search for: gression

Hello In STATA there is command which has regression with clustering option. Can anyone tell me what is the command for the same in R for example in STATA its regress Height Weight, cluster(id) Thanks in Advance Regards Sunita -- View this message in context: http://www.nabble.com/Regression---cluster-option-in-STATA-tp25241948p25241948.html...

Please explain me as what it means and how this analysis can be done using R and which library(ies) are needed. Thanks -- View this message in context: http://www.nabble.com/Regression-using-age-and-Duration-of-disease-as-a-continous-factors-tp24574133p24574133.html Sent from the R help mailing list archive at Nabble.com.

Hi everyone! I'm new to R, and I'm stuck on a problem I don't know how to approach. I have calculated a regression in the form of M ~ D + O + S, and I would like to take this regression and test it with other samples, 5 at a time(5 meaning 5 set, each consisting M, D, O, and S of a specific date). I assume I'll need a for loop. Right now, My data of M, D, O, and S are all stored in separate txt files, but s...

Hi, I am working with CART regression now to predict a probability; the response is binary. Could anyone tell me in which cases it is better to use mean square error for splitting nodes and when mean absolute error should be preferred. I am now using the default (MSE) version and I can see that the obtained optimal tree is very differe...

...-fit (in the least squares sense) a straight line through a set of data points. Thanks a lot to all replying to it. I managed it in Matlab using a function 'fit_3D_data' (link: http://webscripts.softpedia.com/script/Scientific-Engineering-Ruby/Statistics-and-Probability/Orthogonal-Linear-Regression-in-3D-space-35532.html). But to add a 'simple' constraint to the problem, I am stuck. Here it is: I have two sets of data points, representing two separate straight lines (call A and R) that intersect at a point. I need to find the least-square fit to these lines, subject to their interse...

i have a series of regressions i need to run where everything is the same except for the dependent variable, e.g.: lm(y1 ~ x1+x2+x3+x4+x5, data=data) lm(y2 ~ x1+x2+x3+x4+x5, data=data) lm(y3 ~ x1+x2+x3+x4+x5, data=data) is it possible to run all these regs with a single command? given that the bulk of the work for linear reg...

Hi, I'm sure these are basic problems so I apologise in advance for my ignorance. I have a dataset with X, Y, and a Factor with 4 levels. I am trying to figure out how to use lm() to find the r2 value, slope and intercept of an X~Y regression, for each level of the Factor. Is there a way to do this automatically without having to specify different subsets of the factor? my code so far: Regression<-lm(Y~X, data=dataset, subset=factor(Factor)) Also, when I plot this dataset using: xyplot(Y~X|factor(Factor), data=dataset, aspect=&q...

I have a large dataset grouped by a factor and I want to perform a regression on each data subset based on this factor. There are many ways to do this, posted here and elsewhere. I have tried several. However I found one method posted on the R wiki which works exactly as I want, and I like the elegance and simplicity of the solution, but I don't understand how it works....

...ggplot2) ggplot(coef.long,aes(x=variable,y=value,fill=Model))+ ? geom_bar(stat="identity",position="dodge")+ ? scale_fill_discrete(name="Model", ? labels=c("Multiple", "Simple"))+ ? labs(title =paste('La diff?rences des coefficients ? entre la r?gression multiple et simple'), ? x="Models",y="Coefficient")+ ? coord_flip() ? ? ?

...,aes(x=variable,y=value,fill=Model))+ > geom_bar(stat="identity",position="dodge")+ > scale_fill_discrete(name="Model", > labels=c("Multiple", "Simple"))+ > labs(title =paste('La diff?rences des coefficients > entre la r?gression multiple et simple'), > x="Models",y="Coefficient")+ > coord_flip() > > > > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r...

Folks, my question is not R-specific, but I've struck out twice on sci.stat.consult, so I'm turning to the R community. Even if it's a silly question, I expect that someone present will probably tell me so... I have been using multiple Poisson GLMs and similar count-re?gression models to analyse forest songbird abundance data. Many of the spe?cies-level models seem to fit the data pretty well. My next task is to validate/verify/test these models using an independen?t dataset collected for this purpose (no, really!) It seems obvious that I should apply predict.glm() to th...

...ths. Add ext2fs_flush2() and ext2fs_close2() which takes a flag to allow the fsync() to be skipped. Added a test for extent-mapped journals by mke2fs and tune2fs. Added a test for creating a large (over 4GB) journal using mke2fs. Added a test to make sure the inode size remains constant. The regression test script now prints the list of which tests failed. The regression test system now uses /tmp for its scratch files, which is often a tmpfs mounted file system and hence much faster. The i_e2image test was fixed so it works with valgrind. Tests that rely on debugfs are now skipped if debugfs w...

...ll.Venables at csiro.au) 39. j and jcross queries (Robert Biddle) 40. Questions about histograms (Andre Nathan) 41. Re: Questions about histograms (Duncan Murdoch) 42. Re: Questions about histograms (Bill.Venables at csiro.au) 43. Re: Questions about histograms (Andre Nathan) 44. Re: Re gression with time-dependent coefficients (dss) 45. Using R in a university course: dealing with proposal comments (Arin Basu) 46. Re: Using R in a university course: dealing with proposal comments (Bill.Venables at csiro.au) 47. Re: Using R in a university course: dealing with proposal...