ity.
>
>
>
> Tim
>
>
>
> *From:* David Winsemius
> *Sent:* Thursday, September 8, 2022 8:51 PM
> *To:* Bogdan Tanasa
> *Cc:* Ebert,Timothy Aaron ; r-help
> *Subject:* Re: [R] confidence intervals
>
>
>
> *[External Email]*
>
> The first article ha
idence interval and thereby get some context to explain
>> why the equation is correct.
>>
>> Tim
>>
>> -Original Message-
>> From: R-help On Behalf Of Bogdan Tanasa
>> Sent: Sunday, August 28, 2022 8:55 PM
>> To: David Winsemius
>
lf Of Bogdan Tanasa
> Sent: Sunday, August 28, 2022 8:55 PM
> To: David Winsemius
> Cc: r-help
> Subject: Re: [R] confidence intervals
>
> [External Email]
>
> Hi David,
>
> Thank you for your comments, and feed-back message. I am very happy to
> learn from the
the equation is correct.
Tim
-Original Message-
From: R-help On Behalf Of Bogdan Tanasa
Sent: Sunday, August 28, 2022 8:55 PM
To: David Winsemius
Cc: r-help
Subject: Re: [R] confidence intervals
[External Email]
Hi David,
Thank you for your comments, and feed-back message. I am very
Hi David,
Thank you for your comments, and feed-back message. I am very happy to
learn from the experience of the people on R mailing list, and without any
doubt, I am very thankful to you and to everyone for sharing their
knowledge. I do apologize for any confusion that I have created unwillingly
You cross-posted this to StackOverflow and did not say so. ... and you
posted in HTML Bad dog squared. I cast one of the close votes on SO, but
here I can only say ... READ the Posting Guide.
You also give no citation other than someone's Github files with minimal
comments in that material. Y
Dear Matthias,
Many thanks for your response.
Best,
SV
Le mardi 4 août 2020 à 16:22:41 UTC+2, Prof. Dr. Matthias Kohl
a écrit :
you could try:
library(MKinfer)
meanDiffCI(a, b, boot = TRUE)
Best
Matthias
Am 04.08.20 um 16:08 schrieb varin sacha via R-help:
> Dear R-experts,
>
> Usin
you could try:
library(MKinfer)
meanDiffCI(a, b, boot = TRUE)
Best
Matthias
Am 04.08.20 um 16:08 schrieb varin sacha via R-help:
Dear R-experts,
Using the bootES package I can easily calculate the bootstrap confidence
intervals of the means like in the toy example here below. Now, I am looki
Dear R-experts,
Using the bootES package I can easily calculate the bootstrap confidence
intervals of the means like in the toy example here below. Now, I am looking
for the confidence intervals for the difference between group means. In my
case, the point estimate of the mean difference is 64.
Hi Paul,
Currently it does not provide prediction intervals, as it is not assuming a
generative model or a particular error distribution.
I think the best way forward, with nnfor, is to construct empirical ones.
Have a look at this paper for some relatively straightforward approaches that
work q
Dear friends,
Hope you are all doing well. I am currently using function mlp (to fit
multiple layer percentron model) to generate forecasts using package nnfor.
I would like to know if the mlp function provides, or is there a way to
construct confidence intervals for the forecasts generated by th
Dear all,
I am using the Instrumental Variable approach to estimate the causal
effects of TWO endogenous variables in a Mendelian Randomization study.
As long as point estimation is concerned, I have no problem: both "ivreg"
in library "AER" and "tsls" in library "sem" do the job perfectly. The
Hi David, Rui,
Thanks for your precious responses. It works !
Best,
De : David L Carlson
.pt>
Cc : "r-help@r-project.org"
Envoyé le : Dimanche 10 décembre 2017 19h05
Objet : RE: [R] Confidence intervals around the MIC (Maximal information
ist
Subject: Re: [R] Confidence intervals around the MIC (Maximal information
coefficient)
Hi Rui,
Many thanks. The R code works BUT the results I get are quite weird I guess !
MIC = 0.2650
Normal 95% CI = (0.9614, 1.0398)
The MIC is not inside the confidence intervals !
