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...ompare the effect of a variable across regression models. I have looked around but I haven't found anything. Maybe someone could help? Here is the problem: I am studying the effect of a variable (age) on an outcome (local recurrence: lr). I have built 3 models: - model 1: lr ~ age y = \beta_(a1).age - model 2: lr ~ age + presentation variables (X_p) y = \beta_(a2).age + \BETA_(p2).X_p - model 3: lr ~ age + presentation variables + treatment variables( X_t) y = \beta_(a3).age + \BETA_(p3).X_(p) + \BETA_(t3).X_t Presentation variables include variables such as tumor g...

...ome, y, and am fitting multinomial logistic regression with one predictor x. What I would like to find out is, is there a single nonlinear function f(x) which acts in place of the linear predictor x. There is a mechanistic reason to believe this is sensible. So I'd like to fit a model \eta_j = \beta_{ (j) 0 } + \beta_{ (j) x } f(x) where both the function f(x) and its scaling coefficients \beta_{ (j) x } are fit simultaneously. Here \eta_j is the linear predictor, the logodds of outcome j vs the reference outcome. I cannot see how to fit exactly this. Instead I seem to be able to do the follow...

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the logit of Y_3. Is there an existing facility or package in R for doing this? Would multinomRob fit the bill? Many thanks, Roger -- Roger Levy Email: rlevy at...

...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_{1i} * *The vector* *(u_{0i}, u_{1i})'* *has normal distribution with mean* *(0, 0)'*...

...arameter 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_{1i} * *The vector* *(u_{0i}, u_{1i})'* *has normal distribution with mean* *(0, 0)'...

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]]

...as I cannot turn up a solution anywhere else. I will frame it in the context of an R related issue. To illustrate the problem, consider student achievement test score data with multiple observations available for each student. One way of modeling these data might be Y_{ti} = (\mu + \mu_{i} ) + (\beta_0 + \beta_{i} )*(time) + \epsilon_{ti} ; t indexes time and i indexes student The nlme code is tt<-lme(reponse~time, data, random=~time|ID) With this, I can extract the growth rate for each individual in the data set. Conceptually this is the sum of the main effect for time plus the empirical...

...x", and I'm trying: mtext(paste(expression(beta),"max"),side=1,line=2) simply writes "beta max" in the plot. Please, Could you tell me what I'm doing wrong? By the way, is there a way to add Latex expressions to graphics? Then I could use the Latex expression: $\beta_{\mathrm{max}}$. This also would be very useful for me for more complex expressions in plots. Best regards, Javier -- Javier Garc?a-Pintado Institute of Earth Sciences Jaume Almera (CSIC) Lluis Sole Sabaris s/n, 08028 Barcelona Phone: +34 934095410 Fax: +34 934110012 e-mail:jgarcia at ija.csic...

...one-factor model, If I am trying to do a simple regression where I assume the intercept is constant and the 'Beta' is changing, how do I do that? How do i Initialize the filter (i.e. what is appropriate to set m0, and C0 for the example below)? The model I want is: y = alpha + beta + err1; beta_(t+1) = beta_t + err2 I thought of the following: library(mvtnorm) # (1) library(sspir) # Let's get some data so we can all try this at home dfrm <- data.frame( y = c(0.02,0.04,-0.03,0.02,0,0.01,0.04,0.03,-0.01,0.04,-0.01,0.05,0.04, 0.03,0.01,-0....

..., "", co) if(abbreviate == TRUE) { co.n <- abbreviate(gsub("p.*)", "", co), minlength=abbrev.length) } # Get and format DV m.y <- strsplit((as.character(object$call[2])), " ~ ")[[1]][1] # Write coefficent labels b.x <- paste("\\beta_{", co.n ,"}", sep="") # Write error term e <- "\\epsilon_i" # Format coefficint x variable terms m.x <- sub("}Int","}", paste(b.x, co.n, " + ", sep="", collapse="")) # If inline estimates convert coeffici...

...sion coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu + \eta_{i} + \epsilon_{i} \eta_{i} ~ iid N(0,\tau^2) and independent of the \epsilon_{i}, the latter themselves being independent with variances assumed known and equal to the squared standard errors reported in the regression output. I would like to use lme() to estimate \tau^2 by REML,...

...s someone knowledgable on this list could offer some help. I have estimated a binary logistic regression model and would like to calculate average marginal effects for certain predictors of interest. The average marginal effect for a continuous variable cont has been given as AME_cont = 1/n * SUM(beta_{cont} * prob(Y)* 1-prob(Y)) This seems easy enough to calculate with R. However, the predictors I'm interested in are dummy variables, not continuous, and the following formula has been suggested for estimating the average marginal effect for dummies: AME_dummy = 1/n*SUM(prob(Y| dummy=1) - p...

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in

...t.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: Alisa Wade <alisaww a...