search for: refit

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(dolo...

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: http://r.789695.n4.nabble.com/Refit-for-flexmix-tp37...

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...

...774.02 10465.94 10319.95 > tsR [1] 2009.167 2009.250 2009.333 2009.417 2009.500 2009.583 2009.667 2009.750 [9] 2009.833 2009.917 2010.000 2010.083 2010.167 2010.250 2010.333 2010.417 [17] 2010.500 2010.583 2010.667 ***********************First Time Through******************************* > reFit <- nls(response ~ A + B * log(tsR - C), start=c(A=10000,B=1350,C=2000)) Error in numericDeriv(form[[3L]], names(ind), env) : Missing value or an infinity produced when evaluating the model In addition: Warning message: In log(tsR - C) : NaNs produced ***********************Second Time Through...

...tays+ CROStays+ DaysSinceLastStay+ wghtmean_median_age_pop100+ perc_num_white+ perc_num_hispanic+ perc_CROStays+ numMonthsActive+ WEBStays+ property_loyalty+ perc_NoZone+ rho+ PCR_Dummy_class+ ltgold1+ ltgold3+ p_hat_PCR, data = data, k = 2, model = FLXglm(family = "poisson")) rm1<-refit(m1) summary(rm1) Scott Geller Advanced Analytics, Decision Sciences Department, InterContinental Hotels Group 770-604-5149

...got the following error message: [ Error in .C("ARMA_constCoef", as.integer(attr(object, "p")), as.integer(attr(object, : C entry point "ARMA_constCoef" not in DLL for package "nlme" ] The nlme object was fitted with corr = corARMA(q=2) option. I refitted the model, and the same error message appeared. I then refitted the model with option corr = corARMA(p=1), then no problem; but for p = 2, or q = 1 or 2, then the error occurred. When I listed the same fitted nlme objects under R 2.1.1 with nlme 3.1-65, then no problem. I fitted the Ovary data...

...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 missing from cr smooth - refit model with current mgcv. this workspace is normal when I used previous version of R. What's wrong?! Thank in advance. -- View this message in context: http://r.789695.n4.nabble.com/About-Error-message-tp4634955.html Sent from the R help mailing list archive at Nabble.com.

...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 -- pre...

...2", NA, "Compreens??o <-> Produ????o", "alpha.21", NA ), ncol=3, byrow=TRUE) Celpe.cov <- cov(Celpe.Dados.Fatorial[3:7]) Celpe.cfa.sem <- sem(ram=Celpe.Mod.RAM, S=Celpe.cov, N=1443,refit=TRUE,debug=TRUE) Here comes the Results: > Celpe.cfa.sem <- sem(ram=Celpe.Mod.RAM, S=Celpe.cov, N=1443,refit=TRUE,debug=TRUE) observed variables: [1] "1:T1" "2:T2" "3:T3" "4:T4" "5:PI" latent variables: [1] "6:Produ????o" &...

-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 from installer then run these commands to resize btrfs partition (changing...

...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

...recisely the thing that would work best. The issue of course is that "cbind(y, n)" is the name of the first variable in saveit, and it is not being properly quoted somewhere down the line. The same issue can occur on the right hand side. "Save the model frame in case you need to refit something next month" is does not appear to be a safe approach to reproducable research. fit2 <- glm(y ~ sex + log(age), poisson, testdata) save2 <- fit2$model update(fit2, . ~ . - sex, data=save2) # fails glm(y ~ log(age), poisson, save2) # fails I can work around this in my a...

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

...(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 model again by excluding those variables out. I would appreciate if anyone could give some hint on this. Thank you. - adschai [[alternative HTML version deleted]]

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.) Ho...

...is fitted to the pooled data such that the two dose response curves are assumed to differ _only_ in log(r). This fit is also unweighted. 3. The residuals from #2 are used to estimate an appropriate sigma^2 and Delta to use in weighting. 4. The functions described in #1 and #2 are refitted, but this time weighted using the information gathered in #3. 5. How many degrees of freedom should be allocated to the weighted residual sums of squares? (There are three such SSE's, one for each individual model, and one for the overall joint model) Much thanks in...

...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]]

...ficant levels? If they are used for prediction, sometimes they will produce strange results because their coefficient estimates have large variances. Do we just simply ignore them assuming they are not different from level 0? Or do we exclude them in factors (by treating them as zero's) and refit the model? any suggestions?

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 e...

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 do a reboot the grub screen comes up and I can now select CentOS and it will boot. Can someone exp...