I have created robust regression models using least trimmed squares and
MM-regression (using the R package robustbase).
I am now looking to create prediction intervals for the predicted results.
While I have seen some discussion in the literature about confidence intervals
on the estimates
Presumably you've checked out:
http://cran.r-project.org/web/views/Robust.html
If you can estimate the variance of parameter estimates, betahat, then
you can estimate the variance of a predicted value, X betahat; add the
estimated variance of individuals to this if that's what you're
looking for
On 11/02/2015 19:38, Bert Gunter wrote:
Presumably you've checked out:
http://cran.r-project.org/web/views/Robust.html
If you can estimate the variance of parameter estimates, betahat, then
you can estimate the variance of a predicted value, X betahat; add the
estimated variance of individuals
Sorry. I have updated the code to have include the knot selection
(https://github.com/stanstrup/retpred_shiny/blob/master/retdb_admin/make_predictions_CI_tests.R).
I am working on the Good data at the moment.
- Jan.
On 08/18/2014 08:14 PM, David Winsemius wrote:
I had that result
And just then I realized the problem. nknots need to be length(knots).
Otherwise knots are deleted.
I am not so sure this works equally well as my original loess fit
though. The fit I get with cobs is highly dependent on the knot step
size. At 0.4 for example it seems ok. At 0.3 I get points
The knots are deleted anyway (Deleting unnecessary knots ...). It
seems to make no difference.
On 08/14/2014 06:06 PM, David Winsemius wrote:
On Aug 14, 2014, at 7:17 AM, Jan Stanstrup wrote:
Thank you very much for this snippet!
I used it on my data and indeed it does give intervals
I had that result sometimes when testing as well. You don't offer any code so
there's nothing I can do to follow-up.
--
David.
On Aug 18, 2014, at 4:56 AM, Jan Stanstrup wrote:
The knots are deleted anyway (Deleting unnecessary knots ...). It seems to
make no difference.
On
Thank you very much for this snippet!
I used it on my data and indeed it does give intervals which appear
quite realistic (script and data here
https://github.com/stanstrup/retpred_shiny/blob/master/retdb_admin/make_predictions_CI_tests.R).
I also tried getting the intervals with predict.cobs
On Aug 14, 2014, at 7:17 AM, Jan Stanstrup wrote:
Thank you very much for this snippet!
I used it on my data and indeed it does give intervals which appear quite
realistic (script and data here
On Aug 14, 2014, at 9:06 AM, David Winsemius wrote:
On Aug 14, 2014, at 7:17 AM, Jan Stanstrup wrote:
Thank you very much for this snippet!
I used it on my data and indeed it does give intervals which appear quite
realistic (script and data here
Thanks to all of you for your suggestions and comments. I really
appreciate it.
Some comments to Dennis' comments:
1) I am not concerned about predicting outside the original range. That
would be nonsense anyway considering the physical phenomenon I am
modeling. I am, however, concerned that
To follow up on David's suggestion on this thread, I might add that the
demo(predemo)
in my quantreg package illustrates a variety of approaches to prediction
intervals for
quantile regression estimates. Adapting this to monotone nonparametric
estimation
using rqss() or cobs would be quite
On Aug 12, 2014, at 8:40 AM, Bert Gunter wrote:
PI's of what? -- future individual values or mean values?
I assume quantreg provides quantiles for the latter, not the former.
(See ?predict.lm for a terse explanation of the difference).
I probably should have questioned the poster about
Hi,
I am trying to find a way to estimate prediction intervals (PI) for a
monotonic loess curve using bootstrapping.
At the moment my approach is to use the boot function from the boot
package to bootstrap my loess model, which consist of loess + monoproc
from the monoproc package (to force
On Aug 12, 2014, at 12:23 AM, Jan Stanstrup wrote:
Hi,
I am trying to find a way to estimate prediction intervals (PI) for a
monotonic loess curve using bootstrapping.
At the moment my approach is to use the boot function from the boot package
to bootstrap my loess model, which
PI's of what? -- future individual values or mean values?
I assume quantreg provides quantiles for the latter, not the former.
(See ?predict.lm for a terse explanation of the difference). Both are
obtainable from bootstrapping but the details depend on what you are
prepared to assume. Consult
I would like to ask how exactly the prediction intervals are calculated by
function predict.arima in R. I suppose that the method is same as for the
function forecast (which I am actually using).
Unfortunately I can not find it anywhere.
