Predict method for the nb function

Source: R/nb.R

Predict method for the nb function

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

Arguments

object

Return value from nb
pred_data

Provide the dataframe to generate predictions (e.g., titanic). 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 level in the response variable of the nb model will 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/nb.html for an example in Radiant

See also

nb to generate the result

summary.nb to summarize results

Examples

result <- nb(titanic, "survived", c("pclass", "sex", "age"))
predict(result, pred_data = titanic)
#> Naive Bayes Classifier
#> Data                 : titanic 
#> Response variable    : survived 
#> Level(s)             : Yes, No in survived 
#> Explanatory variables: pclass, sex, age 
#> Prediction dataset   : titanic 
#> Rows shown           : 10 of 1,043 
#> 
#>  pclass    sex    age   Yes    No
#>     1st female 29.000 0.877 0.123
#>     1st   male  0.917 0.510 0.490
#>     1st female  2.000 0.922 0.078
#>     1st   male 30.000 0.376 0.624
#>     1st female 25.000 0.881 0.119
#>     1st   male 48.000 0.372 0.628
#>     1st female 63.000 0.891 0.109
#>     1st   male 39.000 0.367 0.633
#>     1st female 53.000 0.878 0.122
#>     1st   male 71.000 0.451 0.549
predict(result, pred_data = titanic, pred_names = c("Yes", "No"))
#> Naive Bayes Classifier
#> Data                 : titanic 
#> Response variable    : survived 
#> Level(s)             : Yes, No in survived 
#> Explanatory variables: pclass, sex, age 
#> Prediction dataset   : titanic 
#> Rows shown           : 10 of 1,043 
#> 
#>  pclass    sex    age   Yes    No
#>     1st female 29.000 0.877 0.123
#>     1st   male  0.917 0.510 0.490
#>     1st female  2.000 0.922 0.078
#>     1st   male 30.000 0.376 0.624
#>     1st female 25.000 0.881 0.119
#>     1st   male 48.000 0.372 0.628
#>     1st female 63.000 0.891 0.109
#>     1st   male 39.000 0.367 0.633
#>     1st female 53.000 0.878 0.122
#>     1st   male 71.000 0.451 0.549
predict(result, pred_cmd = "pclass = levels(pclass)")
#> Naive Bayes Classifier
#> Data                 : titanic 
#> Response variable    : survived 
#> Level(s)             : Yes, No in survived 
#> Explanatory variables: pclass, sex, age 
#> Prediction command   : pclass = levels(pclass) 
#> 
#>     age  sex pclass   Yes    No
#>  29.813 male    1st 0.377 0.623
#>  29.813 male    2nd 0.215 0.785
#>  29.813 male    3rd 0.110 0.890
result <- nb(titanic, "pclass", c("survived", "sex", "age"))
predict(result, pred_data = titanic)
#> Naive Bayes Classifier
#> Data                 : titanic 
#> Response variable    : pclass 
#> Level(s)             : 1st, 2nd, 3rd in pclass 
#> Explanatory variables: survived, sex, age 
#> Prediction dataset   : titanic 
#> Rows shown           : 10 of 1,043 
#> 
#>  survived    sex    age   1st   2nd   3rd
#>       Yes female 29.000 0.410 0.303 0.287
#>       Yes   male  0.917 0.117 0.315 0.568
#>        No female  2.000 0.058 0.253 0.689
#>        No   male 30.000 0.107 0.222 0.672
#>        No female 25.000 0.133 0.258 0.609
#>       Yes   male 48.000 0.633 0.236 0.130
#>       Yes female 63.000 0.891 0.098 0.011
#>        No   male 39.000 0.196 0.262 0.542
#>       Yes female 53.000 0.792 0.165 0.043
#>        No   male 71.000 0.821 0.153 0.026
predict(result, pred_data = titanic, pred_names = c("1st", "2nd", "3rd"))
#> Naive Bayes Classifier
#> Data                 : titanic 
#> Response variable    : pclass 
#> Level(s)             : 1st, 2nd, 3rd in pclass 
#> Explanatory variables: survived, sex, age 
#> Prediction dataset   : titanic 
#> Rows shown           : 10 of 1,043 
#> 
#>  survived    sex    age   1st   2nd   3rd
#>       Yes female 29.000 0.410 0.303 0.287
#>       Yes   male  0.917 0.117 0.315 0.568
#>        No female  2.000 0.058 0.253 0.689
#>        No   male 30.000 0.107 0.222 0.672
#>        No female 25.000 0.133 0.258 0.609
#>       Yes   male 48.000 0.633 0.236 0.130
#>       Yes female 63.000 0.891 0.098 0.011
#>        No   male 39.000 0.196 0.262 0.542
#>       Yes female 53.000 0.792 0.165 0.043
#>        No   male 71.000 0.821 0.153 0.026
predict(result, pred_data = titanic, pred_names = "")
#> Naive Bayes Classifier
#> Data                 : titanic 
#> Response variable    : pclass 
#> Level(s)             : 1st, 2nd, 3rd in pclass 
#> Explanatory variables: survived, sex, age 
#> Prediction dataset   : titanic 
#> Rows shown           : 10 of 1,043 
#> 
#>  survived    sex    age   1st   2nd   3rd
#>       Yes female 29.000 0.410 0.303 0.287
#>       Yes   male  0.917 0.117 0.315 0.568
#>        No female  2.000 0.058 0.253 0.689
#>        No   male 30.000 0.107 0.222 0.672
#>        No female 25.000 0.133 0.258 0.609
#>       Yes   male 48.000 0.633 0.236 0.130
#>       Yes female 63.000 0.891 0.098 0.011
#>        No   male 39.000 0.196 0.262 0.542
#>       Yes female 53.000 0.792 0.165 0.043
#>        No   male 71.000 0.821 0.153 0.026