similar to: Manual two-stage least squares in R

If I wanted to fit a logit model and account for clustering of observations, I would do something like: library(Design) f <- lrm(Y1 ~ X1 + X2, x=TRUE, y=TRUE, data=d) g <- robcov(f, d$st.year) What would I do if I wanted to do the same thing with a probit model? ?robcov says the input model must come from the Design package, but the Design package appears not to do probit? Thanks very

Dear R users,   I am trying to use "wgaim" package for QTL analysis using mixed model approach. But i am stuck with "asreml" function while using "wgaim" package. Do i need a separate package to activate "asreml" function beside "wgaim" package ?   If so, i tried to download "Asreml-R" package (i guess this is right package

Estimados, Les tengo una pequeña consulta. Estoy trabajando con ASReml en Rstudio, me encuentro corriendo un modelo (CHL) para obtener el ANOVA de la interacción de los factores gen x env CHL<- asreml(fixed= MS~geno:env, random = ~rep, data = index) (interacción) Sin embargo, cuando ejecuto el modelo, R indica que siguiente mensaje: Current workspace: 128.000000Mb Warning message: Abnormal

Hi all, I need to run a program (asreml) thousands of times and each time I have to provide a slightly different dataset. Because I have to run asreml under Windows (DOS or scripts) environment, I have trouble to pass a command variable (or pointer variable or %counter in the following example) to R program so at each counter R can generate a different data set for asreml to run. Any

Dear All: I want to do association study based on mixed linear model, My model not only includes serval fixed effects and random effects but also incorporates some covariates such as "birth weight". Otherwise, the size of the data are about 180 individuals and 12 variables and 60000 Fixed effect estimates As asreml-R is not free ,is there any packages for my study? I heard nlme or

Dear friends I want to estimate an equation using two-stage least square but suspect that the model suffers from autocorrelation. Can someone please advise how to implement bootstrapping method in order to calculate the correct standard errors in R? Thank you. Kind regards Thanaset -- View this message in context:

I m in front of a problem of simultaneity bias between two equations and would like to apply the two stage level least square....Is there a special command in R ? I didn't found something about that in the R help. Thanks for your help !!!! ************************************************************** Mathieu Vuilleumier - collaborateur scientifique Institut de recherches ??conomique et

Hi Everyone, I am using ASReml to fit a spatial model. I do not have all the components of ASReml when I call; names(summary()) e.g. names(summary(fcov.asr2)) [1] "call" "loglik" "nedf" "sigma" "varcomp" I am trying to get the coefs but I get "NULL". Does anybody know the reason? Any help would be much appreciated. Regards

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

Using R-2.8.0 and R-2.8.1, I get behaviour like this: R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 [....] > Sys.getenv("LD_LIBRARY_PATH") LD_LIBRARY_PATH

Dear R users, I 'm working on a dataset consisting of 4 different dataframes with tree, leaf, fruit and seed measurements made on 300 trees, coming from 10 provenances (30 trees per provenance, 10 leaves/fruits/seeds per tree). Provenances are fixed effects (they were not randomly chosen), but trees within provenances and leaves/fruits/seeds within trees were randomly assigned. I wanted to

Hi guys I have data that contains the variances vt of the yields of 1, 2, 3, 4, 5,10, 20 year bonds. Assuming the Hull-White model for the yield of a t-year zero-coupon bond, I have to estimate the ? of the Hull-White model using nonlinear least squares and give a 95% con?dence interval for each parameter. Please can you guys tell how to find out ? using R. Any suggestion regarding what functions

Dear all, When doing nonlinear regression, we normally use nls if e are iid normal. i learned that if the form of the variance of e is not completely known, we can use the IRWLS (Iteratively Reweighted Least Squares ) algorithm: for example, var e*i =*g0+g1*x*1 1. Start with *w**i = *1 2. Use least squares to estimate b. 3. Use the residuals to estimate g, perhaps by regressing e^2 on

Does anyone can provide a code to implement least median squares regression in R (not using the lqs function or calling C functions)? Reason: teaching/learning purposes Thanks PM

Dear All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is

I need some help with using recursive least squares lm.fit.recursive {quantreg}. I found some references online but cannot find a reproducible example as to how to use recursive least squares. I'd really appreciate if anyone can point me to a reproducible example. [[alternative HTML version deleted]]

Hi all, A good new year for everybody. Could somebody help me on a question? The Singular Value Decomposition of a matrix A gives A = U * D * t(V) I A is a M X N matrix, U is the left singular matrix (M X N), D is a diagonal singular values matrix (N X N) and V is the transpose right singular ortogonal matrix (N X N). By taking the first l columns of V, with gives a (l X l) matrix, i know

Hello guys. I've built and uploaded to CRAN an R interface to MINPACK Fortran library, which solves non-linear least squares problem by modification of the Levenberg-Marquardt algorithm. The package includes one R function, which passes all the necessary control parameters to the appropriate Fortran functions. The package location is

Hello guys. I've built and uploaded to CRAN an R interface to MINPACK Fortran library, which solves non-linear least squares problem by modification of the Levenberg-Marquardt algorithm. The package includes one R function, which passes all the necessary control parameters to the appropriate Fortran functions. The package location is

I am using the non-linear least squares routine in "R" -- nls. I have a dataset where the nls routine outputs tight confidence intervals on the 2 parameters I am solving for. As a check on my results, I used the Python SciPy leastsq module on the same data set and it yields the same answer as "R" for the coefficients. However, what was somewhat surprising was the the