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
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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
