Hi,
Many thanks for your responses.
Le samedi 6 novembre 2021, 08:39:22 UTC+1, Rui Barradas
a écrit :
Hello,
Às 01:36 de 06/11/21, David Winsemius escreveu:
>
> On 11/5/21 1:16 PM, varin sacha via R-help wrote:
>> Dear R-experts,
>>
>> Here is a toy example. How can I get the bootst
Hi,
I really thank you a lot for your response.
Le samedi 6 novembre 2021, 02:37:46 UTC+1, David Winsemius
a écrit :
On 11/5/21 1:16 PM, varin sacha via R-help wrote:
> Dear R-experts,
>
> Here is a toy example. How can I get the bootstrap confidence intervals
> working ?
>
> Many th
Hello,
Às 01:36 de 06/11/21, David Winsemius escreveu:
On 11/5/21 1:16 PM, varin sacha via R-help wrote:
Dear R-experts,
Here is a toy example. How can I get the bootstrap confidence
intervals working ?
Many thanks for your help
library(DescTools)
library(boo
On 11/5/21 1:16 PM, varin sacha via R-help wrote:
Dear R-experts,
Here is a toy example. How can I get the bootstrap confidence intervals working
?
Many thanks for your help
library(DescTools)
library(boot)
A=c(488,437,500,449,364)
dat<-data.frame(A)
med<-fu
Dear R-experts,
Here is a toy example. How can I get the bootstrap confidence intervals working
?
Many thanks for your help
library(DescTools)
library(boot)
A=c(488,437,500,449,364)
dat<-data.frame(A)
med<-function(d,i) {
temp<-d[i,]
HodgesLehmann(A)
}
boot.out<-
Hi Frank,
Good to know. Thanks again for your help!
Kim
On Wed, Nov 10, 2010 at 8:53 AM, Frank Harrell wrote:
>
> Unfortunately, bootcov is not meant to operate on fit objects produced by
> fit.mult.impute. bootcov "gets there too late" in the process and does not
> know how to penalize for i
Unfortunately, bootcov is not meant to operate on fit objects produced by
fit.mult.impute. bootcov "gets there too late" in the process and does not
know how to penalize for imputation.
Frank
-
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
View this message in contex
Hello,
I am using R.12.2.0. I am trying to generate bootstrap confidence intervals
using bootcov from the rms package. I am able to impute the missing data
using aregImpute and to perform a linear regression on the imputed datasets
using fit.mult.impute, but I am unable to use bootcov to generat
hi glen,
i need conf.intervals for blocked data, as described in the first place.
i've learned in the meantime, that the boot() function can handle this.
i had to formulate the function for the boot command,
put "sites" to the strata argument and resample from each subsetted level of
the fact
It's possible I have failed to understand your situation (it's not clearly
described).
If your model captures the dependence structure (e.g. that induced by a
common-but-unknown block effect), then in many cases it could be set up to
work. If the dependence is of some form not captured in the mod
not yet,
but it would either not solve the non iid problem, or would it?
--
View this message in context:
http://n4.nabble.com/bootstrap-confidence-intervals-non-iid-tp1751619p1752583.html
Sent from the R help mailing list archive at Nabble.com.
__
R
Kay Cichini wrote:
>
> i need to calculate ci's for each of 4 groups within a dataset, to be able
> to infere about differences in the variable "similarity". the problem is
> that data within groups is dependent, as assigned by the blocking-factor
> "site". my guess was to use a block bootstrap
hello,
i need to calculate ci's for each of 4 groups within a dataset, to be able
to infere about differences in the variable "similarity". the problem is
that data within groups is dependent, as assigned by the blocking-factor
"site". my guess was to use a block bootstrap but samples within in t
How can I performing Bootstrap Confidence Intervals for the estimates of
nonparametric regression y=f(x) such as loess and spline smoothing
Thanks in advance
[[alternative HTML version deleted]]
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R-help@r-project.org mailing list
https
That is what I wanted. Thank you very much Chuck.
_Fede_
Chuck Cleland wrote:
>
> _Fede_ wrote:
>> Sorry for the previous message. I have been reading the help page about
>> boot
>> library and I have already understood what the arguments made reference
>> (original data and vector of indices
_Fede_ wrote:
> Sorry for the previous message. I have been reading the help page about boot
> library and I have already understood what the arguments made reference
> (original data and vector of indices). Now everything is ok.Thanks for your
> help.
>
> But I have another doubt. How I can make
Sorry for the previous message. I have been reading the help page about boot
library and I have already understood what the arguments made reference
(original data and vector of indices). Now everything is ok.Thanks for your
help.
But I have another doubt. How I can make a histogram of the boots
Thank you very much for your help, Chuck. But I don't understand the function
"statistic" nor that his arguments make. Those arguments do not take value
at any moment, according to I understand (I have not given values to "d" nor
"ind"). It is not thus?
Can you explain me, please?
Thanks.
_Fede
_Fede_ wrote:
> Hi again.
>
> Watching this example that appears in the help page
>
> ratio <- function(d, w) sum(d$x * w)/sum(d$u * w)
> city.boot <- boot(city, ratio, R = 999, stype = "w",sim = "ordinary")
> boot.ci(city.boot, conf = c(0.90,0.95),type =
> c("norm","basic","perc","bca"))
>
> I
Hi again.
Watching this example that appears in the help page
ratio <- function(d, w) sum(d$x * w)/sum(d$u * w)
city.boot <- boot(city, ratio, R = 999, stype = "w",sim = "ordinary")
boot.ci(city.boot, conf = c(0.90,0.95),type =
c("norm","basic","perc","bca"))
I have tried to do the following (c
You need to call boot() to create an object to pass to boot.ci().
There are lots of examples in the help pages and in the book that package
'boot' supports. From the help:
Usage:
boot.ci(boot.out, conf = 0.95, type = "all",
index = 1:min(2,length(boot.out$t0)), var.t0 = NULL
Hi all.
This is my first post in this forum. Finally I find a forum in the web about
R, although is not in my language.
Now I'm working with Bootstrap CI. I'd like to know how I can calculate a
Bootstrap CI for any statistic, in particular, for Kurtosis Coeficient. I
have done the following co
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