Is there somethi
quot;all")
##
De : Rui Barradas
roject.org>
Envoyé le : Dimanche 10 décembre 2017 16h34
Objet : Re: [R] Confidence intervals around the MIC (Maximal information
coefficient)
Hello,
First of all, when I tried to use function mic I got an error.
mic(cbind(C, D))
Error i
Hello,
First of all, when I tried to use function mic I got an error.
mic(cbind(C, D))
Error in mic(cbind(C, D)) : could not find function "mic"
So I've changed your function myCor and all went well, with a warning
relative to BCa intervals.
myCor <- function(data, index){
mine(data[ind
Dear R-Experts,
Here below is my R code (reproducible example) to calculate the confidence
intervals around the spearman coefficient.
##
C=c(2,4,5,6,3,4,5,7,8,7,6,5,6,7,7,8,5,4,3,2)
D=c(3,5,4,6,7,2,3,1,2,4,5,4,6,4,5,4,3,2,8,9)
cor(C,D,method= "spearman")
library(boot)
myCor=function(data
Richard,
Thanks, Have not previously used the HH package, but looks as if it
contains many useful tools. Will check out your book also.
Best regards,
James
On Thu, Dec 1, 2016 at 10:18 PM, Richard M. Heiberger wrote:
> James,
>
> Please look at the maiz example, the last example in ?MMC
> help
James,
Please look at the maiz example, the last example in ?MMC
help("MMC", package="HH")
where I show how to construct and calculate a set of orthogonal contrasts
for a factor in an analysis of variance setting. mmc and mmcplot use glht
in the multcomp package for the underlying calculations.
Hi R users,
Is there a way to calculate a confidence interval for each contrast in
a set of orthogonal contrasts? The ‘multcomp’ package will calculate
a CIs at the 95% family-wise confidence level. But, these confidence
intervals are extremely wide.
Thanks for your help.
Best regards,
James
___
Dear R-Experts,
I am trying to calculate the confidence intervals of the Goodman & Kruskal
gamma statistics using bootstrap. There is no gamma function in the boot
package. There is a gamma function in the base package, but it is the usual
mathematical function.
So, I decide to try to calcula
i don't know the answer to your larger question, but for confidence
intervals around proportions you might look at ?svyciprop. one of the
method= options might yield the same result as your approximation, not sure
On Mon, May 11, 2015 at 12:40 AM, Brown, Tony Nicholas <
tony.n.br...@vanderbilt.ed
All:
I need to generate confidence intervals for differences in proportions using
data from a complex survey design. An example follows where I attempt to
estimate the difference in depression prevalence by sex.
# Data might look something like this:
Dfr<-data.frame(depression=sample(c("yes","n
On Mar 7, 2015, at 6:29 AM, annclaire wrote:
> Hello!
> I am trying to fit confidence intervals for my server object as well, but I
> can only get an interval estimation for the intercept coefficient and am
> getting NAs for my scale parameter. Did you also have this issue?
> Below is a rough
Hello!
I am trying to fit confidence intervals for my server object as well, but I
can only get an interval estimation for the intercept coefficient and am
getting NAs for my scale parameter. Did you also have this issue?
Below is a rough outline of my code
>newsurvobj <-Surv(studytimenew, cens
Very simple, indeed.
Thank you very much for your help.
Luis
> Subject: Re: [R] Confidence Intervals for survreg (survival)
> From: dwinsem...@comcast.net
> Date: Tue, 20 Aug 2013 11:50:52 -0700
> CC: r-help@r-project.org
> To: ljantu...@hotmail.com
>
>
> On Aug 20
On Aug 20, 2013, at 7:04 AM, Luis Antunes wrote:
> Hello,
> I am fitting a weibull regression model to a survival data set, using survreg
> from the survival package.I would like to obtain the confidence interval for
> the regression coefficients estimates, but I not seeing how.Can anyone help
Hello,
I am fitting a weibull regression model to a survival data set, using survreg
from the survival package.I would like to obtain the confidence interval for
the regression coefficients estimates, but I not seeing how.Can anyone help me?
Thanks,Luis
Hello,
You don't have to exchange 'object' by the name of your model, you call
the function with the name of your model:
x <- 1:20
y <- x + rnorm(20)
fit <- lm(y ~ x)
ci_lm(fit)
beta lowerupper
(Intercept) 0.6741130 -0.9834827 2.331709
x 0.9575906 0.819217
Rui,
Thank you very much. Are there other things I have to adjust except for
exchanging "object" by the name of my model?
