plot.nonparametric_naive_bayes {naivebayes}

Plot Method for nonparametric_naive_bayes Objects

Description

Plot method for objects of class "nonparametric_naive_bayes" designed for a quick look at the estimated class marginal or class conditional densities of metric predictors.

Usage

## S3 method for class 'nonparametric_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 "nonparametric_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

Estimated class marginal or class conditional densities 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(s)

Michal Majka, [e-mail]

See Also

naive_bayes, nonparametric_naive_bayes predict.nonparametric_naive_bayes, tables, get_cond_dist

Examples

data(iris)
y <- iris[[5]]
M <- as.matrix(iris[-5])

### Train the Non-Parametric Naive Bayes with custom prior
prior <- c(0.1,0.3,0.6)
nnb <- nonparametric_naive_bayes(x = M, y = y, prior = prior)
nnb2 <- nonparametric_naive_bayes(x = M, y = y, prior = prior, adjust = 1.5)
nnb3 <- nonparametric_naive_bayes(x = M, y = y, prior = prior, bw = "ucv")

# Visualize estimated class conditional densities corresponding
# to the first feature
plot(nnb, which = 1, prob = "conditional")
plot(nnb2, which = 1, prob = "cond")
plot(nnb3, which = 1, prob = "c")

# Visualize estimated class marginal densities corresponding
# to the first feature
plot(nnb, which = 1)
plot(nnb2, which = 1)
plot(nnb3, which = 1)