similar to: Constraining coefficients

Hello R folks, I have three questions. I am trying to run a logistic regression (binomial family) where the response variable is a proportion. According to R Documentation in "a binomial GLM prior weights are used to give the number of trials when the response is the proportion of successes." However when I run my code I get the following error message: Error in

Hello, I've searched for an answer to no avail. I am wondering if anyone knows how to constrain certain correlations to be equal. I have family data with 2 twins per family plus up to 2 siblings. I would like to somehow constrain all the sibling correlations (twin-sib and sib-sib) to be the same while allowing the twin-twin correlation to be different. Here is some simulated code:

Dear R-Users, Being an engineer and not a statistician, my desired course of action may either be impossible or very simple. I am attempting to fit a non-linear model to some measured data. One term in the model contains a square-root, but in the course of regression, this term turns negative and an error occurs. I started using Micrsoft's Excel Solver, and then I turned to NIST's

Hi All, I have two questions: I am computing a linear regression model with 0 as Intercept. Well, I would like the sum of my predicted values be equal to a constant and therefore analyze if my coefficients are significatively different using or not this constraint. Does anyone know how I can constrain my model in a such way? Here is the code: data<-read.table ("input.txt",

Hi All, I am making the switch to R and uncertain which of the several packages for mixed models is appropriate for my analysis. I am waiting for Pinheiro and Bates' book to arrive via inter-library loan, but it will be a week or more before it arrives. I am trying to fit a generalized linear mixed model of survival data (successes/trials) as a function of several categorical fixed and

Hello, I'm working on an structural VAR using the var command to estimate and the svar command on the resultant object (package: vars). I want to constrain coefficients to equal one another, but that value to be estimated. So for the A matrix, I want A[2,1]=A[1,2] to be my constraints. Can this be done with this package? If so, how? If not, is there another package that it might be done with

Does anyone know if there is a way to constrain two virtual registers to be allocated to the same physical register? Tia. Reed

I have a WLS regression with 1 dependent variable and 3 independent variables. I wish to constrain the predicted values (the fitted values) so that they are greater than zero (i.e. they are positive). I do not know how to impose this constraint in R. Please respond if you have any suggestions. There are some previous postings about constraining the coefficients, but this won't accomplish

I have some questions about the use of weights in binomial glm as I am not getting the results I would expect. In my case the weights I have can be seen as 'replicate weights'; one respondent i in my dataset corresponds to w[i] persons in the population. From the documentation of the glm method, I understand that the weights can indeed be used for this: "For a binomial GLM prior

On 06/10/2014 05:51 PM, Pete Cooper wrote: > Hi Reed > > You can do this on the instruction itself by telling it 2 operands > must be the same register. For example, from X86: > > let Constraints = "$src1 = $dst" in > defm INSERTPS : SS41I_insertf32<0x21, "insertps">; > > Thanks, Hi Pete, Sorry. I should have been more specific. I'm

I am trying to automatically construct a distance function from a training set in order to use it to cluster another data set. The variables are nominal. One variable is a "class" variable having two values; it is kept separate from the others. I have a method which constructs a distance matrix for the levels of a nominal variable in the context of the other variables. I want to

All, Could anyone recommend a package that allows the user to constrain the coefficients from a multiple regression equation? I tried using the gl1ce function in lasso2, but couldn't get it to work. I created a contrived example to illustrate my starting point. data(cars) fmla <- formula(dist ~ speed) gl1c.E <- gl1ce(fmla, data = cars) gl1c.E gl1c.E <- gl1ce(fmla, data =

Hello, Apologies if this question has already arised, hope you can help me to the find the solution to this or point the place to look at. I have a multidimensional piece-wise regression linear problem, i.e. to find not only the regression coefficients for each "interval" but also the beginning and ends of the intervals. To simplify it to the one dimensional case and two intervals,

I need to construct confidence intervals for the binomial variance. This is the usual estimate v = x*(n-x)/n or its unbiased counterpart v' = x*(n-x)/(n-1) where x = binomial number of successes observed in n Bernoulli trials from proportion p. The usual X^2 method for variance confidence intervals will not work, because of the strong non-normal character of the sampling

I have an issue using mle in versions of 32 bits. I am writing a package which I want to submit to the CRAN. When doing the check, there is an example that has an error running in the 32 bits version. The problem comes from the mle function, using it with a lower constrain. In 64 bits version it works fine but when I put it in the R 32 bits it fails. (same numbers, all equal!) The call is:

DeaR comRades: I have a 2D spatial binomial process as shown in the data and code below. I am plotting the number of trials and the number of successes in the spatial binomial experiments and would like to draw the spatial cells were the trials and successes were counted, i.e. a partial grid in the plot only for those cells where there is a number. The cells are 2x2 km cells. The count of Trials

Hi everyone, Let me have a dataframe named ?mydata? and created as below, *> n=c(5,5,5,5) #number of trils > x1=c(2,3,1,3) ) #number of successes > x2=c(5,5,5,5) #number of successes > x3=c(0,0,0,0) #number of successes > x4=c(5,0,5,0) #number of successes > mydata=data.frame(n,x1,x2,x3,x4) > mydata* n x1 x2 x3 x4 1 5 2 5 0 5 2 5 3 5 0 0 3 5 1 5 0 5 4 5 3 5 0

Hi, is it possible in one of the libraries on linear methods to constrain the coefficients to be positive? Thanks Chris -- Christoph M. Friedrich | mailto:friedrich at computer.org Gesellschaft f?r Modulfermenterbau mbH (GfM mbH) | http://www.tussy.uni-wh.de/~chris Alfred-Herrhausen Str. 44 ; D-58455 Witten, Germany

Hello, I'm currently using the segmented package of M.R. Muggeo to fit a two-slope segmented regression. I would like to constrain a null-left-slope, but I cannot make it. I followed the explanations of the package (http://dssm.unipa.it/vmuggeo/segmentedRnews.pdf) to write the following code : fit.glm <- glm(y~x) fit.seg <- segmented(fit.glm, seg.Z=~x,psi=0.3) fit.glm

Dear R-gurus: I have a question about lmer. Basically, I have a dataset, in which each observation records number of trials (N) and number of events (Y) given a covariate combination(X) and group id (grp_id). So, my dataset is in tabular form. (in case my explanation of tabular form is unclear, please see the link: