similar to: How do I output all the R-squares of an SUR? summary(fitSUR$eq[[1:4]])$r.squared does not work

Dear List Members,I am trying to set up a large system of equations and I am trying to find a simple way to set up the list command under the package system fit. Here is the example from system fit and what I am trying to do: EQ1 <- Y1 ~ X1 + X2 + X4EQ2 <- Y2 ~ X2 + X3EQ3 <- Y3 ~ X2 + X3 + X4EQ4 <- Y4 ~ X1 + X3 + X4eqSystem <-list(form1 = EQ1, form2 = EQ2, form3 = EQ3, form4 = EQ4)

Is there a way to apply paste to?list(form1 = EQ1, form2 = EQ2, form3 = EQ3, form4 = EQ4)?such that I don't have to write form1=EQ1 for all my models?(I might have a list of 20 or more)? I also need the EQs to read the formulas associated with them. For example, below, I was able to automate the name assignment but I could not figure out how to?to set up the list using?paste or other

Hello to everyone, I have 2 problems and would be very pleased if anyone can help me: 1) When I use the package "systemfit" for SUR regressions, I get two different variance-covariance matrices when I firstly do the SUR regression ("The covariance matrix of the residuals used for estimation") and secondly do the OLS regressions. In the manual for "systemfit" on page

Hi  I want to fit two equations simultaneously EQ1<-Y1~X1+X2 EQ2<-Y2~X1+X2 eqsystem<-list(Y1HAT=EQ1,Y2HAT=EQ2) fitols<-systemfit(eqsystem, method="OLS", data=BB) How do I get coefficients for the first equation? R code How do I restrict coefficient of X2 in the first equation (say , restrict it to less than  zero). R code Your help is appreciated. Dereje [[alternative

Dear list members, Is there a package somewhere for jointly estimating two poisson processes? I think the closest I've come is using the "SUR" option in the Zelig package (see below), but when I try the "poisson" option instead of the "SUR" optioin I get an error (error given below, and indeed, reading the documentation of the Zelig package, I get the impression

Hi I was trying to estimate simultaneous equation system in R using systemfit. I used the following commands >library(systemfit) > data(Kmenta) > attach(Kmenta) >eqDemand<-consump~price+income > eqSupply<-consump~price+farmprice+trend > fitsur<-systemfit("SUR",list(demand=eqDemand, supply=eqSupply)) and got the following error messege Error in

I have a list called "tabs" that I would like to have the same structure as my list "eqSystem." The two look like they have the same format but they are different because when I look at their attributes, class(eqSystem[[1]]) is "call" but class(tabs[[1]]) is "formula". I want to have class(tabs[[1]]) as a call too. So what does "call" mean? And how

I am having problem using output of lm() function for further analysing using systemfit package. Basicaly, the problem s following - I generate several formulas using lm() > fo1 <- lm(r98[,2] ~ f98[,1] + f98[,2] + ... + f98[,43]) > fo2 <- lm(r98[,1] ~ f98[,1] + f98[,2] + ... + f98[,43]) and than I want to estimate a general model using package systemfit. > fitsur <-

TO: r-help at lists.R-project.org r-help at stat.math.ethz.ch Hello to everyone, I found some problems using R in the estimation of systems of nonlinear equations like SURE (Seemingly Unrelated Regression Equations) with mutual parameters as the following system example: EQ1 PQ1=P1*G1+B1*(Y-P1*G1-P2*G2-P3*G3) EQ2 PQ2=P2*G2+B2*(Y-P1*G1-P2*G2-P3*G3) where G1,G2,G3,B1,B2 are the

Hi useRs, I'm trying for the first time to use a sem. The model finally runs, but gives a warning saying : "In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. " I found in R-help some posts on this warning, but my attemps to modify the code didn't change

A friend of mine sent me below so I am posting below. If it is not enough information, please just let me know and I will tell him. Thanks. -----Original Message----- Sent: Friday, April 13, 2007 3:58 PM To: Leeds, Mark (IED) Subject: R question I am using the "predict" function after I have done a simultaneous estimation of a system using "systemfit". fitsur is the output

Hi all, I'm trying to estimate model parameters in R for a pretty simple system of equations, but I'm having trouble. Here is the system of equations (all derivatives): eqAlgae <- (u_Amax * C_A) * (1 - (Q_Amin / Q_A)) eqQuota <- (p_max * R_V) / (K_p + R_V) - ((Q_A-Q_Amin)*u_Amax) eqResource <- -C_A * (p_max * R_V) / (K_p + R_V) eqSystem <- list(C_A = eqAlgae, Q_A = eqQuota,

Hello all, I am relatively new to R, but enjoying it very much. I am hoping that someone on this list can help me with an issue I am having. I am having issues with iterations over nleqslv, in that the solver does not appear to clean up memory used in previous iterations. I believe I've isolated the/my issue in a small sample of code: library(nleqslv) cons_ext_test <- function(x){

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

I'm getting this error on the attached code and breaking my head but can't figure it out. Any help is much appreciated. Thanks, Vince CODE: library(deSolve) Res_DAE=function(t, y, dy, pars) { with(as.list(c(y, dy, pars)), { res1 = -dS -dES-k2*ES res2 = -dP + k2*ES eq1 = Eo-E -ES eq2 = So-S -ES -P return(list(c(res1, res2, eq1, eq2))) }) } pars <- c(Eo=0.02,

Vince, When that happens, one possible reason is that your DAE is of index > 1, which cannot be solved by daspk. The solver radau, also from deSolve can handle DAEs up to index 3, but you need to rewrite the problem in the form M*y' = f(x,y), where M is a mass matrix. If you do that for your problem, and solve it with radau, then radau complains that the "matrix is repeatedly

I use code ( actually its code from vars package and its directly below ) to do a sequence of lm calls and the data I use from the matrix depends on restrictions. for(i in 1:K){ datares <- datasub[, which(x$restrictions[i, ] == 1),drop=FALSE] y <- yendog[, i] lmres <- lm(y ~ -1 + ., data=datares) # x$varresult[[i]] <- lmres # x$resid[, i] <-

Section I. Overview Hello, About a half year ago there was some rumour on bugtraq concerning a buffer overflow in Linux dynamic linkers, ld.so and ld-linux.so. You can take a look at the beginning of the thread at http://www.geek-girl.com/bugtraq/1997_3/0089.html to refresh old memories; I''ll capitalize anyway. Briefly, there exists a buffer overrun in ld-linux.so versions 1.7.14,

Hello to everyone, I found some problems using R in the estimation of systems of nonlinear equations like SURE (Seemingly Unrelated Regression Equations) with mutual parameters as the following system example: EQ1 PQ1=P1*G1+B1*(Y-P1*G1-P2*G2-P3*G3) EQ2 PQ2=P2*G2+B2*(Y-P1*G1-P2*G2-P3*G3) where G1,G2,G3,B1,B2 are the coefficients to estimate. command "nls" accept only single

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