similar to: How to compare the effect of a variable across regression models?

Hello R users, I have a puzzle with the VGAM package, on my first excursion into generalized additive models, in that this very nice package seems to want to do either more or less than what I want. Precisely, I have a 4-component outcome, 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

Hi all, im triyng to implement a bayesian model with R and c++. I have a strange problem. I can't reproduce the error with a small script and then i post the original one. The problem is after the line for(MCMC_iter2=0;MCMC_iter2<thin;MCMC_iter2++) For the first 34 iterations all work fine, after, all the simulations of mu_acc_P return an "nan". If i delete the line

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

Dear List: My question is more statistical than R oriented (although it originates from my work with nlme). I know statistical questions are occasionally posted, so I hope my question is relevant to the list 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

Hi, I would like to call R's lbfgsb function from my C/C++ code by including R_ext/Applic.h and linking against libR. Currently, I am allocating memory for x (and the other input arrays for lbfgsb) in my C/C++ code via malloc/new. However, this gives a segmentation fault when executing the program. I tried to allocate x via PROTECT(x = NEW_NUMERIC(n)); x_p = NUMERIC_POINTER(x);. This compiles

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]]

Hi all, I'm new with R (and S), and relatively new to statistics (I'm a computer scientist), so I ask sorry in advance if my question is silly. My problem is this: I have a (sample of a) discrete time stochastic process {X_t} and I want to estimate Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} } where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for me to compute

Hello, I met this problem with the function step( ) when I was trying to do forward variable selection. Below is the code I used and the error message. I don't why I am getting this error message. Could someone help me out. I noticed that I got this error message whenever it chose a model with an interaction term X_p :X_q and if I put X_q before X_p in the upper scope formula, like

Hi, I'm trying to use expression() to write a text to a graphic in the margin. Using: mtext(expression(beta),side=1,line=2) writes a perfect beta greek character, but I need to add a subindex "max", 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?

This has ABSOLUTELY nothing to do with R but I was hoping that someone might know because there are obviously a lot of very bright people on this list. Suppose I had a time series of data and at each point in time t, I was calculating x bar + plus minus sigma where x bar was based on a moving window of size n and so was sigma. So, if I was at time t , then x bar t plus minus sigma_t would be

Dear all, I have a dataframe of names (netw), with each cell including last name and initials of an author; some cells have NA. I would like to extract only the last name from each cell; this new dataframe is calle 'res' Here is what I do: res <- data.frame(matrix(NA, nrow=dim(netw)[1], ncol=dim(netw)[2])) for (i in 1:x) { wh <- regexpr('[a-z]{3,}',

Hello! Is it possible to use R time series to identificate a process which is subjected to known input? I.e. I have 2 sequences - one is measurements of black box's state and the second is the "force" by which this black box is driven (which is known too) and I want to fit thist two series with AR-process. The "ar" procedure from stats package expects that the force is

Dear R-brains, I'm rather new to state-space models and would benefit from the extra confidence in using the excellent package sspir. In a 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)?

Hello, I am modeling some survival data wih cph (Design). I have modeled a predictor which showed non linear effect with restricted cubic splines. I would like to retrieve the se(coef) for other, linear, predictors. This is just to make nice LateX tables automatically. I have the coefficients with coef(). How do I do that? Thanks, David Biau. [[alternative HTML version deleted]]

*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} ~

This is not an R question but if anyone can help me, it's much appreciated. Suppose I have a series ( stationary ) y_t and a series x_t ( stationary )and x_t has variance sigma^2_x and epsilon is normal (0, sigma^2_epsilon ) and the two series have the relation y_t = Beta*x_t + epsilon My question is if there are particular values that sigma^2_x and sigma^2_epsilon have to take in

Dear list, I've tried several times to wrap my head around the Design library, without much success. It does some really nice things, but I'm often uncomfortable because I don't understand exactly what it's doing. Anyway, one thing I really like is the latex.ols() function, which converts an R linear model formula to a LaTeX equation. So, I started writing a latex.lm() function

I estimate two competing simple regression models, A and B where the LHS is the same in both cases but the predictor is different ( I handle the intercept issue based on other postings I have seen ). I estimate the two models on a weekly basis over 24 weeks. So, I end up with 24 RSquaredAs and 24 RsquaredBs, so essentally 2 time series of Rsquareds. This doesn't have to be necessarily thought

Hello, I didn't give enough information when I sent an query before, so I'm trying again with a more detailed explanation: In this data set, each patient has a different number of measured variables (they represent tumors, so some people had 2 tumors, some had 5, etc). The problem I have is that often in later cycles for a patient, tumors that were originally measured are now missing (or