similar to: Loop within nlme

Hi, My name is Jos?? Mar??a G??mez, and I am pretty new in R. Thus, I apologize deeply if my questions are extremmely na??ve.I have checked several available books and URL's, without finding any answer. I'm trying to fit Generalized Linear Mixed Models via PQL. Below I provide the structure of my data set. Year and Plot are random variables. Fate is the binomial dependent. I have severe

Thanks to Gabor, Duncan, and Peter. I knew the answer had something to do with solving for a and b in terms of mean and variance. I will build a function using the equations you provided Duncan and will look into using Mathomatic in the future Gabor. Appreciate the help. Peter, this was not homework but I understand your concern. I don't use listserves that often but they do open a whole

I am trying to write a function that will solve a simple system of nonlinear equations for the parameters that describe the beta distribution (a,b) given the mean and variance. mean = a/(a+b) variance = (a*b)/(((a+b)^2) * (a+b+1)) Any help as to where to start would be welcome. -- Scott Story Graduate Student MSU Ecology Department 319 Lewis Hall Bozeman, Mt 59717 406.994.2670 sstory at

Hi, Is there any package that I can use to simulate the Gaussian Mixture Model , which is a mixture modeling method that is widely used in statistical learning theory. I know there is a mclust, however, I think it is a little bit different from my problem. Thanks very much.. regards. -------------------------- Peng Jiang ?? Ph.D. Candidate Antai College of Economics &

Dear All, reading an article by Rodolphe Thiebaut and Helene Jacqmin-Gadda ("Mixed models for longitudinal left-censored repeated measures") I have found this program in SAS proc nlmixed data=TEST QTOL=1E-6; parms sigsq1=0.44 ro=0.09 sigsq2=0.07 sigsqe=0.18 alpha=3.08 beta=0.43; bounds $B!](B1< ro < 1, sigsq1 sigsq2 sigsqe >= 0; pi=2*arsin(1); mu=alpha+beta*TIME+a i+b i*TIME;

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

Hi, I was very much hoping someone could help me with the following. I am trying to convert some SAS NLMIXED code to NLME in R (v.2.1), but I get an error message. Does anyone have any suggestions? I think my error is with the random effect "u" which seems to be parametrized differently in the SAS code. In case it's helpful, what I am essentially trying to do is estimate parameters

Hi, I have a dataset which has a categorical variable "R",a count variable C (integer) and 4 or more numeric variables (A,T,W,H - integers) containing measures for "R". I would like to summarize each level of the variable R by the average for A,T,W and H. I have written a function to calculate weighted averages using C as the weight and this is given below. The function

Below you will find the output from a failed multi-level model run. I am trying to estimate the following model: Pr(PLFP=1)= logistic regression -> B1_j * bm + B2_j * wm + B3_j bf + B4_j wf + B5 yrsed+ B6 age+ B7 age^2+e_ij B1_j = G01 + G11 bmxd + d1 B2_j = G02 + G12 wmxd + d2 B3_j = G03 + G13 bfxd + d3 B4_j = G04 + G14 wfxd + d4 d1-d4 freely correlated Note that there is no

Hello, I'm trying to fit a special kind of proportional odds model from: Whitehead et al. (2001). Meta-analysis of ordinal outcome using individual patient data. Statistics in medicine 20: 2243-2260. (model 2) The data are as follows: library(nlme) library(geepack) library(Design) library(MASS) options(contrasts=c("contr.SAS","contr.poly")) counts <-

Dear R friends A while a go I sent an email to the epi-list and later to the help-list and no answer could fully illuminate my question. So Im trying again with a more specific matter. Im trying to work on a script (function) to analyse data from a diagnostic test meta-analysis with random effects. This was first described by an author using SAS witn PROC NLMIXED. Im not an expert in R and much

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

Hi Folks, I'm seeking confirmation of something which is probably true but which I have not managed to find in the documentation. I have a binary response y={0.1} and a variable x and have fitted a probit response to the data with f <- glm( y~x, family=binomial(link=probit) ) and then, with a specified set of x-value X I have used the predict.glm function as p <- predict( f, X,

Dear list, I have a problem on calculating the standard error of Goodman-Kurskal's gamma using delta method. I exactly follow the method and forumla described in Problem 3.27 of Alan Agresti's Categorical Data Analysis (2nd edition). The data I used is also from the job satisfaction vs. income example from that book. job <- matrix(c(1, 3, 10, 6, 2, 3, 10, 7, 1, 6, 14, 12, 0, 1, 9,

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

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

I am trying to fit a non linear mixed effect model but I also want to do a post hoc comparison. My data is binary and consist of recording mice track prints on plates plates in plots that submited to one of 4 different treatments (fruits and vegetation complexity manipulated for two levels each. The design is random blocks repeated measures with presence or absence of track prints as a response

I'm estimating two models for data with n = 179 with four clusters (21, 70, 36, and 52) named siteid. I'm estimating a logistic regression model with random intercept and another version with random intercept and random slope for one of the independent variables. fit.1 <- lmer(glaucoma~(1|siteid)+x1 +x2,family=binomial,data=set1,method="ML",

I have seen, several times, dots (like this: "y ~." ) in formula descriptions, noticeably in R help. I am unable to see what it does correspond to. Any ideas ? -- --~~ Toulouse, Grenoble, Auch, Arcachon, B??ziers, Paris, Saragosse, L??vignac Sur Save, habitats naturel du Valdo. ~~-- < http://www.le-valdo.com>