search for: laplace

Dear users, is anybody of you aware of a R command to perform laplace transform or even its inversion? Thank you very much. Luca

...: >>> >>> On Tue, Mar 24, 2015 at 1:48 PM, Bob Wilson <bob.wilson at apple.com> wrote: >>>> >>>>> On Mar 24, 2015, at 10:27 AM, Xinliang David Li <davidxl at google.com> wrote: >>>>> >>>>> 2.3) remove the 'laplace rule of succession' which can be very harmful >>>> >>>> I have not seen any consensus on this. I’m not at all convinced this would be a good idea. From what I’ve seen, using LaPlace’s rule avoids really bad behavior in cases where the counts are very small and has almos...

...Tue, Mar 24, 2015 at 1:48 PM, Bob Wilson <bob.wilson at apple.com> wrote: >>>>>> >>>>>>> On Mar 24, 2015, at 10:27 AM, Xinliang David Li <davidxl at google.com> wrote: >>>>>>> >>>>>>> 2.3) remove the 'laplace rule of succession' which can be very harmful >>>>>> >>>>>> I have not seen any consensus on this. I’m not at all convinced this would be a good idea. From what I’ve seen, using LaPlace’s rule avoids really bad behavior in cases where the counts are very sma...

Hello! My question is stated in the Subject: Is it possible to model with Laplace's error distribution? For example, lmer() function have few families of functions, like binomial etc., but not Laplace. Is there any other package that would allow for Laplace? Or is there a way to give "user-defined" family? Sincerely, P. Milin

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects estimates, standard error and p.values). However, Loglikelihood and deviance are different. here is an example reproduced with the bacteria data set available in the MASS package: library(lme4) libra...

...to get this right, at >> least for real, non-benchmark code. > > We do have a lot of users (real world apps) who can get this right -- > I am not joking ;) > >> >> >> Without the rule, the two workload at least produces consistent >> profile data. With the Laplace rule, you get 50 in one case, and 66 >> in the other. >> >> >> Yes, but you’ve got more information in one case than the other. This is a >> feature IMO, not a bug. It’s entirely possible that with workload 2, the >> loop may have executed for a drastically diffe...

...e. >>> >>> We do have a lot of users (real world apps) who can get this right -- >>> I am not joking ;) >>> >>>> >>>> >>>> Without the rule, the two workload at least produces consistent >>>> profile data. With the Laplace rule, you get 50 in one case, and 66 >>>> in the other. >>>> >>>> >>>> Yes, but you’ve got more information in one case than the other. This is a >>>> feature IMO, not a bug. It’s entirely possible that with workload 2, the >>>&gt...

...dom) + trt*center interaction. Then, I want to compare these 2 models with Likelihood Ratio Test. Here are my lmer codes that I don't feel comfortable about their correctness. model1 <- try(lmer(cbind( yvect, nvect-yvect) ~ 1 + (1 | center), family = binomial, niter = 25, method = "Laplace", control = list(usePQL = FALSE) )) model2 <- try(lmer(cbind( yvect, nvect-yvect) ~ trt*center + ( 1 | center) , family = binomial, niter = 25, method = "Laplace", control = list(usePQL = FALSE) )) (I have attached outputs below) What I don't understand is; I thought...

...t on the model outcome. Does such a measure exist in R? My second question is, if there is a "predict"-function for glmms in R? Because I would like to produce a predictive habitat-map (someone asked that before, but I think there was no answer so far). And the third, why the method "laplace" doesn't work with all my models. thank you very much nina klar R output for a model, which works with laplace: > model4a<-lmer(RESPONSE~ D_TO_FORAL + + I((DIST_WATER-200)*(DIST_WATER<200)) + + I((DIST_VILL-900)*(DIST_VILL<900)) + + (1|TIER), family=binomial, method=&quo...

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with...

...go Novillo <dnovillo at google.com> wrote: > > On Tue, Mar 24, 2015 at 1:48 PM, Bob Wilson <bob.wilson at apple.com> wrote: >> >>> On Mar 24, 2015, at 10:27 AM, Xinliang David Li <davidxl at google.com> wrote: >>> >>> 2.3) remove the 'laplace rule of succession' which can be very harmful >> >> I have not seen any consensus on this. I’m not at all convinced this would be a good idea. From what I’ve seen, using LaPlace’s rule avoids really bad behavior in cases where the counts are very small and has almost no impact when...

...~ visit + gender + house + cokefreq + cracfreq + herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial, correlation = corCompSymm(form = 1 | ID), method = "ML") Although the model fits, the summary indicates the model is a "Generalized linear mixed model fit using Laplace". I've tried any number of permutations; is only Laplace supported in lmer, despite the text of the help file? Thanks for any guidance you might give. M-J -- Research assistant Vancouver Injection Drug User Survey BC Centre for Excellence in HIV/AIDS St. Paul's Hospital, Vancouve...

...acteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of ?Laplace?, ?AGQ?

Does anyone have any R code that shows how to do a Laplace Approximation? I know there are a variety of these numerical approximation algorithms and I'm pretty open at this point, I'm just curious how it's approximated in R code. I have seen some functions in packages, but I think they all call C code, and am looking for an example in R only....

Can anyone show how to calculate a multivariate Laplace density? Thanks. -- View this message in context: http://r.789695.n4.nabble.com/Multivariate-Laplace-density-tp3864072p3864072.html Sent from the R help mailing list archive at Nabble.com.

...umber recruits2,number recruits 1), Pop is populations (five level factor) and ja is year (covariate running from 1955-2004). I.e. biologically I am interested to see how the proportion of recruits from the second clutch has changed over time between the different populations. I've used the Laplace optimization method, due to earlier reports of unstability of PQL when running binomial models. First example: (ja covariate range: 1955-2004) > totmod2 <- lmer(p2rec~Pop*ja + (1|VROUW)+(1|ja), data=dltab2, family=binomial, method="Laplace", na.action=na.omit) > summary(tot...

Is there some package out there that implements functions for reliability analysis, especially for software reliability? In particular, I'm looking for: * Laplace factor (Cox & Lewis 1978) * Goel-Okumoto fitting Thanks in advance, -Ekr -- [Eric Rescorla ekr at rtfm.com]

Hello, <http://tolstoy.newcastle.edu.au/R/help/04/07/0312.html#0313qlink1> Could anybody tell me how to generate discrete Laplacian distribution? I need to sample uma discretised Laplacian density like this: J( g -> g´) ~ exp (-lambda | g´ - g |) g in {0,…, gmax} Thanks, Nicolette [[alternative HTML version deleted]]

Hi, I am having problems passing arguments to method="gbm" using the train() function. I would like to train gbm using the laplace distribution or the quantile distribution. here is the code I used and the error: gbm.test <- train(x.enet, y.matrix[,7], method="gbm", distribution=list(name="quantile",alpha=0.5), verbose=FALSE, trControl=trainControl(method="cv",number=5), tuneGrid...

...other attached packages: lme4 lattice Matrix "0.995-2" "0.12-11" "0.995-4" > options(show.signif.stars=FALSE) > m1a<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1|date), + family=binomial(link='probit'), method='Laplace') Error in dev.resids(y, mu, weights) : argument wt must be a numeric vector of length 1 or length 219 > # probit doesn't converge > m1b<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1|date), + family=binomial, method='Laplace') Error in dev.resids(y, m...