39 314.65 313.68 313.18
#> [11] 314.66 315.43 316.27 316.81 317.42 318.87 319.87 319.43 318.01 315.74
#> [21] 314.00 313.68 314.84 316.03 316.73 317.54 318.38 319.31 320.42 319.61
#> [31] 318.42 316.63 314.83 315.16 315.94 316.85 317.78 318.40 319.53 320.42
#> [41] 320.85 320.45 319.45 317.25 316.11 315.27 316.53 317.53 318.58 318.92
#> [51] 319.70 321.22 322.08 321.31 319.58 317.61 316.05 315.83 316.91 318.20
#> [61] 319.41 320.07 320.74 321.40 322.06 321.73 320.27 318.54 316.54 316.71
#> [71] 317.53 318.55 319.27 320.28 320.73 321.97 322.00 321.71 321.05 318.71
#> [81] 317.66 317.14 318.70 319.25 320.46 321.43 322.23 323.54 323.91 323.59
#> [91] 322.24 320.20 318.48 317.94 319.63 320.87 322.17 322.34 322.88 324.25
#> [101] 324.83 323.93 322.38 320.76 319.10 319.24 320.56 321.80 322.40 322.99
#> [111] 323.73 324.86 325.40 325.20 323.98 321.95 320.18 320.09 321.16 322.74
#> [121] 323.83 324.26 325.47 326.50 327.21 326.54 325.72 323.50 322.22 321.62
#> [131] 322.69 323.95 324.89 325.82 326.77 327.97 327.91 327.50 326.18 324.53
#> [141] 322.93 322.90 323.85 324.96 326.01 326.51 327.01 327.62 328.76 328.40
#> [151] 327.20 325.27 323.20 323.40 324.63 325.85 326.60 327.47 327.58 329.56
#> [161] 329.90 328.92 327.88 326.16 324.68 325.04 326.34 327.39 328.37 329.40
#> [171] 330.14 331.33 332.31 331.90 330.70 329.15 327.35 327.02 327.99 328.48
#> [181] 329.18 330.55 331.32 332.48 332.92 332.08 331.01 329.23 327.27 327.21
#> [191] 328.29 329.41 330.23 331.25 331.87 333.14 333.80 333.43 331.73 329.90
#> [201] 328.40 328.17 329.32 330.59 331.58 332.39 333.33 334.41 334.71 334.17
#> [211] 332.89 330.77 329.14 328.78 330.14 331.52 332.75 333.24 334.53 335.90
#> [221] 336.57 336.10 334.76 332.59 331.42 330.98 332.24 333.68 334.80 335.22
#> [231] 336.47 337.59 337.84 337.72 336.37 334.51 332.60 332.38 333.75 334.78
#> [241] 336.05 336.59 337.79 338.71 339.30 339.12 337.56 335.92 333.75 333.70
#> [251] 335.12 336.56 337.84 338.19 339.91 340.60 341.29 341.00 339.39 337.43
#> [261] 335.72 335.84 336.93 338.04 339.06 340.30 341.21 342.33 342.74 342.08
#> [271] 340.32 338.26 336.52 336.68 338.19 339.44 340.57 341.44 342.53 343.39
#> [281] 343.96 343.18 341.88 339.65 337.81 337.69 339.09 340.32 341.20 342.35
#> [291] 342.93 344.77 345.58 345.14 343.81 342.21 339.69 339.82 340.98 342.82
#> [301] 343.52 344.33 345.11 346.88 347.25 346.62 345.22 343.11 340.90 341.18
#> [311] 342.80 344.04 344.79 345.82 347.25 348.17 348.74 348.07 346.38 344.51
#> [321] 342.92 342.62 344.06 345.38 346.11 346.78 347.68 349.37 350.03 349.37
#> [331] 347.76 345.73 344.68 343.99 345.48 346.72 347.84 348.29 349.23 350.80
