similar to: Including random effects in logistic regression.

I'd like to estimate an ordinal logistic regression with a random effect for a grouping variable. I do not find a pre-packaged algorithm for this. I've found methods glmmML (package: glmmML) and lmer (package: lme4) both work fine with dichotomous dependent variables. I'd like a model similar to polr (package: MASS) or lrm (package: Design) that allows random effects. I was

R helpers, I am working with the R program performing multivariate regression trees (MRT). I have a matrix with species and environmental variables saved as a CSV file (sprot_matrix.csv), I have 42 species and 8 environmental variables (SECCHI+PH+TA+PTOT+NTOT+CHLA+AREA+ MEANDEP) for 104 samples Title SpA SpB SpC SpD Varible1 Variable2 Variable3 Sample1 Sample 2

Hi there! We have an old legacy norstar phone system m8x24-ds ( dr5 ) and a couple of m0x16. It has 5 external analog lines. It has no auto attendant, and no voicemail. So every incoming call is forwarded to a operator, she pick up the phone, talks to the caller and transfer the call to the right extension. We are in Argentina, so buying a star talk is out of the question, there is no selling of

Hello R users! I've been experimenting with lmer to estimate a mixed model with a dichotomous dependent variable. The goal is to fit a hierarchical model in which we compare the effect of individual and city-level variables. I've run up against a conceptual problem that I expect one of you can clear up for me. The question is about random effects in the context of a model fit with a

Dear R users, I have a large individual-level dataset (~700,000 records) which I am performing a conditional logistic regression on. Key variables include the dichotomous outcome, dichotomous exposure, and the stratum to which each person belongs. Using this individual-level dataset I can successfully use clogit to create the model I want. However reading this large .csv file into R and running

Dear People at R help, I am trying to figure out the syntax for a three-level logistic model with a single random effect (intercept): Data Collected My data consist of three levels: level 1 is four setting for each student (setting nested within student), and each student is registered in one of 14 universities (students nested within university). More detailed: A. 2,479 students who have a

Does anyone know how to get the C-index from a logistic model - not using the dataset that was used to train the model, but instead using a fresh dataset on the same model? I have a dataset of 400 points that I've split into two halves, one for training the logistic model, and the other for evaluating it. The structure is as follows: column headers are "got a loan" (dichotomous),

Hi all Greetings from Patagonia Argentina - this is my first message to this list. I''m having a weird problem: when I reboot some domU, some OTHER domU gets killed. Scenario: > cat /etc/SuSE-release SUSE Linux Enterprise Server 11 (x86_64) VERSION = 11 PATCHLEVEL = 1 > uname -sri Linux 2.6.32.43-0.4-xen x86_64 > rpm -qa | grep xen- kernel-xen-base-2.6.32.43-0.4.1

Hi everyone, I would like to fit a predictive model to my data in order to compare absorbance readings to a toxin standard. This data was obtained by exposing red blood cells to different toxin concentrations, which lead to the lysis of the red blood cells, increasing the absorbance (hemoglobin is freed). The data has a sigmoid shape (see below), so I thought about fitting a logistic model to the

Hi, I have a dichotomous variable (Q1) whose answers are Yes or No. Also I have 2 categorical explanatory variables (V1 and V2) with two levels each. I used logistic regression to determine whether there is an effect of V1, V2 or an interaction between them. I used the R and SAS, just for the conference. It happens that there is disagreement about the effect of the explanatory variables

Hi, Can one tell me which procedure will fit an ordinal logistic regression model for longitudinal data set. To be precise, I have both dichotomous and polytomous items. Also, I would like to specify different covariance structures (unstructured, ar1 etc) for trial runs. Thanks -- View this message in context:

Hi! I'm pretty raw when working with the R models (linear or not). I'm wondering has anybody worked with the NLME library and dichotomous outcomes. I have a binary outcome variable that I woul like to model in a nested (multilevel) model. I started to fit a logistic model to a NLS function, but could not suceed. I know there are better ways to do it in R with either the LRM or GLM wih

Hello all, Could someone tell me what I am doing wrong here? I am trying to fit a binary logistic model using the lrm function in Design. The dataset I am using has a dichotomous response variable, 'covered' (1-yes, 0-no) with explanatory variables, 'nepall', 'title', 'abstract', 'series', and 'author1.' I am running the following script and

Hello R-User! I appologise in advance if this should also go into statistics but I am presently puzzled. I have a data.frame (about 300 rows and about 80 variables) and my variables are dichotomous factors, continuous (numerical) and ordered factors. I would like to calculate the linear correlation between every pair of my variables, because I would like to perform a logistic regression (glm())

Dear All, I am learning the ropes about logistic regression in R. I found some interesting examples http://bit.ly/Vq4GgX http://bit.ly/W9fUTg http://bit.ly/UfK73e but I am a bit lost. I have several questions. 1) For instance, what is the difference between glm.out = glm(response ~ poverty + gender, family=binomial(logit), data=mydata) and glm.out = glm(response ~ poverty * gender,

Is it possible to fit a structural equation model with link functions in R? I am trying to build a logistic-regression-like model in sem, because incorporating the dichotomous variables linearly seems inappropriate. Mplus can do something similar by specifying a 'link' parameter, but I would like to be able to do it in R, ofcourse. I have explored the 'sem' package from John Fox,

R helpers, I would like to know if it is possible that the last version of R is not giving the surrogate splits when you perform a Multivariate regression tree analysis? I installed the programm in different computers and i run the some matrix and it didn't gave me this information. With a previus version R 2.1.1. I do get the information for the surrogates. Please let me know how to get the

Dear community I'm pretty new to R and I'm trying to do a Point-baserial correlation for a nominal dichotomous variable with a interval scaled variable. It works fine, but the output just shows me the correlation and nothing else (p-Value would be important). I tried it with the following codes: - biseral.cor() - cor.biseral() - I also tried a polyserial() I've found on this

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Estimados Disculpen la ignorancia, probablemente ya lo hayan respondido, pero al actualizar mi versiòn del programa y querer abrir un workspace creado con la versiòn anterior 2.11.1, indica el siguiente error. No es posible cargar el entorno de trabajo creado Gracias por su ayuda, Alejandro -- tel +54-+2944-422731 int 228 fax +54-+2944-424991 correo electrónico almarti en