similar to: R 3.6.1 is released

Displaying 20 results from an estimated 2000 matches similar to: "R 3.6.1 is released"

2011 Oct 13
1
binomial GLM quasi separation
Hi all, I have run a (glm) analysis where the dependent variable is the gender (family=binomial) and the predictors are percentages. I get a warning saying "fitted probabilities numerically 0 or 1 occurred" that is indicating that quasi-separation or separation is occurring. This makes sense given that one of these predictors have a very influential effect that is depending on a
2009 Nov 20
1
different results across versions for glmer/lmer with the quasi-poisson or quasi-binomial families: the lattest version might not be accurate...
Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,
2005 Jun 02
1
glm with variance = mu+theta*mu^2?
How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare "log-linear model fits to
2000 Jul 24
1
scoping problems (PR#614)
I am resubmitting this to r-bugs, since Thomas Lumley indicates that it might be an error: On Wed, 5 Jul 2000, Thomas Lumley wrote: > On Wed, 5 Jul 2000, halvorsen wrote: > > > Hola! > > > > I have the following simple function: > > > > > testcar > > function(pow){ > > ob <- glm(Pound~CG+Age+Vage,data=car,weights=No, > >
2004 Aug 06
0
Unofficial Icecast HOWTO posted
okee, I put the oddcast help online at: http://www.radioqualia.net/oddcast i have to appologise twice for this, because the formatting is kinda crappy and i just converted it from a word doc with a straight doc->html export :/ will fix in the coming weeks but its online for now...also if anyone can give some more informatiuon about the oddcast encoder settings then please let me know so i
2001 Dec 19
1
Pearson residuals in quasi family
Hi all, This is a very silly question or something escapes me: Let obj a simple gam poisson model. Let >obj<-gam(....,family=poisson) >obj1<-update(obj, family=quasi(link="log", var="mu")) >From summary.glm(obj1) the dispersion parameter is estimated 1.165; In fact it is: > (predict(obj1, se.fit=T)$se.fit[1:5]/predict(obj, se.fit=T)$se.fit[1:5])^2 4
2004 Aug 06
2
Unofficial Icecast HOWTO posted
Absolutely!! That was one thing that I was missing and would have like to have included. You can either send me the data or let us know the link. If you wouldn't mind adding to this HOWTO and I will post a link to your page so that people would know where I got the original files. Sounds good. KJ <p>On Wed, 2003-02-12 at 10:37, adam wrote: > looks great! :-) > > if you like i
2018 Jun 17
1
aic() component in GLM-family objects
FWIW p. 206 of the White Book gives the following for names(binomial()): family, names, link, inverse, deriv, initialize, variance, deviance, weight. So $aic wasn't there In The Beginning. I haven't done any more archaeology to try to figure out when/by whom it was first introduced ... Section 6.3.3, on extending families, doesn't give any other relevant info. A patch for
2004 Aug 06
0
Unofficial Icecast HOWTO posted
looks great! :-) if you like i can post a how-to for setting up and streaming with oddcast and the win32 icecast 2 from oddsock (I have both with step by step screen shots etc). adam <p>On 12 Feb 2003, Kerry Cox wrote: > Howdy. I just posted an unofficial HOWTO on configuring icecast2 on a > Linux or other UNIX machine. It's just a draft in DocBook format, but I > was hoping
2006 Jul 08
0
which model (GLMs)is the best?
Dear friends, I used R to analyze my data with the models of generalized linear models, and found three models were relatively good, but i can't decide which is the best,how should i do ? *Model1:* glm(formula = snail ~ grass + gheight + humidity + altitude + soiltem + airtem + grass:altitude, *family = Gamma(link = inverse*), data = model, na.action = na.exclude, control =
2006 Nov 03
1
Translation of R code required
Dear All I am a complete novice when it comes to R. I have been sent some code which I need to run on a dataset. I have downloaded and installed R but I dont seem to be able to get the code to run. I was hoping someone on the list would be able to give me a 'translation' of what the code is trying to do and if there appears to be any errors in it. The code is; mental <-
2019 Apr 26
1
Error in glm(..., family=quasi(..., variance=list(...)))
In a glm() call using a quasi() family, one may define a custom variance function in the form of a "list containing components varfun, validmu, dev.resids, initialize and name" (quoting the help page for family). In trying to do so, I run into the following issue that I have not seen discussed previously: x <- runif(1000, min=0, max=1) y <- x + rnorm(1000, mean=0, sd=1)*x^(3/4)
2005 Nov 28
3
glm: quasi models with logit link function and binary data
# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is
2013 Apr 17
1
mgcv: how select significant predictor vars when using gam(...select=TRUE) using automatic optimization
I have 11 possible predictor variables and use them to model quite a few target variables. In search for a consistent manner and possibly non-manual manner to identify the significant predictor vars out of the eleven I thought the option "select=T" might do. Example: (here only 4 pedictors) first is vanilla with "select=F" >
2006 Jan 14
2
initialize expression in 'quasi' (PR#8486)
This is not so much a bug as an infelicity in the code that can easily be fixed. The initialize expression in the quasi family function is, (uniformly for all links and all variance functions): initialize <- expression({ n <- rep.int(1, nobs) mustart <- y + 0.1 * (y == 0) }) This is inappropriate (and often fails) for variance function "mu(1-mu)".
2002 Jul 01
1
Defining own variance function / quasi-likelihood in a GLM
Hello, I've been looking in the on-line manuals and searching past posts but can't find an answer to this question. I'd like to define my own variance function in a GLM. The function glm(formula, family=quasi(var="var function")) lets me choose from a selection of built in variances, but I want to define my own function for the variance. Is there an S-plus
2006 Jun 12
1
variance specification using glm and quasi
Hi all, Cameron and Trivedi in their 1998 Regression Analysis of Count Data refer to NB1 and NB2 NB1 is the negative binomial model with variance = mu + (alpha * mu^1) yielding (1+alpha)*mu NB2 sets the power to 2; hence, variance = mu + (alpha*mu^2) I think that NB2 can be requested via negbin2<-glm(hhm~sex+age,family=quasi(var="mu^2",link="log")) Is
2012 Oct 18
2
Assessing overdispersion and using quasi model with lmer, possible?
Hello! I am trying to model data on species abundance (count data) with a poisson error distribution. I have a fixed and a random variables and thus needs a mixed model. I strongly doubt that my model is overdispersed but I don't know how to get the overdispersion parameter in a mixed model. Maybe someone can help me on this point. Secondly, it seems that quasi models cannot be implemented
2004 Aug 06
6
Unofficial Icecast HOWTO posted
Howdy. I just posted an unofficial HOWTO on configuring icecast2 on a Linux or other UNIX machine. It's just a draft in DocBook format, but I was hoping it might help someone. I've worked enough with icecast over the past few years that I thought it time to share what I've learned. The URL is as follows: http://quasi.ksl.com/icecast/ I have older documentation in
2010 Feb 05
0
Quasi-binomial GLM and model selection
Hi, I'm using a GLM with a quasi binomial error distribution and I would like to do a model selection method similar to step(AIC) to carry out a restricted search for the "best" model. I would like to know which of my 5 predictor variables would be included in the "best" model if I start with a 'full' model (fullbinom in this case). However, AIC can't be