Torvon
On 29 November 2012 08:17, Rui Barradas wrote:
> ci_lm <- function(object, level = 0.95){
> summfit <- summary(object)
> beta <- summfit$coefficients[, 1]
>
Hello,
Try the following function.
ci_lm <- function(object, level = 0.95){
summfit <- summary(object)
beta <- summfit$coefficients[, 1]
se <- summfit$coefficients[, 2]
df <- summfit$df[1]
alpha <- 1 - level
lower <- beta + qt(alpha/2, df = df)*se
upper <- beta
?summary.lm
---
Jeff NewmillerThe . . Go Live...
DCN:Basics: ##.#. ##.#. Live Go...
Live: OO#.. Dead: OO#.. Playing
Research Eng
I would like to obtain Confidence Intervals for the estimates
(unstandardized beta weights) of each predictor in a WLS regression:
m1 = lm(x~ x1+x2+x3, weights=W, data=D)
SPSS offers that output by default, and I am not able to find a way to do
this in R. I read through predict.lm, but I do not f
Hi - Thanks for the reply. I provided answers below.
Sally
Sally_roman umassd.edu> writes:
> Hi - I am using R version 2.13.0. I have run several GLMMs using
> the glmmPQL function to model the proportion of fish caught in one
> net to the total caught in both nets by length. I started with
Sally_roman umassd.edu> writes:
> Hi - I am using R version 2.13.0. I have run several GLMMs using
> the glmmPQL function to model the proportion of fish caught in one
> net to the total caught in both nets by length. I started with a
> polynomial regression full model with three length terms:
Hi - I am using R version 2.13.0. I have run several GLMMs using the glmmPQL
function to model the proportion of fish caught in one net to the total
caught in both nets by length. I started with a polynomial regression full
model with three length terms: l, l^2, and l^3 (l=length). The length te
Sorry - CSR = Complete Spatial Randomness.
--
View this message in context:
http://r.789695.n4.nabble.com/Confidence-intervals-in-Ripley-s-K-function-a-little-challenge-tp4649392p4649597.html
Sent from the R help mailing list archive at Nabble.com.
I have plotted Ripley's K function for a spatial point pattern for 12 plots,
with 39 Monte Carlo simulations for complete spatial randomness (CSR).
I would like to analyse these data as follows:
I would like to know for which plots the Ripley's K function deviates from
CSR at a number of confide
On 11/07/2012 12:31 AM, kun...@gfz-potsdam.de wrote:
Hi,
I have a question about the computation of confidence intervals in the zyp
package, in particular using the functions zyp.sen and confint.zyp, or
zyp.yuepilon.
(1) I'm a bit confused about the confidence intervals given by zyp.sen and
Hi,
I have a question about the computation of confidence intervals in the zyp
package, in particular using the functions zyp.sen and confint.zyp, or
zyp.yuepilon.
(1) I'm a bit confused about the confidence intervals given by zyp.sen and
confint.zyp. When I request a certain confidence interv
Dear all,
I would like to know if it is possible in R to calculate the confidence
intervals when calculating the IRF of a VAR but using Monte Carlo
procedures.
Thanks for your help.
Best,
Veronica ACURIO
--
View this message in context:
http://r.789695.n4.nabble.com/Confidence-Intervals-for-
If you want a confidence based in new x values you can do. I have this post
with steps to do this. It's written in Portuguese but the R code is useful.
http://ridiculas.wordpress.com/2011/05/19/bandas-de-confianca-para-modelo-de-regressao-nao-linear/
Bests.
Walmes.
==
Thanks! Now it is clear.
