Thanks Frank and Steve.
I rewrite the R code as follows.
# m is the number of fold to split sample, n is the loop number of cross
validation
library(caret)
calcvnb<-function(formula,dat,m,n)
{
cvnb<-rep(0,2)
dim(cvnb)<-c(200,100)
for (i in 1:n)
{
group<-rep(0,length=110)
sg<-createFolds(d
Take a look at the validate.lrm function in the rms package.
Note that the use of threshold probabilities results in an improper
scoring rule which will mislead you. Also note that you need to repeat
10-fold CV 50-100 times for precision, and that at each repeat you have
to start from zero in
Hi,
On Thu, Jan 21, 2010 at 8:55 AM, zhu yao wrote:
> Hi, everyone:
>
> I ask for help about translating a stata program into R.
>
> The program perform cross validation as it stated.
>
> #1. Randomly divide the data set into 10 sets of equal size, ensuring equal
> numbers of events in each set
>
Hi, everyone:
I ask for help about translating a stata program into R.
The program perform cross validation as it stated.
#1. Randomly divide the data set into 10 sets of equal size, ensuring equal
numbers of events in each set
#2. Fit the model leaving out the 1st set
#3. Apply the fitted model
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