similar to: System is computationally singular error for plm random effects models

Dear, I built the generalized method of moments model to estimate the sales rank in the bookstore using plm package in R. The equation is: data1.gmm <- pgmm(dynformula(lnsales_rank ~ ln_price + avg_ham_rate + avg_spam_rate + num_of_ham+ num_of_spam + ship_code2 +ship_code3 +ship_code4+ ship_code5+ ship_code6 + ship_ code7, lag = list(0, 0, 0, 0,0,0,0,0,0,0,0,0), log =FALSE), data=data,

Hi list, has anyone succeeded in using pgmm() on any dataset besides Arellano/Bond's EmplUK, as shown in the vignette? Whatever I try, I eventually get a runtime error because of a singular matrix at various points in pgmm.diff() (which gets called by pgmm()). For example, when estimating a "dynamic" version of the Grunfeld data: data(Grunfeld, package="Ecdat") grun

Dear Sir, I am using the package pgmm you build in panel regression. However, I found that when T is 10, N=30, the error would show as following: system is computationally singular: reciprocal condition number But the similar code works well on Stata, so I wonder how I can optimize the algorithm, for example , the inverse matrix optimization ? And I have checked my data as well, no

Hi folks, I know, this is a fairly common question and I am really disappointed that I could not find a solution. I am trying to calculate Mahanalobis distances in a data frame, where I have several hundreds groups and several hundreds of variables. Whatever I do, however I subset it I get the "system is computationally singular: reciprocal condition number" error. I know what it means

Hi uRsers, when inverting a 2 by 2 matrix using solve, I encountered a error message: solve.default(sigma, tol = 1e-07) : system is computationally singular: reciprocal condition number = 1.7671e-017 and then I test the determinant of this matrix: 6.341393e-06. In my program, I have a condition block that whether a matrix is invertible like this: if(det(sigma)<1e-7) return NULL;

Dear R users, i try to use function dmvnorm(x, mean, sigma, log=FALSE) from R package mvtnorm to calculate the probability of x under the multivariate normal distribution with mean equal to mean and covariance matrix sigma. I become the following Error in solve.default(cov, ...) : system is computationally singular: reciprocal condition number = 1.81093e-19 What could be the reason of it?

Hi all, I was using the Newton-Raphson method to estimate paremeters in the model developed by my supervisor. However, when I interatively computed theta(t+1)=theta(t) - solve(H)*s (where the Hessian matrix and score vector were explicitely derived), I got the error message: Error in solve.default(H) : system is computationally singular: reciprocal condition number = 1.70568e-032. Assume my score

Hello, I am using {plm} to estimate panel models. I want to estimate a model that includes fixed effects for time and individual, but has a random individual effect for the coefficient on the independent variable. That is, I would like to estimate the model: Y_it = a_i + a_t + B_i * X_it + e_it Where i denotes individuals, t denotes time, X is my independent variable, and B (beta) is the

Hi, I'm having trouble with glmmPQL from the MASS package. I'm trying to fit a model with a binary response variable, two fixed and two random variables (nested), with a sample of about 200,000 data points. Unfortunately, I'm getting an error message that is difficult to understand without knowing the internals of the glmmPQL function. > model <- glmmPQL(primed ~

Hi, I am estimating a regression but the summary command is unable to provide me results, while the coefficients are available from the coefficients value. I suppose that it cannot estimate the covariance matrix. Is there any command that I can relax the tolerance so it can estimate the covariance matrix. The code and the error of R is: eq1<-rq(y~factor(year)+factor(state)+x1+x2+x3,

Hello everyone, I'm trying to estimate the parameters of the returns series attached using the GARCH code below, but I get the following error message: Error in solve.default(Hessian) : system is computationally singular: reciprocal condition number = 0 Error in diag(solve(Hessian)) : error in evaluating the argument 'x' in selecting a method for function 'diag' Can

Hi, I have a dataset which has around 138 variables and 30,000 cases. I am trying to calculate a mahalanobis distance matrix for them and my procedure is like this: Suppose my data is stored in mymatrix > S<-cov(mymatrix) # this is fine > D<-sapply(1:nrow(mymatrix), function(i) mahalanobis(mymatrix, mymatrix[i,], S)) Error in solve.default(cov, ...) : system is computationally

Hi all, I am trying to run an autologistic model using the function errorsarlm from spdep package. **I built an XY matrix extracting the two colums from matriz** coords1<-matriz[matriz$casos1==1, c(4,5)] coords1<-as.matrix(coords1) **I identify neighbours of region points** nb20<-dnearneigh(coords1,0,20,longlat=TRUE) ** I build a neighbours list with spatial weights**

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645610 I am estimating a system of seemingly unrelated regressions (SUR) with `gmm::sysGmm` in R. Each of the equations has one unique regressor and one common regressor. The common regressor is a dummy variable indicating the last observation (n-1 zeros followed by 1). I impose a restriction that

Hi all, I am trying to set up a simple path analysis in the SEM package, but I am having some trouble. I keep getting the following error message or something similar with my model, and I'm not sure what I'm doing wrong: Error in solve.default(C) : system is computationally singular: reciprocal condition number = 2.2449e-20 In addition: Warning message: In sem.default(ram = ram, S = S,

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

Hello, I am trying to conduct a Hausman test to choose between FE estimators and RE estimators. When I try to run: library(plm) fixed <- plm(ROS ~ DiffClosenessC +ZZiele + AggSK + nRedundantStrecken + Degree + KantenGew + BetweennessC + SitzKappazitaet, data=Panel,index=c("id","time"),model="within") summary(fixed) fixef(fixed) random <-plm(ROS ~

Dear R-help, I'm very sorry to ask 2 questions in a week. I am using the package 'crr' and it does exactly what I need it to when I use the dataset a. However, when I use dataset b I get the following error message: Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number =

Hello, although I searched for a solution related to my problem I didn?t find one, yet. My skills in R aren?t very large, however. For my Diploma thesis I need to run a GMM estimation on a dynamic panel model using the "pgmm" - function in the plm-Package. The model I want to estimate is: "Y(t) = Y(t-1) + X1(t) + X2(t) + X3(t)" . There are no "normal" instruments