similar to: Fitting binary regression models with asymmetric link

Hi all, Is there any package in R which fits binary regression models with a probit (or logit) link function related to a nonlinear predictor with both fixed and random effects? Something like that: Y ~ Bernoulli(p) probit(p) = h(X*Beta + Z*b), where ``h'' is a nonlinear function, X and Z are known design matrices, Beta are fixed effects and b are random effects. Thanks all in

Hi all, How could I draw a plot with two y axes? For example, if I need too plot two curves in the same graphic, which are measured in different scales. Thanks a lot in advance, Caio [[alternative HTML version deleted]]

Hi all, Does anybody know if it is possible to build a fractional factorial design in R? That is, suppose that we want do design an experiment with 3 factors with 2, 3 and 3 levels, respectivly. However we want to consider, let's say, only 6 from all possible level combinations. Does R design such experiment? Thanks in advance, Caio [[alternative HTML version deleted]]

Hi, I'm trying to make a regression of the form : formula <- y ~ Asym_inf + Asym_sup * ( (1 / (1 + (n1 * (exp( (tmid1-x) / scal1) )^(1/n1) ) ) ) - (1 / (1 + (n2 * (exp( (tmid2-x) / scal2) )^(1/n2) ) ) ) ) which is a sum of the generalized logistic model proposed by richards. with data such as these: x <- c(88,113,128,143,157,172,184,198,210,226,240,249,263,284,302,340) y <-

Dear All, I am new to R and I would like to do the following: I want to fit a logistic model with 3 predictors and then perform a stepwise regression to select the best possible model using either the AIC/BIC criterion. I have used the stepAIC function which works fine but using this method only likely candidates are evaluated (i.e. not all the models are fitted). We should have 2^3=8 possible

Hello, I have this data: Time AMP 0 0.2000000 10 0.1958350 20 0.2914560 40 0.6763628 60 0.8494534 90 0.9874526 120 1.0477692 where AMP is the concentration of this metabolite with time. If you plot the data, you can see that it could be fitted using a logistic regression. For this purpose, I used this code: AMP.nls <- nls(AMP~SSlogis(Time,Asym, xmid, scal), data

Dear all, I am trying to implement goodness-of-fit tests for logistic regression models described in : Hosmer, D.W., Lemeshow, S., 2000. Applied logistic regression. New-York, John Wiley & Sons, Inc., 373 p. (pp. 152-154) Namely I would like to reproduce Osius & Rojek's test (Osius, G., Rojek, D., 1992. Normal goodness-of-fit tests for multinomial models with large degrees of

Dear R-users, I would like to know what are the commands to install (from a local zip file) a package and then to load it. Thaks all, Bests, Caio "Perco a consciencia, mas n併o importa, encontro a maior serenidade na alucina佺ao. 伾 curioso como n併o sei dizer quem sou. Quer dizer, sei-o bem, mas n併o posso dizer. Sobretudo tenho medo de dizer, porque no momento em que tento falar, n併o

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Caio Lucidius > Naberezny Azevedo > Sent: 01 September 2005 12:09 > To: Help mailing list - R > Subject: [R] Multivariate Skew Normal distribution > > > Hi all, > > Could anyone tell me if there is any package (or function)

I am trying to fit a normal linear model with response y and predictor x and two factors sex and group. I would like each combination of sex and group to have individual slopes and then subsequently have parallel slopes. I tried the model y ~ x*sex*group and it seemed to work for the first case.. Is this how it is supposed to be done? For the second the model y ~ sex + group seems to work. In

Hi all, Does anybody know any function that performs gaussian adapative quadrature integration of univariate functions? Thanks in advance, Regards, Caio __________________________________________________ [[alternative HTML version deleted]]

Hi all, Could anyone tell me if there is some package that fits any Item Response Model (further the ltm package)? Regards, Caio --------------------------------- [[alternative HTML version deleted]]

Hi It would be a great help if someone can direct me to access some existing R codes for fitting Logistic regression models for Multivariate data. I want to find some Robust Estimates (M) for these models. Thanks a lot Best Regards Nimal [[alternative HTML version deleted]]

Hi all, is there any package which can do an EM algorithm fitting of logistic regression coefficients given only the explanatory variables? I tried to realize this using the Design package, but I didn't find a way. Thanks a lot & Kind regards Robin Aly

Sir, I want to fit a multinomial logistic regression in R.I think mlogit() is the function for doing this. mlogit () is in packege globaltest.But, I can not install this package. I use the following: install.packages("globaltest") Can you help me? Regards, Suman Dhara [[alternative HTML version deleted]]

Hello, I am trying to simulate binary outcome data for a logistic regression Monte Carlo study. I need to eventually be able to manipulate the structure of the error term to give groups of observations a random effect. Right now I am just doing a very basic set up to make sure I can recover the parameters properly. I am running into trouble with the code below. It works if you take out the object

Hi all, Where f(x) is a logistic function, I have data that follow: g(x) = f(x)*.5 + .5 How would you suggest I modify the standard glm(..., family='binomial') function to fit this? Here's an example of a clearly ill-advised attempt to simply use the standard glm(..., family='binomial') approach: ######## # First generate some data ######## #define the scale and location of

Hi all, I am using the function "plotCI" with the following command: plotCI(m.residuos.p.2 [1:41],li=m.residuos.p.3 [1:41],ui=m.residuos.p.4 [1:41],lty=1,ylab="") This generates exactly what I want except for the fact that I wanna drawn a line linking the points (m.residuos). How could I do that? Thanks a lot in advance, Caio [[alternative HTML version deleted]]

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

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and