Predict Linear Regression

Generates prediction for linear regression. Note that one can either input a 'linearr' object or a matrix of beta coefficients.

# S3 method for linearr
predict(object, X, y = NULL, ...)

Arguments

object	'linearr' object or matrix of betas
X	matrix or data frame of (new) observations
y	optional, matrix or vector of response values
...	additional arguments

Value

predictions and loss metrics

Examples

library(dplyr)
X = dplyr::select(iris, -c(Species, Sepal.Length))
y = dplyr::select(iris, Sepal.Length)
fitted = linearr(X, y, lam = 0.1)
predict(fitted, X)
#> $fitted.values
#>            [,1]
#>   [1,] 5.015416
#>   [2,] 4.689997
#>   [3,] 4.749251
#>   [4,] 4.825994
#>   [5,] 5.080499
#>   [6,] 5.377194
#>   [7,] 4.894684
#>   [8,] 5.021245
#>   [9,] 4.624913
#>  [10,] 4.881642
#>  [11,] 5.216496
#>  [12,] 5.092158
#>  [13,] 4.745645
#>  [14,] 4.532906
#>  [15,] 5.199008
#>  [16,] 5.560786
#>  [17,] 5.093541
#>  [18,] 4.959767
#>  [19,] 5.367758
#>  [20,] 5.225932
#>  [21,] 5.163072
#>  [22,] 5.105200
#>  [23,] 4.796847
#>  [24,] 4.931043
#>  [25,] 5.304898
#>  [26,] 4.831824
#>  [27,] 4.980862
#>  [28,] 5.086329
#>  [29,] 4.950332
#>  [30,] 4.961991
#>  [31,] 4.896907
#>  [32,] 4.909949
#>  [33,] 5.532480
#>  [34,] 5.471002
#>  [35,] 4.825994
#>  [36,] 4.678338
#>  [37,] 4.944503
#>  [38,] 5.136148
#>  [39,] 4.619084
#>  [40,] 5.021245
#>  [41,] 4.888854
#>  [42,] 4.107850
#>  [43,] 4.749251
#>  [44,] 4.934649
#>  [45,] 5.453936
#>  [46,] 4.634349
#>  [47,] 5.352493
#>  [48,] 4.820165
#>  [49,] 5.216496
#>  [50,] 4.885248
#>  [51,] 6.492521
#>  [52,] 6.295046
#>  [53,] 6.513615
#>  [54,] 5.466023
#>  [55,] 6.105625
#>  [56,] 6.146008
#>  [57,] 6.446308
#>  [58,] 5.201659
#>  [59,] 6.282005
#>  [60,] 5.599797
#>  [61,] 5.083151
#>  [62,] 5.952139
#>  [63,] 5.567884
#>  [64,] 6.297270
#>  [65,] 5.572873
#>  [66,] 6.214698
#>  [67,] 6.164879
#>  [68,] 5.964216
#>  [69,] 5.644209
#>  [70,] 5.636574
#>  [71,] 6.340841
#>  [72,] 5.791442
#>  [73,] 6.123113
#>  [74,] 6.343483
#>  [75,] 6.069265
#>  [76,] 6.149614
#>  [77,] 6.303099
#>  [78,] 6.408148
#>  [79,] 6.099795
#>  [80,] 5.473653
#>  [81,] 5.500577
#>  [82,] 5.485312
#>  [83,] 5.711093
#>  [84,] 6.339459
#>  [85,] 6.164879
#>  [86,] 6.369565
#>  [87,] 6.371789
#>  [88,] 5.749676
#>  [89,] 5.992523
#>  [90,] 5.596191
#>  [91,] 6.000576
#>  [92,] 6.291440
#>  [93,] 5.716923
#>  [94,] 5.136576
#>  [95,] 5.868185
#>  [96,] 6.119084
#>  [97,] 5.998352
#>  [98,] 6.069265
#>  [99,] 4.998355
#> [100,] 5.862355
#> [101,] 6.867345
#> [102,] 6.172514
#> [103,] 6.823774
#> [104,] 6.712895
#> [105,] 6.697212
#> [106,] 7.320166
#> [107,] 5.728164
#> [108,] 7.209288
#> [109,] 6.594387
#> [110,] 7.133510
#> [111,] 6.442284
#> [112,] 6.314340
#> [113,] 6.540121
#> [114,] 5.915785
#> [115,] 5.959356
#> [116,] 6.417166
#> [117,] 6.707066
#> [118,] 7.856101
#> [119,] 7.161275
#> [120,] 5.998775
#> [121,] 6.700818
#> [122,] 6.040123
#> [123,] 7.316560
#> [124,] 6.086336
#> [125,] 6.877199
#> [126,] 7.191799
#> [127,] 6.080506
#> [128,] 6.281587
#> [129,] 6.480867
#> [130,] 7.031102
#> [131,] 6.946729
#> [132,] 7.754658
#> [133,] 6.425219
#> [134,] 6.460191
#> [135,] 6.740237
#> [136,] 6.854304
#> [137,] 6.704424
#> [138,] 6.772150
#> [139,] 6.210674
#> [140,] 6.534292
#> [141,] 6.509173
#> [142,] 6.210256
#> [143,] 6.172514
#> [144,] 6.842645
#> [145,] 6.654606
#> [146,] 6.216085
#> [147,] 5.971433
#> [148,] 6.383030
#> [149,] 6.618246
#> [150,] 6.423413
#> 
#> $RSS
#> NULL
#> 
#> $MSE
#> NULL
#>