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Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]

I am having a problem with optim() using the "L-BFGS-B" method. When I set the lower limit for the third parameter equal to zero I get an error message: > low.lim.3 <- 0 > phi_opt <- optim(phi_, model_lik, NULL, method = "L-BFGS-B", lower=c(0.2, -100, low.lim.3, 0), upper= c(10, 100, 10, 10), control = list(maxit = 1000, parscale = c(0.2, u1, 0.002, 0.002), trace = 0, REPORT = 3), hessian = FALSE) Error in chol(M) : the leading minor of order 1 is not positiv...

*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} log(phi_{ij}) = Gamma_{0i} + Gamma_{1i} * z1_{ij} + c2 * z2_{ij} * *Beta_{0i} = b_0 + u_{0i} Beta_{1i} = b_1 + u_{1i} Gamma_{0i} = c_0 + v_{0i} Gamma_{1i} = c_1 + v_{...

...sion 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} ~ 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} + b_2 * x2_{ij} log(phi_{ij}) = Gamma_{0i} + Gamma_{1i} * z1_{ij} + c_2 * z2_{ij} * *Beta_{0i} = b_0 + u_{0i} Beta_{1i} = b_1 + u_{1i} Gamma_{0i} = c_0 + v_{0i} Gamma_{1i} = c_1 + v...

Full_Name: Ian McLeod Version: 2.3.1 OS: Windows Submission from: (NULL) (129.100.76.136) > There is a simple bug in acf as shown below: > > z <- 1 > acf(z,lag.max=1,plot=FALSE) > Error in acf(z, lag.max = 1, plot = FALSE) : > 'lag.max' must be at least 1 > This is certainly a bug. There are two problems: (i) the error message is wrong since lag.max is

...come July we will have fixed that.) Since this is one of the most frequent questions people ask me in direct email, too, let me try (again) to sort it out in some detail. A formula such as y ~ f, where f is a factor in principle generates a single classification model in the form *y_{ij} == mu + phi_i + e_{ij} Write the design matrix in the form X = [1 Xf], where, assuming f has p levels, Xf is the n x p dummy variable (ie binary) matrix corresponding to the phi_i's. So in matrix terms the model is written as *y = 1 mu + Xf phi + e (a) If you remove the intercept term, using y ~ f -1, th...

...; To: r-help at r-project.org Subject: [R] Poisson Regression Message-ID: <AANLkTikXc5tvziGaxuV1GqM3CgNyPPpay-FQCC6uzQWE at mail.gmail.com> Content-Type: text/plain Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]] ------------------------------ Message: 109 Date: Wed, 13 Oct 2010 14:54:39 -0600 From:...