plot.bernoulli_naive_bayes function

Plot Method for bernoulli_naive_bayes Objects

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.

## 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": $P(Xi|class)$

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")

Author(s)

Michal Majka, michalmajka@hotmail.com

See Also

naive_bayes,bernoulli_naive_bayes predict.bernoulli_naive_bayes, tables, get_cond_dist