[R] Overdetermined systems
Hello, I have a simple overdetermined system coming from physical measurements. I would like to know if there is a simple way to compute the result of such a system in R. I am aware of the package chebR and the least square methods to provide an optimal solution. But I am really interested in the error propagation, I have variable uncertainty associated to my measurements and would like to propagate them to come up with a confidence interval for the optimal solutions of the system. Any way or library to do that in R? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Overdetermined-systems-tp3033353p3033353.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Uncertainty propagation
Thanks for the help, I start to get reasonable errors on the model... I finally turned to the simpler lm() fitting. As my data from which I fit has only 8 points in each case, I guess it does not make much sense to downweight outliers and use rlm() in this case. -- View this message in context: http://r.789695.n4.nabble.com/Uncertainty-propagation-tp2713499p2715085.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Fitting with error on data
As this forum proved to be very helpful, I got another question... I'd like to fit data points on which I have an error, dx and dy, on each x and y. What would be the common procedure to fit this data by a linear model taking into account uncertainty on each point? Would weighting each point by 1/sqrt(dx2+dy2) (and taking dx and dy as relative errors) in a lm() fit do the job? I would like to propagate uncertainty of the points into the uncertainty of the fit, would that be the case? Thanks for all the help -- View this message in context: http://r.789695.n4.nabble.com/Fitting-with-error-on-data-tp2715100p2715100.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Uncertainty propagation
Thanks a lot for the help, I linearized my power relations en fitted them with a linear rlm() function. When I re-sample my pairs from a bivariate normal distribution for my power law what transformation do I need to apply a transformation to my covariance (vcov) matrix to get back from my linearized regression to my power law space. Thanks -- View this message in context: http://r.789695.n4.nabble.com/Uncertainty-propagation-tp2713499p2714549.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Uncertainty propagation
I have a small model running under R. This is basically running various power-law relations on a variable (in this case water level in a river) changing spatially and through time. I'd like to include some kind of error propagation to this. My first intention was to use a kind of monte carlo routine and run the model many times by changing the power law parameters. These power laws were obtained by fitting data points under R. I thus have std error associated to them: alpha (±da) * WaterHight ^ beta (±db). Is it statistically correct to sample alpha and beta for each run by picking them from a normal distribution centered on alpha (resp. beta) with a standard deviation of da (resp. db) and to perform my statistics (mean and standrad edviation of the model result) on the model output? It seems to me that da and db are correlated in some way and by doing what I entended to, I would overestimate the final error of my model... My statistical skills are rather weak, is there a way people usually deal with this kind of problems? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Uncertainty-propagation-tp2713499p2713499.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] GRASS raster data processing
Hi, I just imported two raster maps into R using the SPGRASS6 package, one containing elevation data and the other containing an erosion index: Kar_inc -readRAST6(Incis_Kar, plugin=FALSE) Kar_dem - readRAST6(DEM_Kar, plugin=FALSE) I just wanted to make a xy plot of erosion parameter vs elevation. How does this work? I don't get how to handle SpatialGridDataFrames... Thanks a lot Maarten -- View this message in context: http://www.nabble.com/GRASS-raster-data-processing-tp23981740p23981740.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
