similar to: eigenvalues for a sparse matrix

Hi List l am interested in developing price model. I have found a research paper related to price model of corn in US market where it has taken demand & supply forces into consideration. Following are the equation: Supply equation: St= a0+a1Pt-1+a2Rt-1+a3St-1+a5D1+a6D2+a7D3+U1 -(1) Where D1,D2,D3=Quarterly Dummy Variables(Since quarterly data are considered) Here, Supply

I am fitting a reduced form VAR model using VAR in the vars library. I have several endogenous variables, and two exogenous variables. I would like to explore the effects of a shock to one of the exogenous variables on one of the endogenous variables. Using irf in the vars library only calculates the irf for the endogenous variables, this is obviously by design, is there some theoretical

Hi, I would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for

Hi, I am brand new to R and not familiar with the language, though I have been reading the manuals and making some slow going progress. I am working with some source code from a Global Vector Auto -Regressive program written by Ranier Puhr from the R-forge group. I need help interpreting the processes of the following code. I am going to post in parts since it's pretty long: GVAR

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

Hi, New to both R and SEM, so this may be a very simple question. I am trying to run a very simple path analysis using the sem package. There are 2 exogenous (FARSCH, LOCUS10) and 2 endogenous (T_ATTENT, RMTEST) observed variables in the model. The idea is that T_ATTENT mediates the effect of FARSCH and LOCUS10 on RMTEST. The RAM specification I used is FARSCH -> T_ATTENT, y1x1, NA

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

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 there, I want to estimate simultaneous equation model with panel data. The model looks as follows Y1=a0+a1*X1+a2*X2 Y2=b0+b1*X2+b2*X1 X1=Z1-(Y1/a1) X2=Z2-(Y2/b1) I In this model Y1, Y2, X1 and X2 are endogenous variables; Z1, Z2 are exogenous variables and a0, a1, a2, b0, b1 and b2 are parameters. Could any one please help me how to estimate this model in R. Thanking you in anticipation

>From the help document on KalmanLike, KalmanRun, etc., I see the linear Gaussian state space model is a <- T a + R e y = Z' a + eta following the book of Durbin and Koopman. In practice, it is useful to run Kalman filtering/smoothing/forecasting with exogenous factor: a <- T a + L b + R e y = Z' a + M b + eta where b is some known vector (a function of time). Some other

Hi all, Running simulations, I'm generating market response to 2 factors X&Y.. There is no closed form for the market response.. The results are store in a matrix Z(X <- seq(.02,.98,.02), Y <- seq(.01,.19,.01)).. For optmization purpose I need to approximate the values for any factor X in 0,02-0,98 and Y in 0,01-0,19 How can I do it ? For one factor : Xn-1 < x <= Xn

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

Is there any package for Vector Auto-regressive with exogenous variable other than fastVAR? Because it is not able to solve my problem of not taking the base in the model. Please suggest some appropriate solution!!!! -- View this message in context: http://r.789695.n4.nabble.com/Is-there-any-package-for-Vector-Auto-regressive-with-exogenous-variable-other-than-fastVAR-tp4644964.html Sent from

Hi to all, how could I to rotate automatically a data sheet which was imported by read.xls? x1 x2 x3 .... xn y1 1 4 7 ... xn/y1 y2 2 5 8 .... xn/y2 y3 3 6 9 ....xn/y2 yn ... ... ... Xn/Yn to y1 y2 y3 .... yn x1 1 2 3 ..... Yn/x1 x2 4 5 6 .... Yn/x2 x3 7 8 9 .... Yn/x2 xn ... ... ... ..... Yn/xn Kind regards Knut

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

The programming language mosml comes with foldr that 'accumulates' a function f over a list [x1,x2,...,xn] with initial value b as follows foldr f b [x1,x2,...,xn] = f(x1,...,f(xn-1,f(xn,b))...) Observe that "list" should have same elements so in R terminology it would perhaps be appropriate to say that the accumulation takes place over a 'vector'. I wonder if R

Can someone offer some advice on how to properly evaluate a SYMSXP from a .Call ? I have the following in R: variable xn, with an attribute "mu" which references the variable mu in the global environment. I know "references" is a loose term; mu was defined in this fashion as a way to implement deferred binding: foo <- function(x,mu) { attr(x,"mu") <-

I am looking for a Kalman filter that can handle a control input. I thought that l.SS was suitable however, I can't get it to work, and wonder if I am not using the right function. What I want is a Kalman filter that accepts exogenous inputs where the input is found using the algebraic Ricatti equation solution to a penalty function. If K is the gain matrix then the exogenous input

Hi, I have written a small C++ function and compile it. However in R I can't see the function I have defined in C++. I have read some web-pages about Rcpp and C++ but it is a bit confusion for me. Anyway, This is the C++-code: #include <Rcpp.h> using namespace Rcpp; // [[Rcpp::export]] List compute_values_cpp(int totalPoints = 1e5, double angle_increment = 0.01, int radius =

Dear all, The documentation for `all.equal.numeric` says Numerical comparisons for ?scale = NULL? (the default) are done by first computing the mean absolute difference of the two numerical vectors. If this is smaller than ?tolerance? or not finite, absolute differences are used, otherwise relative differences scaled by the mean absolute difference. But the actual behaviour