search for: refitting

Dear R users, I'm trying to use function 'refit' from lme4 and I get this error that I can't understand: > refit(dolo4.model4,cbind(uu,50-uu)) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "refit", for signature "mer", "matrix" if I try: > refit(dolo4.model4,uu) Error in asMethod(object) :

Hi all, Just a small question: After fitting a multivariate mixture using flexmix, I wish to use refit to get the parameters of covariates and their standard errors. However using refit I only can see the components for the first dependent variable. What should I do if I want to see the others? Thanks a lot, Eric -- View this message in context:

Dear All, Is there is a fast way of refitting lm() when the design matrix stays constant but the response is different? For example, y1 ~ X y2 ~ X y3 ~ X ...etc. where y1 is the 1st instance of the response vector. Calling lm() every time seems rather wasteful since the QR-decomposition of X needs to be calculated only once. It would be n...

Hi all, I'm trying to do a simple curve fit and coming up with some interesting results I would like to get comment on. So as shown below, tsR is my explanatory and response is... well... my response. This same data in gnumeric gets fitted with the curve "response=10078.4 + 1358.67 * ln (explanatory - 2009.07) So I'm using nls with the start values supplied by gnumeric. in

Greetings I'm a relatively new R user and I'm trying to build a latent class model. I've used the 'R Site Search' and it appears there's not much dialogue on these packages On mmlcr, I've gotten it working, but not sure if I'm using it correctly. On flexmix, I can only seem to get results for one class. I'm attaching my code below - if anyone

Dear All: I updated my R program as well as associated packages yesterday. Currently my R version is 2.2.1 running under WINXP SP-2. When I tried to list (summary) an nlme object that I developed before, I got the following error message: [ Error in .C("ARMA_constCoef", as.integer(attr(object, "p")), as.integer(attr(object, : C entry point "ARMA_constCoef"

Hi again! I have a question about R. I have done gam in previous version of R with "mgcv" package and saved the workspace. This workspace contains different models and I will do prediction by these GAMs. However, I install new version of R. and use the same workspace. when I type summary(models), and the error message showed Error in Predict.matrix.cr.smooth(object, dk$data) : F is

...84-191 define general classes of influence measures for multivariate regression models, including analogs of Cook's D, Andrews & Pregibon COVRATIO, etc. As in univariate response models, these are based on leverage and residuals based on omitting one (or more) observations at a time and refitting, although, in the univariate case, the computations can be optimized, as they are in stats::influence() and related methods. I'm interested in exploring the multivariate extension in R. I tried the following, and was surprised to find that R returned a result rather than an error -- presuma...

What??s happening with this following code: require(sem) Celpe.Mod.RAM <- matrix(c( # path parametro Inicio "Produ????o -> T1", "gamma.11", NA, "Produ????o -> T2", "gamma.12", NA,

-How to install CentOS on 27? imac (Mid 2010 - 2.93 Ghz, i7, 8gig ram) with OS X and Xubuntu 13.04 already installed Assumed - OS X and Xubuntu already installed and rEFIt already installed in OS X with the line towards the bottom of: /efi/refit/refit.conf set to: default_selection L on the mac root partition so Linux will boot by default -Boot centos 6.4 lived dvd and install btrfs tools

...R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know how to do it in R. Is 'training' and 'training.reports' in (AMORE) able to do it? Thank you for any light on this. Ilh

This issue has arisen within my anova.coxph routine, but is as easily illustrated with glm. testdata <- data.frame(y= 1:5, n= c(8,10,6,20,14), sex = c(0,1,0,1,1), age = c(30,20,35,25,40)) fit <- glm(cbind(y,n) ~ age + sex, binomial, data=testdata, model=TRUE) saveit <- fit$model update(fit, .~. - age, data=saveit)

I am trying to work out a bootstrapped Tobit regression model. I get the coefficients all right, but they all have standard error zero. And I am unable to figure out why. I know the coefficients are correct because that's what I get when do a Tobit (without bootstrapping). Here's my code: # Bootstrap 95% CI for Tobit regression coefficients? library(boot) library(AER) # for the Affairs

Hi - I am using polr. I can get a result from polr fit by calling result.plr <- polr(formula, data=mydata, method="probit"); However, from the 'result.plr', how can I access standard error of the estimated coefficients as well as the t statistics for each one of them? What I would like to do ultimately is to see which coefficients are not significant and try to refit the

Christoph -- There was just a thread on this earlier this week. You can search in the archives for the title: "refitting lm() with same x, different y". (Actually, it doesn't turn up in the R site search yet, at least for me. But if you just go to the archive of recent messages, available through CRAN, you can search on refitting and find it. The original post was from William Valdar, on April 19.) Hope t...

R2.3, WinXP Dear all, I am using the following functions: f1 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(x))/exp(log(Phi4))) f2 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(r)-log(x))/exp(log(Phi4))) subject to the residual weighting Var(e[i]) = sigma^2 * abs( E(y) )^(2*Delta) Here is my question, in steps: 1. Function f1 is separately fitted to two different datasets corresponding to

...o uniformly reduce the size of graphics outputs? It appears that the png() device outputs 5-inch by 5-inch images, and I am trying to change my whole script to produce 4x4 images with the same proportions. I tried specifying parameters within each png(), but it reduced the size of the file without refitting the contents so the graph title went off the margin. This is an example of the code I used: png("graph2.png",height=432, width=432, units="px", res=96) What would be the best way to generate 4x4, proportional graphs? Thanks! [[alternative HTML version deleted]]

Hi list, I am building a regression model with categorical predictor variable coded by treatment contrasts. The summary of the regression model shows that some levels are significant while others are not. The significant ones show that they are statistically significant from the basis factor (at level 0). How do we generally deal with those insignificant levels? If they are used for

Hi all! I am working with GLMM using the binomial family I use the following codes I dropped no significant terms, refitting the model and comparing the changes with likelihood: G.1<-lmer(data$Ymat~stu+spi+stu*sp1+(1|ber),data=data,family="binomial") G.1b<-lmer(data$Ymat~stu+spi+(1|ber),data=data,family="binomial") anova (G.1,G.2) But, when I want to evaluate the significance of random effec...

I've been trying several combinations of OSX, CentOS to try and get CentOS installed on an old iMac. I finally first installed OS X, then installed CentOS in the open space after OS X. With refit installed and selecting CentOS, it starts booting but get a screen that a boot device can't e found. So I then install Xubuntu with the option to replace OS X. After Xbuntu is installed and then