Hello, Are you looking for this?
newd <- data.frame( Class = '1st', Sex = 'Male', Age = 'Child' ) predict(m, newdata = newd, type = 'raw') # No Yes #[1,] 0.3169345 0.6830655 With the default type = 'class' the result is predict(m, newdata = newd) #[1] Yes #Levels: No Yes Hope this helps, Rui Barradas Às 14:42 de 27/02/21, Jeff Reichman escreveu:
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 Naïve 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 ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.