similar to: Computationally singular [provides coefficients but not covariance matrix]

For an application of ridge regression, I need to get the covariance matrices of the estimated regression coefficients in addition to the coefficients for all values of the ridge contstant, lambda. I've studied the code in MASS:::lm.ridge, but don't see how to do this because the code is vectorized using one svd calculation. The relevant lines from lm.ridge, using X, Y are:

Dear all, I am running a code using bootstraps for estimating standard errors but the mac crashes. When I use small number of bootstraps (100) it works fine but if I increase that number it crashes. Thanks in advance dimitris The code, the error and my mac characteristics are the following: ##############code##################### qr.1<- rq(y~factor(year)+factor(state)+x1+I(x^2)+I(x^3),

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 R experts: ?I am writing my own little newey-west standard error function, with heteroskedasticity and arbitrary x period autocorrelation corrections. ?including my function in this post here may help others searching for something similar. it is working quite well, except on occasion, it complains that Error in solve.default(crossprod(x.na.omitted, x.na.omitted)) : system is

thank you, achim. I will try chol2inv. sandwich is a very nice package, but let me make some short suggestions. I am not a good econometrician, so I do not know what prewhitening is, and the vignette did not explain it. "?coeftest" did not work after I loaded the library. automatic bandwidth selection can be a good thing, but is not always. as to my own little function, I like the

Hi All, I'm trying to estimate some parameters in my model via GMM using the function gmm(), but I keep getting the message "The covariance matrix of the coefficients is singular". I've changed the moment conditions and the initial value of the parameters, and I still get this message. Are the results valid after receiving this message? Any ideas on how to get rid of it?

Dear all, I am trying to estimate the prediction from a fixed effects model and their confidence intervals as well. Though I do not want to include in the prediction and at the confidence intervals the intercept. For that reason I used the argument incl.non.slopes=FALSE. But either if it is TRUE or FALSE it does not have any difference and also the system does not provide any warning. I really

Dear all, I'm running a self-written numerical optimization routine (hazard model) which includes computing the inverse of the outer product of the score. I have been getting the above error message ("System is computationally singular"), and after some tweaking, I realized that these variables have some high numbers and the problem could be circumvented by scaling them down (i.e.

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

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)

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, I am trying to use (i) as an index but R considers it as a function and not as text. To be more specific I would like for example to estimate some regressions named qrnox1, qrnox2, qrnox3,..... and so on. But when I am using qrnox(i) ot qrnox[i] it tries to find the ith element of vector qrnox. The thing is that I want to estimate the qrnoxi regression and not the qrnox(i) function or

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

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?

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,

Dear all, I am using the plm package for both fixed and random effects models on my country-year panel data. However, for some of the random effects models I get the following error: Error in solve.default(OM) : system is computationally singular: reciprocal condition number = 1.78233e-18 The same models work fine for fixed effects. I have also noticed that once I remove some of my variables

Hello Everyone I have a MCMC loop to calculate a time varying hierarchical Bayesian structure. This requires me to use around 5-6 matrix inversions in the loop. I use cholesky and chol2inv for the matrix decomposition. Because of the data I am working with I am required to invert a 167 by 167 matrix twice in one iteration. I need to run the iteration for 10000 times, but I get the error

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 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,

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