plot.bernoulli_naive_bayes {naivebayes}

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, [e-mail]

See Also

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")