Predict method for the rforest function

# S3 method for rforest
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  pred_names = "",
  OOB = NULL,
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object	Return value from `rforest`
pred_data	Provide the dataframe to generate predictions (e.g., diamonds). The dataset must contain all columns used in the estimation
pred_cmd	Generate predictions using a command. For example, `pclass = levels(pclass)` would produce predictions for the different levels of factor `pclass`. To add another variable, create a vector of prediction strings, (e.g., c('pclass = levels(pclass)', 'age = seq(0,100,20)')
pred_names	Names for the predictions to be stored. If one name is provided, only the first column of predictions is stored. If empty, the levels in the response variable of the rforest model will be used
OOB	Use Out-Of-Bag predictions (TRUE or FALSE). Relevant when evaluating predictions for the training sample. If missing, datasets will be compared to determine of OOB predictions should be used
dec	Number of decimals to show
envir	Environment to extract data from
...	further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/model/rforest.html for an example in Radiant

Examples

result <- rforest(titanic, "survived", c("pclass", "sex"), lev = "Yes")
predict(result, pred_cmd = "pclass = levels(pclass)")
#> Random Forest
#> Data                 : titanic 
#> Response variable    : survived 
#> Level(s)             : Yes in survived 
#> Explanatory variables: pclass, sex 
#> Prediction command   : pclass = levels(pclass) 
#> Additional arguments : OOB = NULL 
#> 
#>   sex pclass   Yes    No
#>  male    1st 0.833 0.167
#>  male    2nd 0.816 0.184
#>  male    3rd 0.505 0.495
result <- rforest(diamonds, "price", "carat:color", type = "regression")
predict(result, pred_cmd = "carat = 1:3")
#> Random Forest
#> Data                 : diamonds 
#> Response variable    : price 
#> Explanatory variables: carat, clarity, cut, color 
#> Prediction command   : carat = 1:3 
#> Additional arguments : OOB = NULL 
#> 
#>  clarity   cut color carat Prediction
#>      SI1 Ideal     G     1   3014.741
#>      SI1 Ideal     G     2   8309.019
#>      SI1 Ideal     G     3   9859.129
predict(result, pred_data = diamonds) %>% head()
#> Using OOB predictions after comparing the training and prediction data
#> Using OOB predictions
#> Random Forest
#> Data                 : diamonds 
#> Response variable    : price 
#> Explanatory variables: carat, clarity, cut, color 
#> Prediction dataset   : diamonds 
#> Additional arguments : OOB = NULL 
#> 
#>  carat clarity       cut color Prediction
#>  0.320     VS1     Ideal     H    602.805
#>  0.340     SI1 Very Good     G    576.379
#>  0.300     VS2 Very Good     G    545.945
#>  0.350    VVS2     Ideal     H    748.498
#>  0.400     VS2   Premium     F   1009.561
#>  0.600    VVS1     Ideal     E   2865.482

Predict method for the rforest function

Arguments

Details

See also

Examples