similar to: generalized additive mixed models for ordinal data

Dear list members, I am estimating an ordinal generalized estimating equations (GEE) regression model on repeated measurements data. Is there any way (preferably in R) to test the proportional odds assumption of this model? Thanks in advance. Andreas [[alternative HTML version deleted]]

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

Dear list, I'd like to create a visual plot of a clmm() I've fitted using the 'ordinal' package in R. It's possible to do this with a glm() by using the 'effects' package. For example: library(effects) data(BEPS) mod <- lm(political.knowledge ~ age + gender + vote, data=BEPS) eff <- effect("age", mod, default.levels=100) plot(eff,

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Dear R-users, I couldn't find a function or some help in R-project web about the Generalized Singular Value Decomposition. In MatLab there is a simple function for this algebric issue (gsvd). Is there anything like that in R? And, if not, could you help me to apply this method in R? Thanks in advance, Giancarlo +++++ This mail has been sent through the MPI for Demographic Rese...{{dropped}}

Hello, I'm running mixed models in GAMM4 with 2 (non-nested) random intercepts and I want to include a spline term for one of my exposure variables. However, when I include a spline term, I always get reported degrees of freedom of less than 1, even when I know that my spline is using more than 1 degree of freedom. For example, here is the code for my model: >

Hello I'm analyzing a dichotomous dependent variable (dv) with more than 100 measurements (within-subjects variable: hours24) per subject and more than 100 subjects. The high number of measurements allows me to model more complex temporal trends. I would like to compare different models using GLM, GLMM, GAM and GAMM, basically do demonstrate the added value of GAMs/GAMMs relative to

My problem is relatively straight forward, but I cannot seem to find a way to make it work. I have a RCBD with repeated measurements over time. I have created a fit using the gamm4 package. My model is: fit4a <- gamm4(Rate ~ s(Time,by=trt,bs="cr")+trt,data=qual.11.dat, random=~(1|block),correlation=corARH1()) What I would like to create is plots with the X-axis

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Dear R-users, I would like to know whether there is a way to extract a diagonal of an inverse matrix without computing the inverse of the matrix itself. The size of my matrices are really huge and, also using sparse matrix, computing the inverse leads to storage problems and low speed. In other words, given a square matrix A, I aim to know diag(B), where B=solve(A), without computing solve(A).

Dear R-users, I'm struggling in R in order to "squeeze" a matrix without using a for-loop. Although my case is a bit more complex, the following example should help you to understand what I would like to do, but without the slow for-loop. Thanks in advance, Carlo Giovanni Camarda A <- matrix(1:54, ncol=6) # my original matrix A.new <- matrix(nrow=3, ncol=6) # a new

Dear all,? I hope that you can help me on this. I have been struggling to figure this out but I haven't found any solution. I am running a generalised mixed effect model, gamm4, for an ecology project. Below is the code for the model: model<-gamm4(LIFE.OE_spring~s(Q95, by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland? ? ? ? ? ? ?+X.urban.suburban+X.CapWks,

Hi, I've been using gamm4 to build GAMMs for exploring environmental influences on genetic ancestry. Things have gone well and I have 2 very straightforward questions: 1. I've used method=REML. Am I correct that this is an alternative method for estimating the smooth functions in GAMMs rather than GCV that is often used for GAMs? I've read up on REML and it makes sense, but I'm

Dear R-users, within the rgl-package, I would have a question about the usage of persp3d in combination of rgl.viewpoint. I am not able to figure out how to let a 3D plot rotating around likewise the example in ?rgl.viewpoint. It seems that when I use persp3d(...) I see something on my screen, which is different from what I get when it's rotating. Is there any different behavior between

Dear R-users, I would like to place two 3D plots side-by-side in a rgl-setting. It would nice to have something like "par(mfrow=c(1,2))" for basic plots, or an array framework for wireframe(lattice) (see example below). I only managed to overlap two persp3d plots. My final idea would be to animate both surfaces using play3d(rgl). Thanks in advance for any help. Best, Carlo Giovanni

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 continuous predictors, all of which have been normalised (mean subtracted then divided by standard deviation), using a probit link function. From this global model I am generating a confidence set of 200 models using clm() and the

Dear R-help users, I am trying to analyze some visual transect data of organisms to generate a habitat distribution model. Once organisms are sighted, they are followed as point data is collected at a given time interval. Because of the autocorrelation among these "follows," I wish to utilize a GAM-GEE approach similar to that of Pirotta et al. 2011, using packages 'yags' and

Dear R-users, I am going to try to be as clearer as possible, showing also an example. 1) I have a function (in my real case it's much more complex) 2) I use "optim" to minimize 3) I want to use as method L-BFGS-B for several reasons 4) I know I could use "trace=6" (in "control") in order to see "live" the procedure 5) I would like to see separately the

Hi Maria If you have problems just start with a small model with predictions and then plot with xyplot the same applies to xyplot Try library(gamm4) spring <- dget(file = "G:/1/example.txt") str(spring) 'data.frame': 11744 obs. of 11 variables: $ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ... $ SITE_ID

Dear R-users, I am working with some grid-search optimization for 13 values of an object function. At glance one may compute the object function for each possible combination of set of parameters, but in my case would not be feasible: taking for example 13 possible values for each parameters like, in logarithm scale, 10^seq(-3,3,.5) will lead to 13^13 combinations of results. As a second trial