similar to: nlysystemfit and loglikelihood values

Hi everyone, I am new to R and keep getting the message Error in as.vector(x, mode) while trying to run nlsystemfit. Below is my exact code. The data is in Stata format because I only recently swapped to R and am trying to compare results from Stata to make sure I know what is going on. I have searched google and read sever R-help articles with this error. They all say the problem is to do

Dear R users, I am estimating Poisson mixed models using glmmPQL (MASS) and lmer (lme4). We know that glmmPQL do not provide the correct loglikelihood for such models (it gives the loglike of a 'pseudo' or working linear mixed model). I would like to know how the loglike is calculated by lmer. A minor question is: why do glmmPQL and lmer give different degrees-of-freedom for the same

Hello everyone, I have estimated system of three linear equations with one non-linear restrictions with nlsystemfit. I was wondering how I can calculate the R-squared (or some alternative coefficient of determination) for the whole system. This is automatically given by linear systemfit but not by nlsystemfit. I can get the values for each of the equations separately, but apparently not for

Hi everybody, I'm a newbye with lots of problems :). I'm trying to use nlsystemfit, but I recieve two error messages whose origin that I don't understand. 1) When I try to reproduce the example reported in the systemfit package manual, that is library( systemfit ) data( ppine ) hg.formula <- hg ~ exp( h0 + h1*log(tht) + h2*tht^2 + h3*elev + h4*cr) dg.formula <- dg ~ exp( d0

It has always been my understanding that deviance for GLMs is defined by; D = -2(loglikelihood(model) - loglikelihood(saturated model)) and this can be calculated by (or at least usually is); D = -2(loglikelihood(model)) As is done so in the code for 'polr' by Brian Ripley (in the package 'MASS') where the -loglikehood is minimised using optim; res <-

In species distribution modeling where one uses a large sample of background points to capture background variation in presence\pseudo-absence or use\available models (0\1 response) it is frequently recommended that one weight the data so the sum of the absence weights is equal to the sum of presence weights so that the model isn?t swamped by an overwhelming and arbitrary number of background

Hi, I work on order estimation for autoregressive processes and after some inconsistencies cropped up I implemented the AIC criterion myself. Its results do not match the implementation in R and there are a few things I can not understand even after reading the source code of R. I used the function called arima0 (with empty "ma" coeficient vector), and I do not understand how some of

Hi, I would like to estimate a quadratic almost ideal demand system in R which is estimated usually by nonlinear seemingly unrelated regression. But there is no such function in R yet but it is readily available in STATA (nlsur), see B. Poi (2008): Demand-system estimation: Update, Stata Journal 8(4). Now I am thinking, what is quicker learning to "program" STATA which seems not really

Hi all. Belove is the example from the cluster-help page wtih the output. I simply cannot figure out how to relate the estimate and robust Std. Err to the p-value. I am aware this a marginal model applying the sandwich estimator using (here I guess) an emperical (unstructered/exchangeable?) ICC. Shouldent it be, at least to some extend, comparable to the robust z-test, for rx :

Hi,  I tried fitting loglinear model using the glm(catspec). The data used is FHtab. . An independence model was fitted. Here summary() and fitmacro( ) give different values for AIC.   I understand that fitmacro( ) takes the likelilhood ratio L2(deviance) to calculate AIC and uses the formula AIC= L2- d.f(deviance)*2 and this AIC is used for comparison of nested models. (Am I right?)   The value

Hi Could anybody give me a bit of advice on some code I'm having trouble with? I've been trying to calculate the loglikelihood of a function iterated over a data of time values and I seem to be experiencing difficulty when I use the function expm(). Here's an example of what I am trying to do y<-c(5,10) #vector of 2 survival times p<-Matrix(c(1,0),1,2) #1x2 matrix

Hi everyone, I am looking to do some manual maximum likelihood estimation in R. I have done a lot of work in Stata and so I have been using output comparisons to get a handle on what is happening. I estimated a simple linear model in R with lm() and also my own maximum likelihood program. I then compared the output with Stata. Two things jumped out at me. Firstly, in Stata my coefficient

Hi everyone! I already posted http://r.789695.n4.nabble.com/Declaring-a-density-function-with-for-loop-td4635699.html a question on finding density values of a new Binomial like distribution which has the following pmf: http://r.789695.n4.nabble.com/file/n4636134/kb.png Thank fully http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=user_nodes&user=124474 Berend Hasselman and

Hello, I want to solve a nonlinear 3SLS problem with "nlsystemfit()". The equations are of the form y_it = f_i(x,t,theta) The functions f_i(.) have to be formulated as R-functions. When invoking "nlsystemfit()" I get the error Error in deriv.formula(eqns[[i]], names(parmnames)) : Function 'f1' is not in the derivatives table

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

Hey, I want to implement a structural model with the package systemfit with some linear and nonlinear constraints. How to implement linear restrictions is clear. Does anybody know how to set up nonlinear restrictions in the systemfit packages. For example: beta1 = beta2-(beta4/beta6) I look forward to your reply -- View this message in context:

Dear R-experts: I am using survreg() to estimate the parameters of a Weibull density having right-censored observations. Some observations are weighted. To do that I regress the weighed observations against a column of ones. When I enter the data as 37 weighted observations, the parameter estimates are exactly the same as when I enter the data as the corresponding 70 unweighted observations.

Dear R experts, Attached you can find the expression of a loglikelihood function which I would like to maximize in R. So far, I have done maximization with the combined use of the mathematical programming language AMPL (www.ampl.com) and the solver SNOPT (http://www.sbsi-sol-optimize.com/manuals/SNOPT%20Manual.pdf). With these tools, maximization is carried out in a few seconds. I wonder if that

Dear all, I'm trying to use the "optim" function to replicate the results from the "glm" using an example from the help page of "glm", but I could not get the "optim" function to work. Would you please point out where I did wrong? Thanks a lot. The following is the code: # Step 1: fit the glm clotting <- data.frame( u =

Dear UseRs, I wrote the following function to use MLE. --------------------------------------------- mlog <- function(theta, nx = 1, nz = 1, dt){ beta <- matrix(theta[1:(nx+1)], ncol = 1) delta <- matrix(theta[(nx+2):(nx+nz+1)], ncol = 1) sigma2 <- theta[nx+nz+2] gamma <- theta[nx+nz+3] y <- as.matrix(dt[, 1], ncol = 1) x <- as.matrix(data.frame(1,