# plot.gaussian_naive_bayes: Plot Method for gaussian_naive_bayes Objects In naivebayes: High Performance Implementation of the Naive Bayes Algorithm

## Description

Plot method for objects of class `"gaussian_naive_bayes"` designed for a quick look at the class marginal or conditional Gaussian distributions of metric predictors.

## Usage

 ```1 2 3 4``` ```## S3 method for class 'gaussian_naive_bayes' plot(x, which = NULL, ask = FALSE, legend = TRUE, legend.box = FALSE, arg.num = list(), prob = c("marginal", "conditional"), ...) ```

## Arguments

 `x` object of class inheriting from `"gaussian_naive_bayes"`. `which` variables to be plotted (all by default). This can be any valid indexing vector or vector containing names of variables. `ask` logical; if `TRUE`, the user is asked before each plot, see `par(ask=.)`. `legend` logical; if `TRUE` a `legend` will be be plotted. `legend.box` logical; if `TRUE` a box will be drawn around the legend. `arg.num` other parameters to be passed as a named list to `matplot`. `prob` character; if "marginal" then marginal distributions of predictor variables for each class are visualised and if "conditional" then the class conditional distributions of predictor variables are depicted. By default, prob="marginal". `...` not used.

## Details

Class marginal and class conditional Gaussian distributions are visualised by `matplot`.

The parameter `prob` controls the kind of probabilities to be visualized for each individual predictor Xi. It can take on two values:

• "marginal": P(Xi|class) * P(class)

• "conditional": P(Xi|class)

## Author(s)

Michal Majka, michalmajka@hotmail.com

`naive_bayes`, `gaussian_naive_bayes`, `predict.gaussian_naive_bayes`, `tables`, `get_cond_dist`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```data(iris) y <- iris[[5]] M <- as.matrix(iris[-5]) ### Train the Gaussian Naive Bayes with custom prior gnb <- gaussian_naive_bayes(x = M, y = y, prior = c(0.1,0.3,0.6)) # Visualize class marginal Gaussian distributions corresponding # to the first feature plot(gnb, which = 1) # Visualize class conditional Gaussian distributions corresponding # to the first feature plot(gnb, which = 1, prob = "conditional") ```

### Example output

```naivebayes 0.9.7 loaded
```

naivebayes documentation built on March 13, 2020, 1:31 a.m.