similar to: How to do log normal regression?

I am currently trying to replicate the results I got from RATS for a panel regression. The codes in RATS looks like this: * Final equation for Office Cap Rate Spread * Regression, Panel Data preg(effects=time, method=random) CapRate # CapRate{1} RentCycle{1} VacancyChangeYTY InflationYTY RealGDPyty Just wonder what R package also allow me to have the options like (effects=time, method=random).

Hi, I am new to use R, but can anyone tell me how to tranform quarterly data to monthly data? I know SAS has this procedure, so may I assume it is also available in R? Thanks a lot! Ed

I got the following warning messages when I did a binomial logit regression using glm(): Warning messages: 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, Can some one share your thoughts on how to

Hi, Thanks for reading. I am running a process in R for microarray data analysis. RedHat Enterprise Linux 4, dual AMD CPU, 6G memory. However, the R process use only a total of <200M memory. And the CPU usage is total to ~110% for two. The program takes at least 2 weeks to run at the current speed. Is there some way we can increase the usage of CPUs and memories and speed up? Any

Hi, all, Does anybody got the most general expression of a unitary matrix? I found one in the book, four entries of the matrix are: (cos\theta) exp(j\alpha); -(sin\theta)exp(j(\alpha-\Omega)); (sin\theta)exp(j(\beta+\Omega)); (cos\theta) exp(j\beta); where "j" is for complex. However, since for any two unitary matrices, their product should also be a unitary matrix. When I

Dear all, I have a population with three age-classes, at time t=0 the population is: n.zero <- c(1,0,0) I have a transition matrix A which denotes "fertility" and "survival": A <- matrix(c(0,1,5, 0.3,0,0, 0,0.5,0), ncol=3, byrow=TRUE) To obtain the population at t=1, I calculate: A %*% n.zero To obtain the population t=2, I calculate: A %*% (A %*% n.zero) ... and so

I'd like to be able to test linear hypotheses after setting up and running a model using optim or perhaps nlm. One hypothesis I need to test are that the average of several coefficients is less than zero, so I don't believe I can use the likelihood ratio test. I can't seem to find a provision anywhere for testing linear combinations of coefficients after max. likelihood. Cheers

Hello, I want to solve some two dimensional equation system with R. Some systems are not solvable analytically. Here is an example: (I) 1/n*sum{from_i=1_to_n}(Xi) = ln lambda + digamma(c) (II) mean(X) = x / lambda I want to find lambda and c, which R-function could do that task? Carsten [[alternative HTML version deleted]]

Hi, I am a user of R computer language.Several days ago,I download R-2.1.0.From the help manual,I saw there is a function "mle" to find the maximum likelihood estimators.But when I try to use this function,there is no this function.Could you help me to solve this problem? I am looking forward to your answer. Thank you. Jun Liu

Hello all, I would like to plot the emperical CDF, normal CDF and pareto CDF in the same graph and I amusing the following codes. "z" is a vector and I just need the part when z between 1.6 and 3. plot(ecdf(z), do.points=FALSE, verticals=TRUE, xlim=c(1.6,3),ylim=c(1-sum(z>1.6)/length(z), 1)) x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4373,-0.2398), col="red") y

Do we have any R package that can do analysis on finite mixture model with logistic regression? Thanks Faith Feng Gao Dept. of Statistics Virginia Tech. Email: fgao at vt.edu

Hey, I am estiamting a partially linear model y=X\beta+f(\theta) where the f(\theta) is estiamted using wavelets. Has anyone heard of methods to test if the betas are significant or to address model fit? Thanks for any thoughts or comments. Elizabeth Lawson __________________________________________________ [[alternative HTML version deleted]]

Hi, The question is as follows: has anyone coded panel data unit root tests with R? Even the "first generation" tests (see e.g. Levin & Lin 1993; Pesaran, & Smith & Im 1996; Maddala & Wu 1999) would be sufficient for my needs. To my understanding, these are rather easy to code, but as I have taken just my first steps in coding with R, existing code would save me

Dear R-users, Is anyone aware of any function/package for generating a random vector from a joint distribution defined by an independent graph? Or I have to work it out myself? Thanks. Jinfang ------------------------------ Jinfang Wang, Associate Professor Chiba University, Japan

I often use simple nested random-effect models for interlaboratory data. The variance components are important things to know. Is there an R function or package that gets variance components from lm objects? Or can someone point me to a method of doing so?

Hello and sorry to disturb. I'm trying to plot the confidence intervals for the fixed effects of a lme. I want to obtain graphically, if it is possible, a bar with Estimate, upper and lower CI for each level of the factors. I know how to do for a lm model but for a lme one, I tried with plot(intervals(...)) and plot(ci(...)) from the gmodels package but it doesn't work well. Thanks

Alternatively, just type debug(optim) before using it, then step through it by hitting enter repeatedly... When you're done, do undebug(optim). Andy > From: Spencer Graves > > Have you looked at the code for "optim"? If you > execute "optim", it > will list the code. You can copy that into a script file and walk > through it line by line to

Hi: I am trying to do a spurious regression in R but I can not find the function. Anybody used it before? The problem I have is try to do a regression with several time series. An alternative is to use the GLS function to fit the linear regression with the correlation structure AR(3) for the response (or residual). I hope the residuals after the GLS regression will be independent judged by

Dear R users; Ive got two questions concerning nlme library 3.1-65 (running on R 2.2.0 / Win XP Pro). The first one is related to augPred function. Ive been working with a nonlinear mixed model with no problems so far. However, when the parameters of the model are specified in terms of some other covariates, say treatment (i.e. phi1~trt1+trt2, etc) the augPred function give me the following

Hi all, I encountered this problem with parameterization in R: I have two factors in a regression. how about if I want to set constraint so that for each factor, the sum of their coefficients equals to zero(instead of choosing a reference category)? for example, I have factor(variable) A(with three categories) and factor(variable) B(with 4 categories), and I want to parameterize so that the sum