Error message not understood - r

Question

I am trying to compute a function in R but, i am getting a weird error message which is not giving me any hint on what might be wrong...
G2M1$data is just a matrix containing data.
library(klaR)
out <- NaiveBayes(x = G2M1$data, grouping = G2M1$labels, usekernel = TRUE, density(G2M1$data, bw = "nrd0", adjust = 1,kernel = "gaussian"))
error message:
Error in sum(prior) : invalid 'type' (list) of argument
I am not sure why, since i am not defining any prior?

Answer 1

The first step in asking a question on Stack Overflow is to create a reproducible example. That is a small example that users can input into their computers to test, diagnose, and solve your issue. It not only helps others but it also enables you to properly assess your problem and potentially find a solution while creating the example.
Example
G2M1 <- list(data=as.matrix(iris[-5]), labels=iris[[5]])
This is an example dataset in the same structure and name as your question using the iris dataset.
Recreate error
Let's run your expression as is to see the error:
library(klaR)
out <- NaiveBayes(x = G2M1$data, grouping = G2M1$labels, usekernel = TRUE, density(G2M1$data, bw = "nrd0", adjust = 1,kernel = "gaussian"))
#Error in sum(prior) : invalid 'type' (list) of argument
Now we have found the error with our example. Let's investigate why it's happening. Let's look at the density expression and save it to a variable:
den <- density(G2M1$data, bw = "nrd0", adjust = 1,kernel = "gaussian")
class(den)
#[1] "density"
typeof(den)
#[1] "list"
It is a list. It not only has the densities, it also has other information like the call used and coordinates that we do not need for the model. Where are the densities themselves? We look to the documentation:
y the estimated density values.
Let's subset the variable with y to see the densities:
head(den$y)
#[1] 0.0003561307 0.0004076448 0.0004647614 0.0005300218 0.0006043244 0.0006864581
This is what the model is looking for. We substitute den$y into the model call:
out <- NaiveBayes(x = G2M1$data, grouping = G2M1$labels, usekernel = TRUE, den$y)
Success. In the future, remember to create an example for everyone. And use these basic troubleshooting techniques. Good luck