soundgen (version 2.7.2)

naiveBayes: 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 the likelihood function of the preceding wlPrior observations if prior = 'dynamic'.

Usage

naiveBayes(
  formula,
  train,
  test = train,
  prior = c("flat", "static", "dynamic")[2],
  wlPrior = 3,
  wlClumper = NULL,
  runBack = TRUE,
  plot = FALSE
)

Value

Returns the test dataframe with new columns: "pr" = the predicted class membership, "[class]" = posterior probabilities per class, "like_[class]" = log-likelihoods, "prior_[class]" = log-priors, "priorF_[class]" / "priorB_[class]" = forward / backward log-priors per class.

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 prior, "static" = use class probabilities in the training dataset, "dynamic" = update prior probabilities from weighted likelihoods of wlPrior preceding observations

wlPrior

the length of a Gaussian window used for updating dynamic priors

wlClumper

the minimum expected number of observations of the same class before the class can change

runBack

if TRUE, the dynamic prior is calculated both forward and backward and averaged (only has an effect f prior = 'dynamic')

plot

if TRUE, produces diagnostic plots

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, data = 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) # 84% correct

# 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) # 87% correct

# 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)  # 61% correct

# 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 clumper the predicted classes (the latter imposes strong
# constraints on the predictions, but may be worth it if the data is known to
# be strongly "clumpered", 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) # 94% correct

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) # 89% correct

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) # 98% correct

# 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 = test,
  prior = 'dynamic', wlPrior = 10, plot = TRUE)
table(nb4$group, nb4$pr)
mean(nb4$group == nb4$pr)  # still 94% correct