gaussian.nb: Naive Bayes classifiers

In Rfast: A Collection of Efficient and Extremely Fast R Functions

Naive Bayes classifiers

Description

Gaussian, Poisson, geometric and multinomial naive Bayes classifiers.

Usage

gaussian.nb(xnew = NULL, x, ina, parallel = FALSE)
poisson.nb(xnew, x, ina)
multinom.nb(xnew, x, ina) 
geom.nb(xnew, x, ina, type = 1)
gammanb(xnew = NULL, x, ina, tol = 1e-07)   

Arguments

xnew	A numerical matrix with new predictor variables whose group is to be predicted. For the Gaussian naive Bayes, this is set to NUUL, as you might want just the model and not to predict the membership of new observations. For the Gaussian case this contains any numbers, but for the multinomial and Poisson cases, the matrix must contain integer valued numbers only.
x	A numerical matrix with the observed predictor variable values. For the Gaussian case this contains any numbers, but for the multinomial and Poisson cases, the matrix must contain integer valued numbers only.
ina	A numerical vector with strictly positive numbers, i.e. 1,2,3 indicating the groups of the dataset. Alternatively this can be a factor variable.
type	Type 1 refers to the case where the minimum is zero and type 2 for the case of the minimum being 1. This is for the geometric distribution. This argument is for the geometric distribution. Type 1 refers to the case where the minimum is zero and type 2 for the case of the minimum being 1.
tol	The tolerance value to terminate the Newton-Raphson algorithm in the gamma distribution.
parallel	If you want parallel computations set this equal to TRUE.

Value

For the Poisson and Multinomial naive Bayes classifiers the estimated group, a numerical vector with 1, 2, 3 and so on. For the Gaussian naive Bayes classifier a list including:

mu	A matrix with the mean vector of each group based on the dataset.
sigma	A matrix with the variance of each group and variable based on the dataset.
ni	The sample size of each group in the dataset.
est	The estimated group of the xnew observations. It returns a numerical value back regardless of the target variable being numerical as well or factor. Hence, it is suggested that you do \"as.numeric(target)\" in order to see what is the predicted class of the new data.

For the Gamma classifier a list including:

a	A matrix with the shape parameters.
b	A matrix with the scale parameters.
est	The estimated group of the xnew observations. It returns a numerical value back regardless of the target variable being numerical as well or factor. Hence, it is suggested that you do \"as.numeric(target)\" in order to see what is the predicted class of the new data.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[e-mail]> and Manos Papadakis <[e-mail]>.

See Also

gaussiannb.pred, colmeans, colVars

Examples

x <- as.matrix(iris[, 1:4])
a <- gaussian.nb(x, x, iris[, 5])
x1 <- matrix( rpois(100 * 4, 5), ncol = 4)
x2 <- matrix( rpois(50 * 4, 10), ncol = 4)
x <- rbind(x1, x2)
ina <- c( rep(1, 100), rep(2, 50) )
res<-poisson.nb(x, x, ina)
res<-geom.nb(x, x, ina)
res<-multinom.nb(x, x, ina)

Rfast documentation built on Nov. 9, 2023, 5:06 p.m.