... yes.
... And do note that in sampling, truncated != censored.
(They are often confused)
Cheers,
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
Clifford Stoll
On Sun, Oct 5
Bob O'Hara gmail.com> writes:
>
> This isn't an R question at all, so I don't know why it's on this list. But
> the best answer I've got is "a truncated t-distribution with an infinite
> number of degrees of freedom".
>
> Bob
Or, perhaps more productively: "since your question is a general
This isn't an R question at all, so I don't know why it's on this list. But
the best answer I've got is "a truncated t-distribution with an infinite
number of degrees of freedom".
Bob
On 5 October 2014 17:18, thanoon younis wrote:
> Dear all R-users
> I have a question regarding truncated norma
Dear all R-users
I have a question regarding truncated normal distribution
: which type of probability distribution has same properties of truncated
normal distribution?
Many thanks in advance
--
Thanoon Y. Thanoon
PhD Candidate
Department of Mathematical Sciences
Faculty of Science
University T
-Mensaje original-
De: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] En
nombre de Julia Cains
Enviado el: lunes, 19 de abril de 2010 9:22
Para: r-help@r-project.org
Asunto: [R] Truncated Normal Distribution and Truncated Pareto distribution
Dear R helpers,
I have a
The truncreg package fits the truncated normal model.
Le lundi 19 avril 2010 à 00:21 -0700, Julia Cains a écrit :
> Dear R helpers,
>
> I have a bimodal dataset dealing with loss amounts. I have divided this
> dataset into two with the bounds for the first dataset i.e. dataset-A being
> 5,000
Dear R helpers,
I have a bimodal dataset dealing with loss amounts. I have divided this dataset
into two with the bounds for the first dataset i.e. dataset-A being 5,000$ to
100,000$ and the dataset-B deals with the losses exceeding 100,000$ i.e.
dataset-B is left truncated.
I need to fit tru
G'day all,
On Wed, 23 Jul 2008 20:48:59 -0400
Duncan Murdoch <[EMAIL PROTECTED]> wrote:
> On 23/07/2008 8:17 PM, cindy Guo wrote:
> > Yes, I know. I mean if I want to generate 100 numbers from
> > N(0,1)I((0,1),(5,10)). There are two intervals (0,1) and (5,10).
> > Then the function will give 50
On 23/07/2008 8:17 PM, cindy Guo wrote:
Yes, I know. I mean if I want to generate 100 numbers from
N(0,1)I((0,1),(5,10)). There are two intervals (0,1) and (5,10). Then the
function will give 50 numbers in the first interval and 50 in the other.
No, it doesn't handle that case at all. I didn't
On 7/23/2008 4:22 PM, Duncan Murdoch wrote:
On 7/23/2008 3:41 PM, cindy Guo wrote:
Hi, I want to generate random samples from truncated normal say
Normal(0,1)Indicator((0,1),(2,4)). It has more than one intervals. In the
library msm, it seems to me that the 'lower' and 'upper' arguments can only
On 7/23/2008 3:41 PM, cindy Guo wrote:
Hi, I want to generate random samples from truncated normal say
Normal(0,1)Indicator((0,1),(2,4)). It has more than one intervals. In the
library msm, it seems to me that the 'lower' and 'upper' arguments can only
be a number. I tried rtnorm(1,mean=0,sd=1, l
Hi, I want to generate random samples from truncated normal say
Normal(0,1)Indicator((0,1),(2,4)). It has more than one intervals. In the
library msm, it seems to me that the 'lower' and 'upper' arguments can only
be a number. I tried rtnorm(1,mean=0,sd=1, lower=c(0,2),upper=c(1,4)) and it
didn't w
ravi wrote:
>
> I have the following code, where we need to solve for mu and sigma, when
> we have mut and sdt. Can you suggest how to use a solve function in R to
> do that? I am new to R and am not sure how to go from defining the
> functions, to solving for them.
>
> Thanks
>
>
> tru
I have the following code, where we need to solve for mu and sigma, when we
have mut and sdt. Can you suggest how to use a solve function in R to do
that? I am new to R and am not sure how to go from defining the functions,
to solving for them.
Thanks
truncated <- function(x)
{
mu=x[1];
sigma
I am using TNORM - rtnorm to simulate from a truncated normal distribution.
However, the current function available allows us to define the mean and SD
of the non-truncated (original) distribution and then run the simulation.
http://rss.acs.unt.edu/Rdoc/library/msm/html/tnorm.html
I would instead
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