Predict from a cv_model

Predict from a cv_model

# S3 method for bmbstats_cv_model
predict(object, new_data = NULL, type = "numeric", ...)

Arguments

object	A cv_model object.
new_data	A data frame or matrix of new predictors.
type	A single character. The type of predictions to generate. Valid options are: "numeric" for numeric predictions.
...	Not used, but required for extensibility.

Value

A tibble of predictions. The number of rows in the tibble is guaranteed to be the same as the number of rows in new_data.

Examples

m1 <- cv_model(
  Sepal.Length ~ . - Species,
  iris
)
#> Cross-validating: 5 folds, 10 repeats
#> Done!
predict(m1, new_data = iris)
#>        1        2        3        4        5        6        7        8 
#> 5.015416 4.689997 4.749251 4.825994 5.080499 5.377194 4.894684 5.021245 
#>        9       10       11       12       13       14       15       16 
#> 4.624913 4.881642 5.216496 5.092158 4.745645 4.532906 5.199008 5.560786 
#>       17       18       19       20       21       22       23       24 
#> 5.093541 4.959767 5.367758 5.225932 5.163072 5.105200 4.796847 4.931043 
#>       25       26       27       28       29       30       31       32 
#> 5.304898 4.831824 4.980862 5.086329 4.950332 4.961991 4.896907 4.909949 
#>       33       34       35       36       37       38       39       40 
#> 5.532480 5.471002 4.825994 4.678338 4.944503 5.136148 4.619084 5.021245 
#>       41       42       43       44       45       46       47       48 
#> 4.888854 4.107850 4.749251 4.934649 5.453936 4.634349 5.352493 4.820165 
#>       49       50       51       52       53       54       55       56 
#> 5.216496 4.885248 6.492521 6.295046 6.513615 5.466023 6.105625 6.146008 
#>       57       58       59       60       61       62       63       64 
#> 6.446308 5.201659 6.282005 5.599797 5.083151 5.952139 5.567884 6.297270 
#>       65       66       67       68       69       70       71       72 
#> 5.572873 6.214698 6.164879 5.964216 5.644209 5.636574 6.340841 5.791442 
#>       73       74       75       76       77       78       79       80 
#> 6.123113 6.343483 6.069265 6.149614 6.303099 6.408148 6.099795 5.473653 
#>       81       82       83       84       85       86       87       88 
#> 5.500577 5.485312 5.711093 6.339459 6.164879 6.369565 6.371789 5.749676 
#>       89       90       91       92       93       94       95       96 
#> 5.992523 5.596191 6.000576 6.291440 5.716923 5.136576 5.868185 6.119084 
#>       97       98       99      100      101      102      103      104 
#> 5.998352 6.069265 4.998355 5.862355 6.867345 6.172514 6.823774 6.712895 
#>      105      106      107      108      109      110      111      112 
#> 6.697212 7.320166 5.728164 7.209288 6.594387 7.133510 6.442284 6.314340 
#>      113      114      115      116      117      118      119      120 
#> 6.540121 5.915785 5.959356 6.417166 6.707066 7.856101 7.161275 5.998775 
#>      121      122      123      124      125      126      127      128 
#> 6.700818 6.040123 7.316560 6.086336 6.877199 7.191799 6.080506 6.281587 
#>      129      130      131      132      133      134      135      136 
#> 6.480867 7.031102 6.946729 7.754658 6.425219 6.460191 6.740237 6.854304 
#>      137      138      139      140      141      142      143      144 
#> 6.704424 6.772150 6.210674 6.534292 6.509173 6.210256 6.172514 6.842645 
#>      145      146      147      148      149      150 
#> 6.654606 6.216085 5.971433 6.383030 6.618246 6.423413