Francisco
On Wed, 16 May 2012 07:32:56 -0400, Gabor Grothendieck wrote
> On Tue, May 15, 2012 at 11:20 PM, Gabor Grothendieck
> wrote:
> > On Tue, May 15, 2012 at 8:08 PM, Francisco Mora Ardila
> > wrote:
> >> Hi all
> >>
> >> I have fitted a model usinf nls function to
On Tue, May 15, 2012 at 11:20 PM, Gabor Grothendieck
wrote:
> On Tue, May 15, 2012 at 8:08 PM, Francisco Mora Ardila
> wrote:
>> Hi all
>>
>> I have fitted a model usinf nls function to these data:
>>
>>> x
>> [1] 1 0 0 4 3 5 12 10 12 100 100 100
>>
>>> y
>> [1] 1.281055090 1.5
On Tue, May 15, 2012 at 11:20 PM, Gabor Grothendieck
wrote:
> On Tue, May 15, 2012 at 8:08 PM, Francisco Mora Ardila
> wrote:
>> Hi all
>>
>> I have fitted a model usinf nls function to these data:
>>
>>> x
>> [1] 1 0 0 4 3 5 12 10 12 100 100 100
>>
>>> y
>> [1] 1.281055090 1.5
On Tue, May 15, 2012 at 8:08 PM, Francisco Mora Ardila
wrote:
> Hi all
>
> I have fitted a model usinf nls function to these data:
>
>> x
> [1] 1 0 0 4 3 5 12 10 12 100 100 100
>
>> y
> [1] 1.281055090 1.563609934 0.001570796 2.291579783 0.841891853
> [6] 6.553951324 14.243
On Tue, 15 May 2012 20:33:02 -0400, David Winsemius wrote
> On May 15, 2012, at 8:08 PM, Francisco Mora Ardila wrote:
>
> > Hi all
> >
> > I have fitted a model usinf nls function to these data:
> >
> >> x
> > [1] 1 0 0 4 3 5 12 10 12 100 100 100
> >
> >> y
> > [1] 1.281055090 1.5
On May 15, 2012, at 8:08 PM, Francisco Mora Ardila wrote:
Hi all
I have fitted a model usinf nls function to these data:
x
[1] 1 0 0 4 3 5 12 10 12 100 100 100
y
[1] 1.281055090 1.563609934 0.001570796 2.291579783 0.841891853
[6] 6.553951324 14.243274230 14.519899320
Hi all
I have fitted a model usinf nls function to these data:
> x
[1] 1 0 0 4 3 5 12 10 12 100 100 100
> y
[1] 1.281055090 1.563609934 0.001570796 2.291579783 0.841891853
[6] 6.553951324 14.243274230 14.519899320 15.066473610 21.728809880
[11] 18.553054450 23.722637370
Hi,
Does anyone have any suggestions for plotting confidence intervals onto
fitted non-linear models?
Thank you for your help!
Kate
--
View this message in context:
http://r.789695.n4.nabble.com/confidence-intervals-for-non-linear-models-tp4506095p4506095.html
Sent from the R help mailing list
On Tue, Mar 6, 2012 at 8:55 PM, Byerly, Mike M (DFG)
wrote:
>
> estimates <-
c(67.42,30.49,32.95,23.53,10.26,6.03,23.53,0.93,50.72,24.2,25.84,18.54,
7.16,3.6,9.35,0.33,87.28,37.25,40.16,28.59,13.77,8.92,40.74,1.68,48.28,23.09,
24.49,17.7,6.63,3.28,7.79,0.26,91.63,38.74,41.6,29.74,14.49,9.51,44.1
# I have some population estimates and confidence intervals for various
size classes
# of animals captured with two gear types. I'd like to plot the
estimates along with
# the 90 and 95% CI's by size class for each gear type.
# The data can be read in as:
estimates <-
c(67.42,30.49,32.95,
Dear gls-experts,
while reading and testing some examples of the book
"introductionary time series analysis with R",
I encountered the following fact which puzzles me.
Confidence intervals for global temperature time series (P99)
computed from general least squares (GLS) to fit the time series
> From: pda...@gmail.com
> Date: Sun, 8 May 2011 09:33:23 +0200
> To: rh...@sticksoftware.com
> CC: r-help@r-project.org
> Subject: Re: [R] Confidence intervals and polynomial fits
>
>
> On May 7, 2011, at 16:15 , Ben Haller wrote:
>
> > On May 6, 2011,
On May 7, 2011, at 16:15 , Ben Haller wrote:
> On May 6, 2011, at 4:27 PM, David Winsemius wrote:
>
>> On May 6, 2011, at 4:16 PM, Ben Haller wrote:
>>>
>>
>>> As for correlated coefficients: x, x^2, x^3 etc. would obviously be highly
>>> correlated, for values close to zero.
