Hi dear friends,
I am using the naiveBayes function of e1071 package in R. My code is as below:
sp
look at the BayesTree package examples (in the help file:
##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0 #y = f(x) + sigma*z , z~N(0,1)
n = 100 #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
Ey = f(x)
y=Ey+sigma*rnorm(n)
lmFit = lm(y~.,data.frame(x,y)) #compare lm fit to BART later
##run BART
set.seed(99)
bartFit = bart(x,y,ndpost=200) #default is ndpost=1000, this is to run example fast.
plot(bartFit) # plot bart fit
##compare BART fit to linear matter and truth = Ey
fitmat = cbind(y,Ey,lmFit$fitted,bartFit$yhat.train.mean)
colnames(fitmat) = c('y','Ey','lm','bart')
print(cor(fitmat)
Dear Vikas Ramachandra ,
Thank you very much for your answer. I had seen this code in help, but it was so different from my subject. I have one column of raw data as result and a table of raw data as predictors, and wish to call the bart function for making the model. I am looking to find a similar code.
Best Regards
Hello dear Vikas Ramachandra,
Thanks a lot for you answers. I had used the data table as X and result column as Y, But I received error.
Today I will run it again and send you my code and the related error statement.
Perhaps you may see my mistake.
Thank you very much for your time.
Here is my code:
library(stats)
library(BayesTree)
sp
your x and y have to be matrices
so full final code is:
library(stats)
library(BayesTree)
sp
u can obtain prediction ('y') values from bart for your test set (xtest), when you call bart itself:
MySuperModel
Thank you very much dear Dr. Vikas Ramachandra.
It done well.
I will try to find some script which describes the result of the bart prediction. It had 5 columns which I suppose each column be one of my test record, and for each column there exist about 100 rows, which were hard for me to grasp them.
Best regards
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