Well, first of all, note that there is no "lms" method for the stats
package's lm() function. You can't just make stuff up, you know!
And second, ?lmsreg -- after loading MASS via library(MASS), if you
haven't already done this after your install -- is what you want.
Other than ?lmsreg and what Enrico pointed you to, however, you'll
have to manage on your own. Statistical tutorials are not the remit of
this list. You might wish to consult with someone locally for help.
You may be able to get an answer to a post of a specific question
about usage **if you post code that fails** and otherwise follow the
posting guide (below).
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Sat, Oct 8, 2016 at 1:10 PM, Bryan Mac <bryanmac...@gmail.com> wrote:
> I am confused reading the document.
>
> I have installed and added the package (MASS).
>
> What is the function for LMS Regression?
>
>
> Bryan Mac
> bryanmac...@gmail.com
>
>
>
>> On Oct 8, 2016, at 6:17 AM, Enrico Schumann <e...@enricoschumann.net> wrote:
>>
>> On Sat, 08 Oct 2016, Bryan Mac <bryanmac...@gmail.com> writes:
>>
>>> Hi R-help,
>>>
>>> How do you perform least median square regression in R? Here is what I have
>>> but received no output.
>>>
>>> LMSRegression <- function(df, indices){
>>> sample <- df[indices, ]
>>> LMS_NAR_NIC_relation <- lm(sample$NAR~sample$NIC, data = sample, method =
>>> "lms")
>>> rsquared_lms_nar_nic <- summary(LMS_NAR_NIC_relation)$r.square
>>>
>>> LMS_SQRTNAR_SQRTNIC_relation <- lm(sample$SQRTNAR~sample$SQRTNIC, data =
>>> sample, method = "lms")
>>> rsquared_lms_sqrtnar_sqrtnic <-
>>> summary(LMS_SQRTNAR_SQRTNIC_relation)$r.square
>>>
>>> out <- c(rsquared_lms_nar_nic, rsquared_lms_sqrtnar_sqrtnic)
>>> return(out)
>>> }
>>>
>>> Also, which value should be looked at decide whether this is best
>>> regression model to use?
>>>
>>> Bryan Mac
>>> bryanmac...@gmail.com
>>>
>>
>> A tutorial on how to run such regressions is included
>> in the NMOF package.
>>
>> https://cran.r-project.org/package=NMOF/vignettes/PSlms.pdf
>>
>>
>> --
>> Enrico Schumann
>> Lucerne, Switzerland
>> http://enricoschumann.net
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
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