>>
>> Not just
On May 6, 2011, at 4:27 PM, David Winsemius wrote:
> On May 6, 2011, at 4:16 PM, Ben Haller wrote:
>>
>
>> As for correlated coefficients: x, x^2, x^3 etc. would obviously be highly
>> correlated, for values close to zero.
>
> Not just for x close to zero:
>
> > cor( (10:20)^2, (10:20)^3 )
>
On May 6, 2011, at 4:16 PM, Ben Haller wrote:
As for correlated coefficients: x, x^2, x^3 etc. would obviously be
highly correlated, for values close to zero.
Not just for x close to zero:
> cor( (10:20)^2, (10:20)^3 )
[1] 0.9961938
> cor( (100:200)^2, (100:200)^3 )
[1] 0.9966219
Is th
On May 6, 2011, at 1:58 PM, Prof Brian Ripley wrote:
> On Fri, 6 May 2011, Bert Gunter wrote:
>
>> FWIW:
>>
>> Fitting higher order polynomials (say > 2) is almost always a bad idea.
>>
>> See e.g. the Hastie, Tibshirani, et. al book on "Statistical
>> Learning" for a detailed explanation why.
On Fri, 6 May 2011, Bert Gunter wrote:
FWIW:
Fitting higher order polynomials (say > 2) is almost always a bad idea.
See e.g. the Hastie, Tibshirani, et. al book on "Statistical
Learning" for a detailed explanation why. The Wikipedia entry on
"smoothing splines" also contains a brief explanat
FWIW:
Fitting higher order polynomials (say > 2) is almost always a bad idea.
See e.g. the Hastie, Tibshirani, et. al book on "Statistical
Learning" for a detailed explanation why. The Wikipedia entry on
"smoothing splines" also contains a brief explanation, I believe.
Your ~0 P values for the
On May 6, 2011, at 12:31 PM, David Winsemius wrote:
> On May 6, 2011, at 11:35 AM, Ben Haller wrote:
>
>> Hi all! I'm getting a model fit from glm() (a binary logistic regression
>> fit, but I don't think that's important) for a formula that contains powers
>> of the explanatory variable up to
> From what you have written, I am not exactly sure what your
> seat-of-the-pant sense is coming from. My pantseat typically does not
> tell me much; however, quartic trends tend to less stable than linear,
> so I am not terribly surprised.
My pantseat is not normally very informative either, b
On May 6, 2011, at 11:35 AM, Ben Haller wrote:
Hi all! I'm getting a model fit from glm() (a binary logistic
regression fit, but I don't think that's important) for a formula
that contains powers of the explanatory variable up to fourth. So
the fit looks something like this (typing into
Hi Ben,
>From what you have written, I am not exactly sure what your
seat-of-the-pant sense is coming from. My pantseat typically does not
tell me much; however, quartic trends tend to less stable than linear,
so I am not terribly surprised.
As two side notes:
x_qt <- x^4 # shorter code-wise
an
Hi all! I'm getting a model fit from glm() (a binary logistic regression
fit, but I don't think that's important) for a formula that contains powers of
the explanatory variable up to fourth. So the fit looks something like this
(typing into mail; the actual fit code is complicated because it
Is there a ROC curve plotting function for censored survival data that
allows condidence intervals? I know there is the survivalROC package for
plotting ROC curves of survival data, but it does not include CI. And
there is roc.plot in the variance package, but it does not support
censored data.
Is there any quick way to compute confidence intervals for the
noncentrality parameter of the noncentral chi-square family? I get an
error when installing the Deducer package.
I would appreciate any help.
David
__
R-help@r-project.org mailing list
http
Hello, I am running a model with four categories and want predicted
probabilities in each category. Now for this example I wont give a
counterfactual just the training data is fine but is there anyway to get a
confidence interval around the predicted probabilities in each group? I have
tried but it
On 20/02/2011 18:52, David Winsemius wrote:
On Feb 20, 2011, at 1:27 PM, Ben Ward wrote:
However, the
Y ~ X + Y^2
Produces the best fitting line - it is pretty much on the data points
- I'm trying to make a standard curve, with which to take readings
from a spectrophotometer off of. Rather
On Feb 20, 2011, at 1:27 PM, Ben Ward wrote:
However, the
Y ~ X + Y^2
Produces the best fitting line - it is pretty much on the data
points - I'm trying to make a standard curve, with which to take
readings from a spectrophotometer off of. Rather than what I would
normally use models fo
It is, I tried a glm with a poisson distribution, as was suggested to me
previously, but the Residual Deviance was too high - the book I'm
reading says it suggests overdispersion because it's way above the
Residual degrees of freedom:
glm(formula = Approximate.Counts ~ X..Light.Transmission, f
model <- lm(Approximate.Counts~X..Light.Transmission +
I(Approximate.Counts^2), data=Standards)
Might not be addressing the problem, don't you have Y ~ X + Y^2 here? That's
a violation of the assumptions of an lm isn't it?
