naiveBayes: Naive Bayes

In soundgen: Sound Synthesis and Acoustic Analysis

Naive Bayes

Description

An implementation of a Naive Bayes classifier adapted to autocorrelated time series such as the type of nonlinear vocal phenomena in consecutive audio frames. All predictors must be continuous, and the outcome must be categorical. Cases with missing values are not deleted because the posterior probabilities of each outcome class can be calculated from different combinations of predictors on a case-by-case basis. Two optional modifications of a standard Naive Bayes algorithm can be made: (1) classifications can be "clumped" at the final stage, ensuring that every run or "epoch" of a particular predicted class is at least minLength steps long, and (2) priors can be continuously adapted based on static posterior probabilities of the preceding wlPrior observations if prior = 'dynamic'. naiveBayes_train calculates conditional means and standard deviations per class and global proportions of each class in the dataset. This is mostly useful because the output can be passed on to naiveBayes to save time if naiveBayes() is called in a loop with the same training dataset.

Usage

naiveBayes(
  formula,
  train,
  test = train,
  prior = c("static", "dynamic", "flat"),
  wlPrior = 3,
  wlClumper = NULL,
  runBack = TRUE,
  plot = FALSE
)

naiveBayes_train(formula, train)

Arguments

formula	model formula of the type outcome ~ predictor1 + predictor2 + ... (no interactions)
train	either the training dataframe or the output of naiveBayes_train. This data is used to calculate class-specific distributions of the predictors and prior class probabilities
test	the test dataframe. This data is used to make predictions - that is, outcome class probabilities given the values of predictors
prior	"flat" = all classes are equally likely a priori, "static" = use class probabilities in the training dataset, "dynamic" = update prior probabilities from static posterior probabilities of wlPrior preceding observations
wlPrior	the length of the dynamic-prior window in observations
wlClumper	the minimum length of a predicted class run, enforced by clumper()
runBack	if TRUE, the dynamic prior is calculated both forward and backward and averaged (only has an effect if prior = 'dynamic')
plot	if TRUE, produces diagnostic plots

Value

A dataframe with new columns: "pr" = the predicted class membership, "[outcome][class]" = posterior probabilities per class, "like_[class]" = normalized likelihoods, "prior_[class]" = normalized priors, "priorF_[class]" / "priorB_[class]" = forward / backward normalized priors (only filled for prior = 'dynamic').

Functions

• naiveBayes_train(): Train a naive Bayes classifier

Examples

set.seed(151)
## create some fake data
df = data.frame(group = rep(c(
    rep('A', 150), rep('B', 50), rep('A', 120),
    rep('A', 100), rep('B', 30), rep('A', 90)
  ), 3))
df$group = as.factor(df$group)
df$x1 = rnorm(nrow(df), mean = ifelse(df$group == 'A', 3, 6), sd = 2)
df$x2 = rnorm(nrow(df), mean = ifelse(df$group == 'A', 2, -1), sd = 2)
boxplot(x1 ~ group, df)
boxplot(x2 ~ group, df)

## train the classifier
mod_train = naiveBayes_train(group ~ x1 + x2, train = df)
mod_train

## test on new data generated by the same process
test = data.frame(group = rep(c(
  rep('A', 90), rep('B', 40), rep('A', 150),
  rep('B', 40), rep('A', 130), rep('B', 30)
), 2))
test$group = as.factor(test$group)
test$x1 = rnorm(nrow(test), mean = ifelse(test$group == 'A', 3, 6), sd = 2)
test$x2 = rnorm(nrow(test), mean = ifelse(test$group == 'A', 2, -1), sd = 2)

# flat priors (same prior probability for each class)
nb_flat = naiveBayes(group ~ x1 + x2, train = mod_train, test = test,
  prior = 'flat', plot = TRUE)
# same as passing 'train' directly to the model, w/o calling naiveBayes_train():
nb_flat = naiveBayes(group ~ x1 + x2, train = df, test = test, prior = 'flat')
table(nb_flat$group, nb_flat$pr)
mean(nb_flat$group == nb_flat$pr)

# static priors (use original class proportions as prior class probabilities)
nb_static = naiveBayes(group ~ x1 + x2, train = mod_train, test = test,
  prior = 'static', wlClumper = NULL, plot = TRUE)
table(nb_static$group, nb_static$pr)
mean(nb_static$group == nb_static$pr)

# specify custom static priors
mod_train2 = mod_train
mod_train$table
mod_train2$table = list(A = .1, B = .9)  # sum to 1
nb_static2 = naiveBayes(group ~ x1 + x2, train = mod_train2, test = test,
  prior = 'static', wlClumper = NULL, plot = TRUE)
mean(nb_static2$group == nb_static2$pr)

# if we expect autocorrelation, ie class X is more likely a priori if the
# last few observations were also likely to be class X, we can use dynamic
# priors and/or clump the predicted classes (the latter imposes strong
# constraints on the predictions, but may be worth it if the data is known to
# be strongly "clumped", ie if we know classes occur in long'ish runs)
nb1 = naiveBayes(group ~ x1 + x2, train = mod_train, test = test,
  prior = 'dynamic', wlPrior = 10, plot = TRUE)
table(nb1$group, nb1$pr)
mean(nb1$group == nb1$pr)

nb2 = naiveBayes(group ~ x1 + x2, train = mod_train, test = test,
  prior = 'static', wlClumper = 10, plot = TRUE)
table(nb2$group, nb2$pr)
mean(nb2$group == nb2$pr)

nb3 = naiveBayes(group ~ x1 + x2, train = mod_train, test = test,
  prior = 'dynamic', wlPrior = 10, wlClumper = 10, plot = TRUE)
table(nb3$group, nb3$pr)
mean(nb3$group == nb3$pr)

# NAs in the data are not a problem
test1 = test
test1$x1[sample(1:nrow(test1), 100)] = NA
test1$x2[sample(1:nrow(test1), 10)] = NA
summary(test1)

nb4 = naiveBayes(group ~ x1 + x2, train = mod_train, test = test1,
  prior = 'dynamic', wlPrior = 10, plot = TRUE)
table(nb4$group, nb4$pr, useNA = 'ifany')
mean(nb4$group == nb4$pr, na.rm = TRUE)

soundgen documentation built on Sept. 20, 2026, 5:07 p.m.