On Sun, 4 Mar 2012, yindalon wrote:
I run the following code:
library(rpart)
data(kyphosis)
fit <- rpart(Kyphosis ~ ., data=kyphosis)
plot(fit)
text(fit, use.n=TRUE)
The text labels represent the count of each class at the leaf node.
Unfortunately, the numbers are rounded and in scientific not
Hi,
My previous version of the conditional MVN function had a bug in that it would
not work when conditional distribution was required for a single variable. I
fixed this and also made a few minor changes. Here is the new version.
condNormal <- function(x.given, mu, sigma, given.ind, req.ind){
I run the following code:
library(rpart)
data(kyphosis)
fit <- rpart(Kyphosis ~ ., data=kyphosis)
plot(fit)
text(fit, use.n=TRUE)
The text labels represent the count of each class at the leaf node.
Unfortunately, the numbers are rounded and in scientific notation rather
than the exact number of e
On Mar 4, 2012, at 4:40 PM, Gabor Grothendieck wrote:
> On Sun, Mar 4, 2012 at 4:19 PM, wrote:
>> On Sun, 4 Mar 2012, Florent D. wrote:
>>
>>> Hello,
>>>
>>> I have noticed that the memory usage inside an R session increases as
>>> more and more objects with unique names are created, even aft
On Sun, Mar 4, 2012 at 4:19 PM, wrote:
> On Sun, 4 Mar 2012, Florent D. wrote:
>
>> Hello,
>>
>> I have noticed that the memory usage inside an R session increases as
>> more and more objects with unique names are created, even after they
>> are removed. Here is a small reproducible example:
>>
>
On Sun, 4 Mar 2012, Florent D. wrote:
Hello,
I have noticed that the memory usage inside an R session increases as
more and more objects with unique names are created, even after they
are removed. Here is a small reproducible example:
gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncel
Hello,
I have noticed that the memory usage inside an R session increases as
more and more objects with unique names are created, even after they
are removed. Here is a small reproducible example:
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 531720 14.2 899071 24.1 818163
Hi,
Let X = (x_1, x_2, ... , x_p) be multivariate normal with mean, mu = (mu_1,
... , mu_p) and covariance = Sigma. I was looking for an R function to compute
conditional mean and conditional variance of a given subset of X given another
subset of X. While this is trivially easy to do, the
I have a tcltk gui I created in R. I can open R and use the source command to
open the gui and then click buttons to perform functions. I would like to
make this a standalone program where the user does not have to open R and
type the source command to run the tcltk gui. I have a very nasty workaro