Also for plotting CI on a curve look into ggplot2::geom_ribbon, it's muc
Hi David,
I had use log(x)inside the lm call and used predict, although I didn't
know about logs of data making a multiplacative model
exp(log(x)+log(y)) = x*y.
I'll have a look at the poisson model. and see what I manage to produce.
Looking at the internet the Cumulative distribution functio
On Feb 19, 2011, at 1:08 PM, Ben Ward wrote:
Hi Graham,
Thanks, that does explain lots. I've been playing with making log's of
data in models to make the relationship linear, which it does, which
suggests to me that lm() is the right way to go, however, after if
try
to predict after y valu
Hi Graham,
Thanks, that does explain lots. I've been playing with making log's of
data in models to make the relationship linear, which it does, which
suggests to me that lm() is the right way to go, however, after if try
to predict after y values after about 60% on the x axis for light
trans
I've just realised the couple of graphs I put on here have been stripped
off. If anyone has to see them and can't see my problem from code, I can
send them directly to anyone who thinks they can help but wants to see them.
Thanks,
Ben W.
On 18/02/2011 23:29, Ben Ward wrote:
Hi, I wonder if an
Hi, I wonder if anyone could advise me with this:
I've been trying to make a standard curve in R with lm() of some
standards from a spectrophotometer, so as I can express the curve as a
formula, and so obtain values from my treated samples by plugging in
readings into the formula, instead of t
On Wed, 8 Dec 2010, S.M. Raghavan wrote:
Hi all,
I am trying to fit a logistic regression for a bivariate response using five
independent variables in a stepwise procedure. My outputs look okay but does
any one know (or is there any literature on) how the confidence intervals
are calculated for
On Dec 8, 2010, at 11:59 AM, S.M. Raghavan wrote:
Hi all,
I am trying to fit a logistic regression for a bivariate response
using five
independent variables in a stepwise procedure. My outputs look okay
but does
any one know (or is there any literature on) how the confidence
intervals
a
See McCullagh and Nelder's GLM book for details -- and also probably
V&R's MASS for a concise summary, although I don't have it at hand and
can't be sure it's there. Really, practically any book on GLM should
have details.
**HOWEVER** You should realize that all these references are "wrong"
in the
Hi all,
I am trying to fit a logistic regression for a bivariate response using five
independent variables in a stepwise procedure. My outputs look okay but does
any one know (or is there any literature on) how the confidence intervals
are calculated for the reported odds ratios..?
Thanks!
Dear David,
Thank you for your prompt response.
I understand that this may seem like an simple problem to you, but I have never
done this before, so please excuse the mistakes along the line!
May I revise the code according to your suggestion and, should I have further
problems, ask you for y
On Oct 7, 2010, at 12:01 PM, Christian Goelz wrote:
Dear Sirs,
I was hoping you can help me, I am quite desperate in finding a
solution for my problem! I have looked everywhere on the net and tried
hundreds of codes, but I am still not anywhere close to the solution.
I am quite new to R, so pl
Dear Sirs,
I was hoping you can help me, I am quite desperate in finding a
solution for my problem! I have looked everywhere on the net and tried
hundreds of codes, but I am still not anywhere close to the solution.