I am particularly interested in how it works for Arima
From: Greg Snow greg.s...@imail.org
roject.org
Sent: Friday, 17 June 2011, 21:40
Subject: RE: [R] prediction intervals
I am not an expert in time series (that is why I referred you to the task view
rather than give my own inexpert opinion). I do remember from
] On Behalf Of Dave Evens
Sent: Thursday, June 16, 2011 11:33 AM
To: r-help@r-project.org
Subject: [R] prediction intervals
Dear members,
I'm fitting linear model using lm which has numerous auto-regressive
terms as well as other explanatory variables. In order to calculate
prediction
; r-help@r-project.org
Subject: Re: [R] prediction intervals
Thank you for your post Greg.
Do you have any useful references regarding this variability (papers etc)?
Many thanks.
Dave
From: Greg Snow greg.s...@imail.org
To: Dave Evens daveeve...@yahoo.co.uk; r-help@r-project.org
r-help@r
Dear members,
I'm fitting linear model using lm which has numerous auto-regressive terms as
well as other explanatory variables. In order to calculate prediction
intervals, i've used a for-loop as the auto-regressive parameters need to be
updated each time so that a new forecast and
Evens
Sent: Thursday, June 16, 2011 11:33 AM
To: r-help@r-project.org
Subject: [R] prediction intervals
Dear members,
I'm fitting linear model using lm which has numerous auto-regressive
terms as well as other explanatory variables. In order to calculate
prediction intervals, i've
Dear members,
I'm fitting linear model using lm which has numerous auto-regressive terms as
well as other explanatory variables. In order to calculate prediction
intervals, i've used a for-loop as the auto-regressive parameters need to be
updated each time so that a new forecast and
As I understand it, predict.lm(l ,newdata=nd ,interval=confidence) yields
confidence bands for the predicted mean of new observations and lm.predict(l
,newdata=nd ,interval=prediction) yields confidence bands for new
observations themselves, given an lm object l.
However with regard to
When I ask R to compute: predict(data, int = c), following a linear
regression, what is it computing exactly? How are these lower and upper
prediction limits different than what I would get for confidence limits?
Thanks,
Matt.
[[alternative HTML version deleted]]
Dear all,
I am trying to get an estimate of uncertainty surrounding a single predicted
value from a beta regression model (this is similar to a logistic glm - in that
it involves a link function and linear predictor - but it uses the beta
distribution rather than discrete binomial). For
...@comcast.net]
Sent: Friday, 24 April 2009 10:24 PM
To: Michelle Ensbey
Cc: r-help@r-project.org
Subject: Re: [R] prediction intervals (alpha and beta) for model average
estimates from binomial glm and model.avg (library=dRedging)
In R, the predict family of functions provides that facility
: David Winsemius [mailto:dwinsem...@comcast.net]
Sent: Friday, 24 April 2009 10:24 PM
To: Michelle Ensbey
Cc: r-help@r-project.org
Subject: Re: [R] prediction intervals (alpha and beta) for model
average estimates from binomial glm and model.avg (library=dRedging)
In R, the predict family
Hi all,
I was wondering if there is a function out there, or someone has written code
for making confidence intervals around model averaged predictions (y~á+âx). The
model average estimates are from the dRedging library?
It seems a common thing but I can't seem to find one via the search
In R, the predict family of functions provides that facility. If you
want the code it will be in the particular function associated with
the model type.
?predict
?predict.glm
# the example illustrates creation of prediction curves on the
response scale for a specific range of data.
#
] Prediction intervals for zero inflated Poisson
regression
Thierry,
Simon had written some code for this but we never got round to fully
integrate it into the pscl package. A file pb.R is attached, but as a
disclaimer: I haven't looked at this code for a while. It still seems to
work
Dear all,
I'm using zeroinfl() from the pscl-package for zero inflated Poisson
regression. I would like to calculate (aproximate) prediction intervals
for the fitted values. The package itself does not provide them. Can
this be calculated analyticaly? Or do I have to use bootstrap?
What I tried
Dear all,
nbsp;
This is a relist of my previous question. I noticed that some
charactersnbsp;were nbsp;not displayed in the previous version.
nbsp;
nbsp;
Is there a function to calculate thenbsp; prediction intervals for random
effects in non-linear mixed models? I found a way to do it for
Marc Bernard bernarduse1 at yahoo.fr writes:
Is there a function to calculate thenbsp; prediction intervals for random
effects in non-linear mixed
models? I found a way to do it for linear mixed models but not for
non-linearnbsp;mixed one.
Please do not send HTML mail to the list.
A
On Apr 13, 2008, at 1:41PM , Dieter Menne wrote:
Spencer Graves spencer.graves at pdf.com writes:
How can I get prediction intervals from a mixed-effects model?
Consider the following example:
library(nlme)
fm3 - lme(distance ~ age*Sex, data = Orthodont, random = ~ 1)
df3.1 -
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