similar to: Gauss-Laguerre using statmod

Hello! I would like to use lmer() to fit data, which are some estimates and their standard errors i.e kind of a "meta" analysis. I wonder if weights argument is the right one to use to include uncertainty (standard errors) of "data" into the model. I would like to use lmer(), since I would like to have a "freedom" in modeling, if this is at all possible. For

Hi all, We know that Hermite polynomial is for Gaussian, Laguerre polynomial for Exponential distribution, Legendre polynomial for uniform distribution, Jacobi polynomial for Beta distribution. Does anyone know which kind of polynomial deals with the log-normal, Student抯 t, Inverse gamma and Fisher抯 F distribution? Thank you in advance! David [[alternative HTML version deleted]]

dear R experts---sorry, second question of the day. I want to match some moments. I am writing my own code---I have exactly as many moment conditions as parameters, and I am leary of having to learn the magic of GMM weighting matrices (if I was to introduce more). the process sounds easy conceptually. (Seen it in seminars many times, so how hard could it possibly be?...me thinks) first

I've written a series of functions that evaluates an integral from -inf to a or b to +inf using equally spaced quadrature points along a normal distribution from -10 to +10 moving in increments of .01. These functions are working and give very good approximations, but I think they are computationally wasteful as I am evaluating the function at *many* points. Instead, I would prefer to use

R programming question, not machine learning, although that's the content. Apologies to all for whom the following code is eye-burning. I am using foreach() to run a simulation on a randomForest model (actually conditional randomForest ... "party" package). The simulation is in two dimensions. examining how "mtry" and "ntrees" are related in terms of predictive

Hi all, I'm trying to define and log-likelihood function to work with MLE. There will be parameters like mu_i, sigma_i, tau_i, ro_i, for i between 1 to 24. Instead of listing all the parameters, one by one in the function definition, is there a neat way to do it in R ? The example is as follows: ll<- function(mu1=-0.5,b=1.2,tau_1=0.5,sigma_1=0.5,ro_1=0.7) { if (tau1>0 &&

Spencer Thanks for your thoughts on this. I did a bit of work and did end up with a method (more a trick), but it did work. I am certain there are better ways to do this, but here is how I resolved the issue. The integral I need to evaluate is \begin{equation} \frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta} {\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta} \end{equation} Where

Hi everybody, I am new to rails. I get the following error when run the rake engine:migrate for the engine login_engine. ------------------------------------------------------------------------ gbalaji:~/projects/test/vendor/plugins/login_engine gopalbalaji$ rake engine_migrate ENGINE=log --trace (in /Users/gopalbalaji/Projects/test) ** Invoke engine_migrate (first_time) ** Invoke

Hello, I have some parameters from Mclust function. The parameters are in the form *parametersDf * * mu_1 mu_2 var_mc1 var_mc2 c1 c2 * *2 1.357283 2.962736 0.466154 0.1320129 0.5258975 0.4741025 * *21 8.357283 9.962736 0.466154 0.1320129 0.5258975 0.4741025 * Each row in the above data frame

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

I am having trouble printing a table out to the GUI display when the table is created and printed within a loop. I get a "Error: syntax error message" If I comment out the print statement, the loop runs fine and I can print out the last iteration of the table. ...[multiple loops and calculations ending with.....]... +

Hello, I am using R2.5 under Windows. Looks like the following statement vars <- (obj$sigma^2)*vw in getVarCov.gls method (nlme package) needs to be replaced with: vars <- (obj$sigma*vw)^2 With best regards Andrzej Galecki Douglas Bates wrote: >I'm not sure when the getVarCov.gls method was written or by whom. To >tell the truth I'm not really sure what

Dear All i have a centos 5 server running as a backup PC server and is working perfect for abt 3 months jus a couple of days back when i tried to telnet to the server it gave me connection refused i go to the server and when i say telnet localhost it says getaddrinfo: localhost Name or service not known the /etc/hosts file has the loopback entry and the actuall ip address of the server

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests). I am following Smoothing Splines, D.G. Pollock (available online) where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i

Hi! I am using the NLME package for R to modeling glucose-insuline response with Bergman's model, very similar to the example in the documentation for the NLME package. My question concerns the model for the residuals. I use a proportional model , Var(e_{ij})=(sigma_g*G(t))^2 for the glucose response and Var(e_{ij})=(sigma_i * I(t))^2 for the insulin response. Hence I have a varPower model,

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests) I am following http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) +

Hello all, I keep on getting the following error message when I try downloading statmod: > install.packages("statmod") Installing package(s) into ‘C:\Users\Isaac\Documents/R/win-library/2.12’ (as ‘lib’ is unspecified) trying URL ' http://www.revolution-computing.com/cran/bin/windows/contrib/2.12/statmod_1.4.9.zip ' Error in download.file(url, destfile, method, mode =

#I wish to create a "list of three dataframes" ("results2") from a "list of two dataframes" (temp) and a dataframe ("c")? #Please advise. a <- data.frame(x=c(1,2,3),y=c(5,7,9)) b <- data.frame(x=c(2,4,7,9),y=c(2,3,5,4)) c <- data.frame(x=c(22,34,7,9),y=c(52,63,5,4)) results1 <- list(a,b,c) #what I want #but this is how I need to get there

Just to inform: I posted that before in R-sig-ecology but as it might be interesting also for other useRs, I post it also to the general r-user list: Hello Alexandre, thank you very much. I also found another way to extract summarizing information from lm results over e.g. 1000 repeated model runs: results2 <- t(as.data.frame(results)) summary(results2) Although some questions popped up in