Making model predictions

R User Forum

Is there a better way than grabbing individual cell values from a model
output to make predictions. For example the output from the following Na?ve
Bayes model

library(e1071)

## Example of using a contingency table:
data(Titanic)
m <- naiveBayes(Survived ~ ., data = Titanic)
m

will produce the following results:

Call:
naiveBayes.formula(formula = Survived ~ ., data = Titanic)

A-priori probabilities:
Survived
       No      Yes
0.676965 0.323035

Conditional probabilities:
         Class
Survived        1st        2nd        3rd       Crew
      No  0.08187919 0.11208054 0.35436242 0.45167785
      Yes 0.28551336 0.16596343 0.25035162 0.29817159

         Sex
Survived       Male     Female
      No  0.91543624 0.08456376
      Yes 0.51617440 0.48382560

         Age
Survived      Child      Adult
      No  0.03489933 0.96510067
      Yes 0.08016878 0.91983122

Say I want to calculate the probability of P(survival = No | Class = 1st,
Sex = Male, and Age= Child).

While I  can set an object (e.g. myObj <- m$tables$Class[1,1])  to the
respective cell and perform the calculation, there must be a better way, as
I continue to learn R.

Jeff