I am quite new to R, so please excuse if this seems simple:
I am trying to
@manchester.ac.uk" ,
"r-help@r-project.org"
cc
Fernando Marmolejo Ramos
Subject
Re: [R] confidence intervals around p-values
One other case where a confidence interval on a p-value may make sense is
permutation (or other resampling) tests. The population parameter p-value
help-boun...@r-project.org [mailto:r-help-boun...@r-
> project.org] On Behalf Of Ted Harding
> Sent: Thursday, September 09, 2010 8:25 AM
> To: r-help@r-project.org
> Cc: Fernando Marmolejo Ramos
> Subject: Re: [R] confidence intervals around p-values
>
> On 09-Sep-10 13:21:07, Du
On 09-Sep-10 13:21:07, Duncan Murdoch wrote:
> On 09/09/2010 6:44 AM, Fernando Marmolejo Ramos wrote:
>> Dear all
>>
>> I wonder if anyone has heard of confidence intervals around
>> p-values...
>
> That doesn't really make sense. p-values are statistics, not
> parameters. You would compute a
On 09/09/2010 6:44 AM, Fernando Marmolejo Ramos wrote:
Dear all
I wonder if anyone has heard of confidence intervals around p-values...
That doesn't really make sense. p-values are statistics, not
parameters. You would compute a confidence interval around a population
mean because that's
Fernando Marmolejo Ramos adelaide.edu.au> writes:
>
> Dear all
>
> I wonder if anyone has heard of confidence intervals around p-values...
>
> Any pointer would be highly appreciated.
No, and my reflex is that it seems like a bad idea.
If you are using p-values as an index of effect size
Dear all
I wonder if anyone has heard of confidence intervals around p-values...
Any pointer would be highly appreciated.
Best
Fer
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On Aug 17, 2010, at 6:10 AM, North, Bernard V wrote:
Dear All,
Is it possible to get confidence intervals for Harrell's concordance
index or, equivalently, Somer's D using the rms package or in some
other way ?
I have survival data it would be the c-index in the Cox model setting
I beli
Dear All,
Is it possible to get confidence intervals for Harrell's concordance index or,
equivalently, Somer's D using the rms package or in some other way ?
I have survival data it would be the c-index in the Cox model setting
Many thanks
Dr Bernard North
Statistical Consultant
Statistical
install.packages('rms')
require(rms)
?Gls
?plot.Predict
Frank E Harrell Jr Professor and ChairmanSchool of Medicine
Department of Biostatistics Vanderbilt University
On Sat, 14 Aug 2010, Camilo Mora wrote:
Hi everyone:
Is there a function in R to calculate th
Hi everyone:
Is there a function in R to calculate the confidence intervals for the
predictions of a GLS(Generalized Least Square) model?
The function "predict" gives confidence intervals for the predictions
of other types of models (lm, glm, etc) but not gls.
Any input will be much appre
manchester.ac.uk> writes:
>
> On 07-Aug-10 09:29:41, Michael Bedward wrote:
> > Thanks for that clarification Peter - much appreciated.
> >
> > Is there an R function that you'd recommend for calculating
> > more valid CIs ?
> > Michael
>
> It depends on what you want to mean by "more valid"!
Michael Bedward wrote:
> On 7 August 2010 19:56, Martin Maechler wrote:
>
>> I'm coming late to the thread,
>> but it seems that nobody has yet given the advice which I would
>> very *strongly* suggest to anyone asking for confidence
>> intervals in GLMs:
>>
>> Use confint()
>
> confint was actu
On 07-Aug-10 09:29:41, Michael Bedward wrote:
> Thanks for that clarification Peter - much appreciated.
>
> Is there an R function that you'd recommend for calculating
> more valid CIs ?
> Michael
It depends on what you want to mean by "more valid"! If you have
a 95% CI for the linear predictor (
On 7 August 2010 19:56, Martin Maechler wrote:
> I'm coming late to the thread,
> but it seems that nobody has yet given the advice which I would
> very *strongly* suggest to anyone asking for confidence
> intervals in GLMs:
>
> Use confint()
confint was actually mentioned in the second post on
> "PD" == Peter Dalgaard
> on Sat, 07 Aug 2010 10:37:49 +0200 writes:
PD> Michael Bedward wrote:
>>> I was aware of this option. I was assuming it was not ok to do fit +/-
1.96
>>> se when you requested probabilities. If this is legitimate then all the
>>> better.
Thanks for that clarification Peter - much appreciated.
Is there an R function that you'd recommend for calculating more valid CIs ?
Michael
On 7 August 2010 18:37, Peter Dalgaard wrote:
>
> Probably, neither is optimal, although any transformed scale is
> asymptotically equivalent. E.g., neith
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