On Oct 8, 2012, at 5:17 PM, Eiko Fried wrote:
>> It **might** also help the OP clarify his intent in
>> exactly the way you describe (or not).
>
>
> My intent is to perform a linear regression on a metric dependent variable.
What do you mean by "a metric dependent variable"?
> Unfortunately,
> It **might** also help the OP clarify his intent in
> exactly the way you describe (or not).
My intent is to perform a linear regression on a metric dependent variable.
Unfortunately, two key assumptions - normal distribution and
homoscedasticity - cannot be met.
I read that using a robust reg
On 08/10/2012 00:37, Bert Gunter wrote:
Have you checked the Robust task view on CRAN?? Would seem that that
should have been the first place to look.
It is still a conceptual question. I presume this means an ordered
response, and then we need to know what is meant by 'regression'.
If you
I don't know about the topic of your question. Have you used the RSiteSearch
function to research it yourself?
---
Jeff NewmillerThe . . Go Live...
DCN:Basics: ##.#. ##.
Have you checked the Robust task view on CRAN?? Would seem that that
should have been the first place to look.
-- Bert
On Sun, Oct 7, 2012 at 3:30 PM, Eiko Fried wrote:
> Thank you Jeff! Please ignore the first of my two questions then, and
> apologies for not making it clear that my second ques
Thank you Jeff! Please ignore the first of my two questions then, and
apologies for not making it clear that my second question was about R.
(2) "Are there ways of using robust regressions with ordered data" ... in
R?
Thank you
On 7 October 2012 18:26, Jeff Newmiller wrote:
> This does not ap
This does not appear to be a question about R. You should post in a list or
forum dedicated to discussing statistics theory, such as
stats.stackoverflow.com.
---
Jeff NewmillerThe . .
I have two regressions to perform - one with a metric DV (-3 to 3), the
other with an ordered DV (0,1,2,3).
Neither normal distribution not homoscedasticity is given. I have a two
questions:
(1) Some sources say robust regression take care of both lack of normal
distribution and heteroscedasticit
Hi,
I need to perform a robust regression with several dummy independent
variables (i.e., binary), which are paired (clustered). The outcome
(dependent variable) is continuous.
I have tried using the robcov function with the next syntaxis:
out1 <- ols(y ~ x1 + x2 + x3 , x=T)
robustclust <- ro
Hi R,
Can I perform a stepwise method for robust regression? In other words,
how do I combine the methods "step" and "rlm" together?
Thanks and Regards,
Shubha
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__
R
"You could try rlm in the MASS package; it doesn't use he resampling
step."
That seems to do the trick. Thank you!
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Hello.
I've got a dataset that may have outliers in both x and y. While I am not
at all familiar with robust regression, it looked like the function lmrob in
package robustbase should handle this situation. When I try to use it, I
get:
Too many singular resamples
Aborting fast_s_w_mem()
Looki
On Jan 22, 2009, at 11:06 AM, Georg Ehret wrote:
Dear Miss R,
I have a large dataset that is skewed and would like to calculate
confidence intervals for my regression line. Unfortunately I have
trouble
finding commands for rubust regression and in particular for the
confidence
intervals
Dear Miss R,
I have a large dataset that is skewed and would like to calculate
confidence intervals for my regression line. Unfortunately I have trouble
finding commands for rubust regression and in particular for the confidence
intervals... Can you possibly give a hint?
Thank you and best rega
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