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

## Description

Plot method for objects of class `"poisson_naive_bayes"` designed for a quick look at the class marginal or class conditional Poisson distributions of non-negative integer predictors.

## Usage

 ```1 2 3 4``` ```## S3 method for class 'poisson_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 `"poisson_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 or class conditional Poisson 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`, `poisson_naive_bayes`, `predict.poisson_naive_bayes`, `tables`, `get_cond_dist`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```cols <- 10 ; rows <- 100 M <- matrix(rpois(rows * cols, lambda = 3), nrow = rows, ncol = cols) # is.integer(M) # [1] TRUE y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE)) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0 ### Train the Poisson Naive Bayes pnb <- poisson_naive_bayes(x = M, y = y, laplace = laplace) # Visualize class conditional Poisson distributions corresponding # to the first feature plot(pnb, which = 1, prob = "conditional") # Visualize class marginal Poisson distributions corresponding # to the first feature plot(pnb, which = 1) ```