Skip to contents

Plot Method for poisson_naive_bayes Objects

plot.poisson_naive_bayes.Rd

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

# S3 method for 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

Michal Majka, michalmajka@hotmail.com

See also

naive_bayes, poisson_naive_bayes, predict.poisson_naive_bayes, tables, get_cond_dist

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)