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

 plot.bernoulli_naive_bayes R Documentation

Plot Method for bernoulli_naive_bayes Objects

Description

Plot method for objects of class `"bernoulli_naive_bayes"` designed for a quick look at the class marginal distributions or class conditional distributions of 0-1 valued predictors.

Usage

``````## S3 method for class 'bernoulli_naive_bayes'
plot(x, which = NULL, ask = FALSE, arg.cat = list(),
prob = c("marginal", "conditional"), ...)
``````

Arguments

 `x` object of class inheriting from `"bernoulli_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=.)`. `arg.cat` other parameters to be passed as a named list to `mosaicplot`. `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 conditional or class conditional distributions are visualised by `mosaicplot`.

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`,`bernoulli_naive_bayes` `predict.bernoulli_naive_bayes`, `tables`, `get_cond_dist`

Examples

``````# Simulate data
cols <- 10 ; rows <- 100 ; probs <- c("0" = 0.4, "1" = 0.1)
M <- matrix(sample(0:1, rows * cols,  TRUE, probs), nrow = rows, ncol = cols)
y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7)))
colnames(M) <- paste0("V", seq_len(ncol(M)))
laplace <- 0.5

# Train the Bernoulli Naive Bayes model
bnb <- bernoulli_naive_bayes(x = M, y = y, laplace = laplace)

# Visualize class marginal probabilities corresponding to the first feature
plot(bnb, which = 1)

# Visualize class conditional probabilities corresponding to the first feature
plot(bnb, which = 1, prob = "conditional")

``````

naivebayes documentation built on June 25, 2024, 1:16 a.m.