To: Simone Mascia
Cc: r-help@r-project.org
Subject: Re: [R] Robust standard error
[External Email]
On Sun, 02 Oct 2022, Bert Gunter writes:
> On Sun, Oct 2, 2022 at 6:42 AM Simone Mascia
>
> wrote:
>
>> Is there a way to estimate Robust standard errors when using a nls()
On Sun, 02 Oct 2022, Bert Gunter writes:
> On Sun, Oct 2, 2022 at 6:42 AM Simone Mascia
> wrote:
>
>> Is there a way to estimate Robust standard errors when using a nls()
>> function? I'm trying to fit some data to a complicated model and everything
>> works fine with nls() but I also wanted to o
You may get a helpful response here, but generally speaking, this list is
about R **programming**, and statistical issues/tutorials are off topic.
You might try
https://stackoverflow.com/questions/tagged/statistics
if you don't get adequate help here.
-- Bert
On Sun, Oct 2, 2022 at 6:42 AM Simone
Is there a way to estimate Robust standard errors when using a nls()
function? I'm trying to fit some data to a complicated model and everything
works fine with nls() but I also wanted to obtain a robust estimate of my
errors.
I tried "coeftest(m, vcov=sandwich)" and it seems to work, but so does
On Sun, 28 Sep 2014, Arnab Dutta wrote:
Hi,
In order to have robust standard errors in R, what would be the command
that can generate results similar to the "robust" option in STATA?
This usually refers to sandwich standard errors aka HC or HC0 in case of
the linear regression model. Thes
Arnab Dutta gmail.com> writes:
>
> Hi,
>
> In order to have robust standard errors in R, what would be the command
> that can generate results similar to the "robust" option in STATA? I tried
> using the "lmrob" command from the package "robustbase". With that, the
> Adjusted R squared is q
Hi,
In order to have robust standard errors in R, what would be the command
that can generate results similar to the "robust" option in STATA? I tried
using the "lmrob" command from the package "robustbase". With that, the
Adjusted R squared is quite different from the normal "lm" command. Thi
Is the (non-clustered) sandwich estimator really robust to autocorrelation?
Thanks
Frank
-
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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Sent from t
It depends on what you mean by "robust." Robust to what?
I recommend looking at the sandwich package which gives
heteroskedasticity and autocorrelation robust variance/covariance
matrices. For instance, you could do the following to get your OLS
estimates with heteroskedasticity consistent
Hi,
I have ove the robust standard error of an estimator but I don't know how to
do this.
The code for my regression is the following:
reg<-lm(fsn~lctot)
But then what do I need to do?
--
Charlène Lisa Cosandier
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