similar to: Constrain coefs. in linear model to sum to 0

Dear all, Even after a couple of hours looking at old messages I still haven't found a solution for my problem. I'm trying to fit an additive linear regression model with 2 effects, both fixed, to some dataset. The function contrasts(effectA) <- contr.sum can gaurantee that the coefficients per parameter sum to one, and the function dummy.coef provices the estimates of all

Howdee, I am able to fit a 4-parameter logistic growth curve to a dataset which comprise many individuals (using R v. 2.3.1). Yet, if I want to obtain the parameters for each individual (i.e., for each 'id') using nlsList, then I obtain an Error message which I have trouble interpreting. Any advice as to how I can solve this problem? Thanks for your time, Marc > reg <-nls(mass ~

Hi, I have fit a series of ols() models, by group, in this manner: l <- ols(y ~ rcs(x, 4)) ... where the series of 'x' values in each group is the same, however knots are not always identical between groups. The result is a table of 'coefs' derived from the ols objects, by group: group Intercept top top' top'' 1 6.864 0.01 2.241 -2.65

Dear useRs, I have used using the excellent mgcv package (version 1.7-12) to create a generalized additive model (gam) including random effects - represented with s(...,bs="re") - on the basis of dialect data. My model contains two random-effect factors (Word and Key - the latter representing a speaker) and I have added both random intercepts and various random slopes for these

Hi All, So I thought I had this acts_as_paranoid problem previously and fixed by going to rails 0.14.2, although I''m not 100% it was the same thing. I''m now on rails 0.14.3 and I''m getting: undefined method `constrain'' for class `Class'' for any model that includes acts_as_paranoid. My application trace is below. Has anyone else got this and know a

Here's a couple quick patches to resolve some minor issues related to multiple outputs that I bumped into today. nodetectfix.patch: Enables reading outputs config (instead of 640x480 default) when using "--no-detection" switch but config says detect. constrainyoutput.patch: Allows full vertical workarea to be used in the case of multiple vertical outputs. Currently it won't

----- Original Message ----- > > > Hi, > > How can I specify in a .td file that source and destination should > not use the same register? I think that you can use the EarlyClobber operand flag to achieve this (TableGen has an @earlyclobber constraint; there are some examples in the ARM backend). -Hal > > Thanks. > > > * Elena > > > >

Many thanks to those took time replied to my question. They were very helpful and I solved my problem by reparameterization. With the help of optim() the fitting procedure is very robust and insensitive to initial starting value. Once again, many thanks. Kevin -------- Original post --------- >Dear R and S users, > >I've been working on fitting finite mixture of negative

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

> On May 27, 2018, at 10:31 PM, francesc badia roca <fbr600 at gmail.com> wrote: > > 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

I'm plotting some trip length frequencies using the following code: plot( density(zTestData$Distance, weights=zTestData$Actual), xlim=c(0,10), main="Test TLFD", xlab="Distance", col=6 ) lines(density(zTestData$Distance, weights=zTestData$FlatWeight), col=2) lines(density(zTestData$Distance, weights=zTestData$BrdWeight ), col=3) which works fine except the

Hi, How can I specify in a .td file that source and destination should not use the same register? Thanks. - Elena --------------------------------------------------------------------- Intel Israel (74) Limited This e-mail and any attachments may contain confidential material for the sole use of the intended recipient(s). Any review or distribution by others is strictly prohibited. If

On Jul 16, 2013, at 9:32 AM, Hal Finkel <hfinkel at anl.gov> wrote: > ----- Original Message ----- >> >> >> Hi, >> >> How can I specify in a .td file that source and destination should >> not use the same register? > > I think that you can use the EarlyClobber operand flag to achieve this (TableGen has an @earlyclobber constraint; there are

Dear R and S users, I've been working on fitting finite mixture of negative exponential distributions using maximum likelihood based on the example given in MASS. So far I had much success in fitting two components. The problem started when I tried to extend the procedure to fit three components. More specifically, likelihood = sum( ln(c1*exp(-x/lambda1)/lambda1 + c2*exp(-x/lambda2)/lambda2

Hello, I managed to "linearize" my LDA decision boundaries now I would like to call abline three times but be able to specify the exact x range. I was reading the doc but it doesn't seem to support this use-case? are there alternatives. The reason why I use abline is because I first call plot to plot all the three datasets and then call abline to "append" these decision

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:

I wonder if someone could explain how the following R idiom works (it's used in glm.fit). start <- coef start[fit$pivot] <- coef coef is a vector of coefficients, set by .Fortran("dqrls", ...). fit$pivot is a vector of integer indexes (indicating how dqrls permuted the columns of x). If coef has n elements, fit$pivot is a permutation of seq(1,5). start[fit$pivot]

Dear Martin, I think that your plan makes sense. It's too bad that aov() behaved differently in this respect from lm(), and thus created more work, but it's not be a bad thing that the difference is now explicit and documented. I expect that that other problems like this will surface, particularly with contributed packages (and I know that you're aware that this has already happened

Dear list, I was wondering if it is possible to get the p-values for one-sided tests on the parameters of a linear regression. For instance, I use lm() and store the result in an object. lm() gives me a matrix, using summary() and coef() on which gives me a matrix containing the coefficients, the standard errors, the t-statistics and the two-sided p-values by default. Can I get it to provide me

Dear List, Must be a silly question, but I was wondering whether there is a direct way of calculating "standard error of a HR or exp(coef)" from coxph objects x <- coxph(Surv(time, status) ~ age + inst, lung)> x coef exp(coef) se(coef) z page 0.0190 1.02 0.00925 2.06 0.04inst -0.0104 0.99 0.01028 -1.01 0.31 cheers, Ehsan [[alternative HTML