#> [341] 351.66 351.07 349.33 347.92 346.27 346.18 347.64 348.78 350.25 351.54
#> [351] 352.05 353.41 354.04 353.62 352.22 350.27 348.55 348.72 349.91 351.18
#> [361] 352.60 352.92 353.53 355.26 355.52 354.97 353.75 351.52 349.64 349.83
#> [371] 351.14 352.37 353.50 354.55 355.23 356.04 357.00 356.07 354.67 352.76
#> [381] 350.82 351.04 352.69 354.07 354.59 355.63 357.03 358.48 359.22 358.12
#> [391] 356.06 353.92 352.05 352.11 353.64 354.89 355.88 356.63 357.72 359.07
#> [401] 359.58 359.17 356.94 354.92 352.94 353.23 354.09 355.33 356.63 357.10
#> [411] 358.32 359.41 360.23 359.55 357.53 355.48 353.67 353.95 355.30 356.78
#> [421] 358.34 358.89 359.95 361.25 361.67 360.94 359.55 357.49 355.84 356.00
#> [431] 357.59 359.05 359.98 361.03 361.66 363.48 363.82 363.30 361.94 359.50
#> [441] 358.11 357.80 359.61 360.74 362.09 363.29 364.06 364.76 365.45 365.01
#> [451] 363.70 361.54 359.51 359.65 360.80 362.38 363.23 364.06 364.61 366.40
#> [461] 366.84 365.68 364.52 362.57 360.24 360.83 362.49 364.34
data(iris)
unstrip(iris)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 5.1 3.5 1.4 0.2 1
#> [2,] 4.9 3.0 1.4 0.2 1
#> [3,] 4.7 3.2 1.3 0.2 1
#> [4,] 4.6 3.1 1.5 0.2 1
#> [5,] 5.0 3.6 1.4 0.2 1
#> [6,] 5.4 3.9 1.7 0.4 1
#> [7,] 4.6 3.4 1.4 0.3 1
#> [8,] 5.0 3.4 1.5 0.2 1
#> [9,] 4.4 2.9 1.4 0.2 1
#> [10,] 4.9 3.1 1.5 0.1 1
#> [11,] 5.4 3.7 1.5 0.2 1
#> [12,] 4.8 3.4 1.6 0.2 1
#> [13,] 4.8 3.0 1.4 0.1 1
#> [14,] 4.3 3.0 1.1 0.1 1
#> [15,] 5.8 4.0 1.2 0.2 1
#> [16,] 5.7 4.4 1.5 0.4 1
#> [17,] 5.4 3.9 1.3 0.4 1
#> [18,] 5.1 3.5 1.4 0.3 1
#> [19,] 5.7 3.8 1.7 0.3 1
#> [20,] 5.1 3.8 1.5 0.3 1
#> [21,] 5.4 3.4 1.7 0.2 1
#> [22,] 5.1 3.7 1.5 0.4 1
#> [23,] 4.6 3.6 1.0 0.2 1
#> [24,] 5.1 3.3 1.7 0.5 1
#> [25,] 4.8 3.4 1.9 0.2 1
#> [26,] 5.0 3.0 1.6 0.2 1
#> [27,] 5.0 3.4 1.6 0.4 1
#> [28,] 5.2 3.5 1.5 0.2 1
#> [29,] 5.2 3.4 1.4 0.2 1
#> [30,] 4.7 3.2 1.6 0.2 1
#> [31,] 4.8 3.1 1.6 0.2 1
#> [32,] 5.4 3.4 1.5 0.4 1
#> [33,] 5.2 4.1 1.5 0.1 1
#> [34,] 5.5 4.2 1.4 0.2 1
#> [35,] 4.9 3.1 1.5 0.2 1
#> [36,] 5.0 3.2 1.2 0.2 1
#> [37,] 5.5 3.5 1.3 0.2 1
#> [38,] 4.9 3.6 1.4 0.1 1
#> [39,] 4.4 3.0 1.3 0.2 1
#> [40,] 5.1 3.4 1.5 0.2 1
#> [41,] 5.0 3.5 1.3 0.3 1
#> [42,] 4.5 2.3 1.3 0.3 1
#> [43,] 4.4 3.2 1.3 0.2 1
#> [44,] 5.0 3.5 1.6 0.6 1
#> [45,] 5.1 3.8 1.9 0.4 1
#> [46,] 4.8 3.0 1.4 0.3 1
#> [47,] 5.1 3.8 1.6 0.2 1
#> [48,] 4.6 3.2 1.4 0.2 1
#> [49,] 5.3 3.7 1.5 0.2 1
#> [50,] 5.0 3.3 1.4 0.2 1
#> [51,] 7.0 3.2 4.7 1.4 2
#> [52,] 6.4 3.2 4.5 1.5 2
#> [53,] 6.9 3.1 4.9 1.5 2
#> [54,] 5.5 2.3 4.0 1.3 2
#> [55,] 6.5 2.8 4.6 1.5 2
#> [56,] 5.7 2.8 4.5 1.3 2
#> [57,] 6.3 3.3 4.7 1.6 2
#> [58,] 4.9 2.4 3.3 1.0 2
#> [59,] 6.6 2.9 4.6 1.3 2
#> [60,] 5.2 2.7 3.9 1.4 2
#> [61,] 5.0 2.0 3.5 1.0 2
#> [62,] 5.9 3.0 4.2 1.5 2
#> [63,] 6.0 2.2 4.0 1.0 2
#> [64,] 6.1 2.9 4.7 1.4 2
#> [65,] 5.6 2.9 3.6 1.3 2
#> [66,] 6.7 3.1 4.4 1.4 2
#> [67,] 5.6 3.0 4.5 1.5 2
#> [68,] 5.8 2.7 4.1 1.0 2
#> [69,] 6.2 2.2 4.5 1.5 2
#> [70,] 5.6 2.5 3.9 1.1 2
#> [71,] 5.9 3.2 4.8 1.8 2
#> [72,] 6.1 2.8 4.0 1.3 2
#> [73,] 6.3 2.5 4.9 1.5 2
#> [74,] 6.1 2.8 4.7 1.2 2
#> [75,] 6.4 2.9 4.3 1.3 2
#> [76,] 6.6 3.0 4.4 1.4 2
#> [77,] 6.8 2.8 4.8 1.4 2
#> [78,] 6.7 3.0 5.0 1.7 2
#> [79,] 6.0 2.9 4.5 1.5 2
#> [80,] 5.7 2.6 3.5 1.0 2
#> [81,] 5.5 2.4 3.8 1.1 2
#> [82,] 5.5 2.4 3.7 1.0 2
#> [83,] 5.8 2.7 3.9 1.2 2
#> [84,] 6.0 2.7 5.1 1.6 2
#> [85,] 5.4 3.0 4.5 1.5 2
#> [86,] 6.0 3.4 4.5 1.6 2
#> [87,] 6.7 3.1 4.7 1.5 2
#> [88,] 6.3 2.3 4.4 1.3 2
#> [89,] 5.6 3.0 4.1 1.3 2
#> [90,] 5.5 2.5 4.0 1.3 2
#> [91,] 5.5 2.6 4.4 1.2 2
#> [92,] 6.1 3.0 4.6 1.4 2
#> [93,] 5.8 2.6 4.0 1.2 2
#> [94,] 5.0 2.3 3.3 1.0 2
#> [95,] 5.6 2.7 4.2 1.3 2
#> [96,] 5.7 3.0 4.2 1.2 2
#> [97,] 5.7 2.9 4.2 1.3 2
#> [98,] 6.2 2.9 4.3 1.3 2
#> [99,] 5.1 2.5 3.0 1.1 2
#> [100,] 5.7 2.8 4.1 1.3 2
#> [101,] 6.3 3.3 6.0 2.5 3
#> [102,] 5.8 2.7 5.1 1.9 3
#> [103,] 7.1 3.0 5.9 2.1 3
#> [104,] 6.3 2.9 5.6 1.8 3
#> [105,] 6.5 3.0 5.8 2.2 3
#> [106,] 7.6 3.0 6.6 2.1 3
#> [107,] 4.9 2.5 4.5 1.7 3
#> [108,] 7.3 2.9 6.3 1.8 3
#> [109,] 6.7 2.5 5.8 1.8 3
#> [110,] 7.2 3.6 6.1 2.5 3
#> [111,] 6.5 3.2 5.1 2.0 3
#> [112,] 6.4 2.7 5.3 1.9 3
#> [113,] 6.8 3.0 5.5 2.1 3
#> [114,] 5.7 2.5 5.0 2.0 3
#> [115,] 5.8 2.8 5.1 2.4 3
#> [116,] 6.4 3.2 5.3 2.3 3
#> [117,] 6.5 3.0 5.5 1.8 3
#> [118,] 7.7 3.8 6.7 2.2 3
#> [119,] 7.7 2.6 6.9 2.3 3
#> [120,] 6.0 2.2 5.0 1.5 3
#> [121,] 6.9 3.2 5.7 2.3 3
#> [122,] 5.6 2.8 4.9 2.0 3
#> [123,] 7.7 2.8 6.7 2.0 3
#> [124,] 6.3 2.7 4.9 1.8 3
#> [125,] 6.7 3.3 5.7 2.1 3
#> [126,] 7.2 3.2 6.0 1.8 3
#> [127,] 6.2 2.8 4.8 1.8 3
#> [128,] 6.1 3.0 4.9 1.8 3
#> [129,] 6.4 2.8 5.6 2.1 3
#> [130,] 7.2 3.0 5.8 1.6 3
#> [131,] 7.4 2.8 6.1 1.9 3
#> [132,] 7.9 3.8 6.4 2.0 3
#> [133,] 6.4 2.8 5.6 2.2 3
#> [134,] 6.3 2.8 5.1 1.5 3
#> [135,] 6.1 2.6 5.6 1.4 3
#> [136,] 7.7 3.0 6.1 2.3 3
#> [137,] 6.3 3.4 5.6 2.4 3
#> [138,] 6.4 3.1 5.5 1.8 3
#> [139,] 6.0 3.0 4.8 1.8 3
#> [140,] 6.9 3.1 5.4 2.1 3
#> [141,] 6.7 3.1 5.6 2.4 3
#> [142,] 6.9 3.1 5.1 2.3 3
#> [143,] 5.8 2.7 5.1 1.9 3
#> [144,] 6.8 3.2 5.9 2.3 3
#> [145,] 6.7 3.3 5.7 2.5 3
#> [146,] 6.7 3.0 5.2 2.3 3
#> [147,] 6.3 2.5 5.0 1.9 3
#> [148,] 6.5 3.0 5.2 2.0 3
#> [149,] 6.2 3.4 5.4 2.3 3
#> [150,] 5.9 3.0 5.1 1.8 3