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It seems that you are looking for clmm(...., threshold="symmetric2"). This threshold argument is valid for clmm, but apparently I forgot to document it for clmm, so take a look at help(clm) instead. > library(ordinal) > fmm1 <- clmm(rating ~ temp...
On 20 April 2011 13:41, Karen Lamb <k.lamb at sphsu.mrc.ac.uk> wrote: > Hi all, > > I have a data set containing around 8148 individuals nested within approximately 2548 areas (DZID). I have an ordinal response (Num20) with...
This is to announce the new R-package ?ordinal? that implements cumulative link (mixed) models for ordinal (ordered categorical) data (http://www.cran.r-project.org/package=ordinal/). The main features are: - scale (multiplicative) as well as location (additive) effects...
Hi Maarten, This is fixed in the development version of lmerTest on GitHub - you may install with library("devtools") # install if you don't have it already. install_github("runehaubo/lmerTest?) Furture bug-reports are also welcome here. For your...
On 27 February 2018 at 15:27, Kornbrot, Diana <d.e.kornbrot at herts.ac.uk> wrote: > Thanks > Am also replying to list, so excuse duplication > used install packages followed by library > though that was happening on 1st attempt - but...
I don't think 'summary' is actually exported by lmerTest (version >= 3.0-0): library(lmerTest) fm <- lmer(Informed.liking ~ Gender + Information * Product + (1 | Consumer) + (1 | Consumer:Product), data=ham) lmerTest::summary(fm) # gives: Error: 'summary' is not an exported...
According to standard likelihood theory these are actually not t-values, but z-values, i.e., they asymptotically follow a standard normal distribution under the null hypothesis. This means that you could use pnorm instead of pt to get the...
2008/7/16 Dimitris Rizopoulos <Dimitris.Rizopoulos at med.kuleuven.be>: > well, for computing the p-value you need to use pchisq() and dchisq() (check > ?dchisq for more info). For model fits with a logLik method you can directly > use...
On 15 April 2013 13:18, Thomas <thomasfoxley at aol.com> wrote: > > Dear List, > > I am using both the clm() and clmm() functions from the R package 'ordinal'. > > I am fitting an ordinal dependent variable with 5 categories to 9...
Dear Caroline, Yes, it seems you have complete separation for the 'Timepoint' variable. This means that the likelihood is unbounded for that parameter and the optimizer just terminates when it gets far enough out on an asymptote and improvements are...
2008/7/16 Dimitris Rizopoulos <Dimitris.Rizopoulos at med.kuleuven.be>: > well, for computing the p-value you need to use pchisq() and dchisq() (check > ?dchisq for more info). For model fits with a logLik method you can directly > use...
Hi Jeremy, I think Jessica is right that probably you could make polr converge and produce a Hessian if the data are better scaled, but there might also be other things not allowing you to get the Hessian/vcov. Could...
Nicholas, I agree with David: this doesn't sound so good and it seems that the model might not have converged... However, to provide more qualified help, I will need to see the result of summary(Model.mlol) sessionInfo() If...
On 20 September 2012 12:22, Klemens Weigl <klemens.weigl at gmail.com> wrote: > Dear David and Jarrod, > > so far I tried a lot :-). > > 1st solution: > To run a linear mixed model with 'the 2 groups/treatments' as fixed effect...
Dear Heather, You can make this test using the ordinal package. Here the function clm fits cumulative link models where the ordinal logistic regression model is a special case (using the logit link). Let me illustrate how to test the...
On 21 September 2011 23:56, Jeremy Koster <helixed2 at yahoo.com> wrote: > Thanks to Ben, Thomas, and David for the responses.? As usual, the explanations didn't really sink in until I experimented with different formulations of models. > > Somewhat...
On 1 May 2018 at 15:45, Maarten Jung <Maarten.Jung at mailbox.tu-dresden.de> wrote: > Dear Rune, > > am I right in thinking of Model3b as a zero-correlation-parameter > model (m_zcp) but with the variances of the...
A third, and often preferable, way is to add an observation-level random effect: library(lme4) data1$obs <- factor(seq_len(nrow(data1))) model <- glmer(y ~ x1 + x2 + (1 | obs), family=poisson(link=log), data=data1) See http://glmm.wikidot...
Brilliant, David, thank you so much! Cheers, Rune > 16. mai 2017 kl. 18.44 skrev David L Carlson <dcarlson at tamu.edu>: > > Fixing a typo in the original, adding a simplification, and using dissimilarity instead of similarity: > > set.seed(42...
The most recent discussion of the h-likelihood framework that I have seen was in Statistical Science, 2009, vol. 24, no.3: Inference for Models with Unobservables: Another view by Lee and Nelder. I found the following discussion very interesting...
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