Naive Bayes using e1071::naiveBayes
nb(dataset, rvar, evar, laplace = 0, data_filter = "", envir = parent.frame())
| dataset | Dataset |
|---|---|
| rvar | The response variable in the logit (probit) model |
| evar | Explanatory variables in the model |
| laplace | Positive double controlling Laplace smoothing. The default (0) disables Laplace smoothing. |
| data_filter | Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
| envir | Environment to extract data from |
A list with all variables defined in nb as an object of class nb
See https://radiant-rstats.github.io/docs/model/nb.html for an example in Radiant
summary.nb to summarize results
plot.nb to plot results
predict.nb for prediction
#> Naive Bayes Classifier #> Data : titanic #> Response variable : survived #> Levels : Yes, No in survived #> Explanatory variables: pclass, sex, age #> Laplace : 0 #> Nr obs : 1,043 #> #> A-priori probabilities: #> survived #> Yes No #> 0.407 0.593 #> #> Conditional probabilities (categorical) or means & st.dev (numeric): #> pclass #> survived 1st 2nd 3rd #> Yes 0.421 0.271 0.308 #> No 0.167 0.236 0.597 #> #> sex #> survived female male #> Yes 0.682 0.318 #> No 0.155 0.845 #> #> age #> survived mean st.dev #> Yes 28.819 15.004 #> No 30.497 13.881 #>#> List of 10 #> $ model :List of 7 #> ..$ apriori : 'table' int [1:2(1d)] 425 618 #> .. ..- attr(*, "dimnames")=List of 1 #> .. .. ..$ dataset[[1]]: chr [1:2] "Yes" "No" #> ..$ tables :List of 3 #> .. ..$ pclass: 'table' num [1:2, 1:3] 0.421 0.167 0.271 0.236 0.308 ... #> .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. ..$ dataset[[1]]: chr [1:2] "Yes" "No" #> .. .. .. ..$ pclass : chr [1:3] "1st" "2nd" "3rd" #> .. ..$ sex : 'table' num [1:2, 1:2] 0.682 0.155 0.318 0.845 #> .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. ..$ dataset[[1]]: chr [1:2] "Yes" "No" #> .. .. .. ..$ sex : chr [1:2] "female" "male" #> .. ..$ age : num [1:2, 1:2] 28.8 30.5 15 13.9 #> .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. ..$ dataset[[1]]: chr [1:2] "Yes" "No" #> .. .. .. ..$ age : NULL #> ..$ levels : chr [1:2] "Yes" "No" #> ..$ isnumeric: Named logi [1:3] FALSE FALSE TRUE #> .. ..- attr(*, "names")= chr [1:3] "pclass" "sex" "age" #> ..$ call : language naiveBayes.default(x = dataset[, -1, drop = FALSE], y = dataset[[1]], laplace = laplace) #> ..$ residuals: logi NA #> ..$ model : tibble [1,043 × 4] (S3: tbl_df/tbl/data.frame) #> .. ..$ survived: Factor w/ 2 levels "Yes","No": 1 1 2 2 2 1 1 2 1 2 ... #> .. ..$ pclass : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ... #> .. ..$ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ... #> .. ..$ age : num [1:1043] 29 0.917 2 30 25 ... #> .. ..- attr(*, "description")= chr "## Titanic\n\nThis dataset describes the survival status of individual passengers on the Titanic. The titanic d"| __truncated__ #> ..- attr(*, "class")= chr "naiveBayes" #> $ form :Class 'formula' language survived ~ pclass + sex + age #> .. ..- attr(*, ".Environment")=<environment: 0xc759ad0> #> $ lev : chr [1:2] "Yes" "No" #> $ vars : chr [1:3] "pclass" "sex" "age" #> $ not_vary : chr(0) #> $ df_name : chr "titanic" #> $ rvar : chr "survived" #> $ evar : chr [1:3] "pclass" "sex" "age" #> $ laplace : num 0 #> $ data_filter: chr "" #> - attr(*, "class")= chr [1:3] "nb" "model" "list"