performNB {nbTransmission}

Performs naive bayes classification

Description

The function performNB Calculates the posterior probabilities of a dichotomous class variable given a set of covariates using Bayes rule.

Usage

performNB(training, prediction, obsIDVar, goldStdVar, covariates, l = 1)

Arguments

training

The training dataset name.

prediction

The prediction dataset name.

obsIDVar

The variable name (in quotes) of the observation ID variable.

goldStdVar

The variable name (in quotes) of the outcome in the training dataset (needs to be a logical variable with value TRUE for observations with the outcome of interest.)

covariates

A character vector containing the covariate variable names. All covariates need to be categorical factor variables.

l

Laplace smoothing parameter that is added to each cell (a value of 0 indicates no smoothing).

Details

The main purpose of this function is to be used by nbProbabilities to estimate the relative transmission probability between individuals in an infectious disease outbreak. However, it can be used more generally to estimate the probability of any dichotomous outcome given a set of categorical covariates.

The function needs a training dataset with the outcome variable (goldStdVar) which is TRUE for those who have the value of interest and FALSE for those who do not. The probability of having the outcome (<goldStdVar> = TRUE) is predicted in the prediction dataset.

Value

List containing two dataframes:

1. probabilities - a dataframe combining training and prediction with predictied probabilities for the prediction dataframe. Column names:

   • <obsIDVar> - the observation ID with the name specified

   • p - the probability that <goldStdVar> = TRUE for observations in the prediction dataset.

2. estimates - a dataframe with the effect estimates derived from the training dataset. Column names:

   • level - the covariate name and level

   • est - the log odds ratio for this covariate and level

   • se - the standard error of the log odds ratio

See Also

nbProbabilities

Examples

## Use iris dataset and predict if a flower is of the specices "virginica".

data(iris)
irisNew <- iris
## Creating an id variable
irisNew$id <- seq(1:nrow(irisNew))
## Creating logical variable indicating if the flower is of the species virginica
irisNew$spVirginica <- irisNew$Species == "virginica"

## Creating categorical/factor versions of the covariates
irisNew$Sepal.Length.Cat <- factor(cut(irisNew$Sepal.Length, c(0, 5, 6, 7, Inf)),
                                 labels = c("<=5.0", "5.1-6.0", "6.1-7.0", "7.1+"))

irisNew$Sepal.Width.Cat <- factor(cut(irisNew$Sepal.Width, c(0, 2.5, 3, 3.5, Inf)),
                                 labels = c("<=2.5", "2.6-3.0", "3.1-3.5", "3.6+"))

irisNew$Petal.Length.Cat <- factor(cut(irisNew$Petal.Length, c(0, 2, 4, 6, Inf)),
                                 labels = c("<=2.0", "2.1-4.0", "4.1-6.0", "6.0+"))

irisNew$Petal.Width.Cat <- factor(cut(irisNew$Petal.Width, c(0, 1, 2, Inf)),
                               labels = c("<=1.0", "1.1-2.0", "2.1+"))

## Using NB to predict if the species is virginica
## (training and predicting on same dataset)
pred <- performNB(irisNew, irisNew, obsIDVar = "id",
                    goldStdVar = "spVirginica",
                    covariates = c("Sepal.Length.Cat", "Sepal.Width.Cat",
                                   "Petal.Length.Cat", "Petal.Width.Cat"), l = 1)
irisResults <- merge(irisNew, pred$probabilities, by = "id")
tapply(irisResults$p, irisResults$Species, summary)