Dear RsR list members, I somehow got stuck wth lmRob and I am sure I am missing something and I do not know what. It is my first time using robust statistics and I have read all your posts and pdfs recommended I think it is likely to be a newbie question. I ploted two variables and the pictures shows a number of outliers that I want to identify given that the main trend between those two measurements is positive. (although they look like a lot, it is more or less 800 points out of 6000) I used then robust<-lmRob(WPROB~NPP,data=a) lmRob(formula = WPROB ~ NPP, data = a) Coefficients: (Intercept) NPP 499.86274538 -0.00990502 Degrees of freedom: 6002 total; 6000 residual Residual standard error: 250.3199 1) I do not really understand the slope of the regression, why is it negative??? then used plot.lmRob(robust) and choose for option 11 (scatter plot with fits) What is meant to be the x axis (WPROB) turns into the y axis. The regression line and the confidence intervals shown have nothing to do, of course, with the coefficients retrieved...what am I doing wrong? Thank you in advance, pep -------------- next part -------------- An HTML attachment was scrubbed... URL: <https://stat.ethz.ch/pipermail/r-sig-robust/attachments/20101019/aaa084eb/attachment.html> -------------- next part -------------- A non-text attachment was scrubbed... Name: moz-screenshot-1.png Type: image/png Size: 7291 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-sig-robust/attachments/20101019/aaa084eb/attachment.png> -------------- next part -------------- A non-text attachment was scrubbed... Name: moz-screenshot-2.png Type: image/png Size: 19908 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-sig-robust/attachments/20101019/aaa084eb/attachment-0001.png> -------------- next part -------------- A non-text attachment was scrubbed... Name: josep_serra.vcf Type: text/x-vcard Size: 448 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-sig-robust/attachments/20101019/aaa084eb/attachment.vcf>
[RsR] robust scatterplot with fits
2 messages · Pep Serra, Matias Salibian-Barrera
Hello Pep,
If NPP is the response variable (the "y" variable) and WPROB is the
explanatory variable (the "x" variable), then you should have used
lmrob(NPP ~ WPROB, data=a)
Also, I recommend you use the latest version of the "robustbase" package
> install.packages('robustbase')
> library(robustbase)
There the function for a robust regression fit is called lmrob (not
lmRob, which is now deprecated).
Finally, based on your plot, you might also want to consider a model
incorporating log(WPROB), since the relationship does not appear to be
quite linear to the naked eye.
Hope this helps.
Matias
On 10-10-19 07:32 AM, Pep Serra sat down at the computer and wrote:
Dear RsR list members, I somehow got stuck wth lmRob and I am sure I am missing something and I do not know what. It is my first time using robust statistics and I have read all your posts and pdfs recommended I think it is likely to be a newbie question. I ploted two variables and the pictures shows a number of outliers that I want to identify given that the main trend between those two measurements is positive. (although they look like a lot, it is more or less 800 points out of 6000) I used then robust<-lmRob(WPROB~NPP,data=a) lmRob(formula = WPROB ~ NPP, data = a) Coefficients: (Intercept) NPP 499.86274538 -0.00990502 Degrees of freedom: 6002 total; 6000 residual Residual standard error: 250.3199 1) I do not really understand the slope of the regression, why is it negative??? then used plot.lmRob(robust) and choose for option 11 (scatter plot with fits) What is meant to be the x axis (WPROB) turns into the y axis. The regression line and the confidence intervals shown have nothing to do, of course, with the coefficients retrieved...what am I doing wrong? Thank you in advance, pep
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_____________________________________________________ Matias Salibian-Barrera - Department of Statistics The University of British Columbia Phone: (604) 822-3410 - Fax: (604) 822-6960 "The plural of anecdote is not data" (George Stigler?)