Predict using generic function

Source: R/cv-model-fit.R

Predicts using predict function and supplied model

generic_predict(
  model,
  predictors,
  SESOI_lower = 0,
  SESOI_upper = 0,
  na.rm = FALSE
)

Arguments

model

Model object
predictors

Data frame
SESOI_lower

Lower smallest effect size of interest threshold
SESOI_upper

Upper smallest effect size of interest threshold
na.rm

Should NAs be removed? Default is FALSE

Examples

m1 <- lm_model(
  predictors = iris[2:3],
  outcome = iris[[1]]
)

generic_predict(m1)
#>        1        2        3        4        5        6        7        8 
#> 5.006900 4.405440 4.589118 4.582638 5.127192 5.658785 4.886608 4.943514 
#>        9       10       11       12       13       14       15       16 
#> 4.285148 4.582638 5.304390 5.000420 4.405440 4.234723 5.494548 6.146434 
#>       17       18       19       20       21       22       23       24 
#> 5.431162 5.006900 5.538493 5.424682 5.057325 5.304390 4.899569 4.937034 
#>       25       26       27       28       29       30       31       32 
#> 5.171137 4.519252 5.000420 5.063806 4.886608 4.759836 4.639544 4.943514 
#>       33       34       35       36       37       38       39       40 
#> 5.785558 5.848944 4.582638 4.532213 4.949994 5.127192 4.348534 4.943514 
#>       41       42       43       44       45       46       47       48 
#> 4.949994 3.506491 4.589118 5.120712 5.652305 4.405440 5.481588 4.646024 
#>       49       50       51       52       53       54       55       56 
#> 5.304390 4.766316 6.523915 6.410104 6.517435 5.042947 5.985842 5.928936 
#>       57       58       59       60       61       62       63       64 
#> 6.644207 4.764898 6.106133 5.467209 4.397542 5.998802 4.922655 6.163039 
#>       65       66       67       68       69       70       71       72 
#> 5.537076 6.232906 6.169520 5.581021 5.207184 5.226625 6.580821 5.644407 
#>       73       74       75       76       77       78       79       80 
#> 5.795683 6.042747 5.935416 6.112614 6.099653 6.454049 6.049228 5.119294 
#>       81       82       83       84       85       86       87       88 
#> 5.049427 4.992522 5.467209 6.150079 6.169520 6.650688 6.403623 5.270570 
#>       89       90       91       92       93       94       95       96 
#> 5.941897 5.283531 5.631446 6.226425 5.403823 4.644606 5.637926 5.998802 
#>       97       98       99      100      101      102      103      104 
#> 5.878510 5.935416 4.714473 5.701313 7.383982 6.150079 6.966201 6.675191 
#>      105      106      107      108      109      110      111      112 
#> 6.909295 7.364541 5.568060 7.073532 6.307835 7.801764 6.751538 6.263890 
#>      113      114      115      116      117      118      119      120 
#> 6.738578 5.852589 6.270370 6.865350 6.738578 8.383783 7.054091 5.491713 
#>      121      122      123      124      125      126      127      128 
#> 7.092973 6.156559 7.180863 6.036267 7.213265 7.263690 6.099653 6.397143 
#>      129      130      131      132      133      134      135      136 
#> 6.554899 6.909295 6.839428 8.213065 6.554899 6.270370 6.314315 7.080012 
#>      137      138      139      140      141      142      143      144 
#> 7.276651 6.858870 6.340237 6.801964 6.915775 6.631246 6.150079 7.206785 
#>      145      146      147      148      149      150 
#> 7.213265 6.567860 5.852589 6.567860 7.162840 6.510954