similar to: Rolling regression - o/p selected coefficients

Hi everyone, I am trying to apply linear regression to adjacent columns in a matrix (i.e. col1~col2; col3~col4; etc.). The columns in my matrix come with identifiers at the top of each column, but when I try to use these identifiers to reference the columns in the regression function using rollapply(), the columns are not recognised and the regression breaks down. Is there a more robust way to

Hi everyone, I'm experiencing difficulty getting the results I want when I use a nested for loop. I have a data set to which I perform some calculations, and then try to apply a regression over a rolling window. The code runs, but the regression results I am getting (intercept and slope) are simply the same, repeated again and again in the results matrix. The regression does not seem to be

Hi! I have a question that is probably very basic, but I cannot figure out how to do it. I simply need to compare the significance of a regression slope against a slope of 1, instead of the default of zero. I know this topic has been posted before, and I have tried to use the advice given to others to fix my problem. I tried the offset command based on one of these advice threads as follows:

Hello, I want to know if two quadratic regressions are significantly different. I was advised to make the test using step 1 bootstrapping both quadratic regressions and get their slope coefficients. (Let's call the slope coefficient *â*^1 and *â*^2) step 2 use the slope difference *â*^1-*â*^2 and bootstrap the slope coefficent step 3 find out the sampling distribution above and

Have you tried web searching on " R constrained linear regression" or similar. There seemed to be resources related to your issues when I looked. You might also search on rseek.org . There are apparently several packages that do regression with constraints, but I don't know if they fit your situation. Cheers, Bert Bert Gunter "The trouble with having an open mind is that

Thank you, Greg, and also to Scott Ellison, who replied privately. I am in the process of trying out both suggestions. After I sent my initial message, I came across the Systemfit package, which allows specification of constraints on parameters. In theory, this should solve my problem perfectly. However, I was not able to get it to work with my data, as every attempt yielded the following

This is a quadratic programming problem that you can solve using either a quadratic programming solver with constraints or a general nonlinear solver with constraints. See https://cran.r-project.org/web/views/Optimization.html for more info on what is available. Here is an example using a nonlinear least squares solver and non-negative bound constraints. The constraint that the coefficients sum

Ok, and if I would just like to force my slope coefficients to be inside an interval, let's say, between 0 and 1? Is there a way in R to formulate such a constraint regression? Thanks in advance and kind regards, Aljosa Aljosa Aleksandrovic, FRM, CAIA Quantitative Analyst - Convertibles aljosa.aleksandrovic at man.com Tel +41 55 417 7603 Man Investments (CH) AG Huobstrasse 3 | 8808

If the slope coefficients sum to a constant, the regressors are dependent and so a unique solution is impossible (an infinity of solutions would result). So I think you have something going on that you don't understand and should consult a local statistician to help you formulate your problem appropriately. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people

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The nls2 package can be used to get starting values. On Thu, Apr 28, 2016 at 8:42 AM, Aleksandrovic, Aljosa (Pfaeffikon) <Aljosa.Aleksandrovic at man.com> wrote: > Hi Gabor, > > Thanks a lot for your help! > > I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to

Folks, I asked a question on this mailing list about the subset support of lm(). In a flash, I got three helpful responses from Rajarshi Guha <rxg218 at psu.edu> <http://jijo.cjb.net> Erin Hodgess <hodgess at gator.uhd.edu> and Peter Dalgaard <p.dalgaard at biostat.ku.dk> :-) and it was just great. The mistake I was making was in not understanding the notion of a

Hello R-users: I am using "yags" for fitting GEE which is giving me the same result as "Proc GENMOD". Now I have couple of questions related to yags output. (By the way, someone told me to run the geeglm for the same analysis and I did run but did not get the same result as of genmod and don't know how to correct the geeglm codes so that all three will be same!)

Hi there, I've got a question which is really trivial for sure but still I have to ask as I'm not making any progress solving it by myself (please be patient with an undergraduate student): I've got a linear model (lm and lmer fitted with method="ML"). Now I want to compare the coefficients (slope, intercept, not the random effects) of both models with a given value (e.g.

Hi Gabor, Thanks a lot for your help! I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to be inside an interval, let?s say, between 0 and 1, what kind of starting values are normally recommended for the start argument (e.g. Using a 4 factor model with b1, b2, b3 and b4, I tried

Dear R users, I have run into a very unexpected problem and I was hoping someone could explain it to me. I have a 650 000 by 12 matrix and I want to perform a rolling regression on it, width 36 or 48, using the package performanceAnalytics. ie: rol.lm<-rollingRegression(lm(y~x1+x2+x3+x4+x5),data=denise,width=36) The regressions occur without a problem and I store my output (coefficients,

Dear all, I am a relative novice with R, so please forgive any terrible errors... I am working with a GLM that describes a response variable as a function of a categorical variable with three levels and a continuous variable. These two predictor variables are believed to interact. An example of such a model follows at the bottom of this message, but here is a section of its summary table:

I am interested in whether the slopes in a linear model are different from 0. I.e. I would like to obtain the slope estimates, and their standard errors, ``relative to 0'' for each group, rather than relative to some baseline. Explicitly I would like to write/represent the model as y = a_i + b_i*x + E i = 1, ..., K, where x is a continuous variate and i indexes groups (levels of a

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of

Dear all, I have spent the last few days on a seemingly simple and previously documented rolling regression. I have a 60 year data set organized in a ts matrix. The matrix has 5 columns; cash_ret, epy1, ism1, spread1, unemp1 I have been able to come up with the following based on previous help threads. It seems to work fine. The trouble is I get regression coefficients but need the immediate