naiveBayes {crimelinkage}  R Documentation 
After binning, this adds pseudo counts to each bin count to give df approximate degrees of freedom. If partition=quantile, this does not assume a continuous uniform prior over support, but rather a discrete uniform over all (unlabeled) observations points.
naiveBayes(formula, data, weights, df = 20, nbins = 30,
partition = c("quantile", "width"))
naiveBayes.fit(X, y, weights, df = 20, nbins = 30,
partition = c("quantile", "width"))
formula 
an object of class 
data 
data.frame of predictors, can include continuous and
categorical/factors along with a response vector (1 = linked, 0 = unlinked),
and (optionally) observation weights (e.g., 
weights 
a vector of observation weights or the column name in

df 
the degrees of freedom for each component density. if vector, each predictor can use a different df 
nbins 
the number of bins for continuous predictors 
partition 
for binning; indicates if breaks generated from quantiles or equal spacing 
X 
data frame of categorical and/or numeric variables 
y 
binary vector indicating linkage (1 = linked, 0 = unlinked) or logical vector (TRUE = linked, FALSE = unlinked) 
Fits a naive bayes model to continous and categorical/factor predictors. Continous predictors are first binned, then estimates shrunk towards zero.
BF a bayes factor object; list of component bayes factors
predict.naiveBayes
, plot.naiveBayes
# See vignette: "Statistical Methods for Crime Series Linkage" for usage.