plot.poisson_naive_bayes function

Plot Method for poisson_naive_bayes Objects

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.

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

Examples

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)

Author(s)

Michal Majka, michalmajka@hotmail.com

See Also

naive_bayes, poisson_naive_bayes, predict.poisson_naive_bayes, tables, get_cond_dist