search for: singularities

Hello, I'm using the gstat package within R for an automated procedure that uses ordinary kriging. I can see that there is a logical ("singular") atrtibute of some adjusted model semivariograms: .- attr(*, "singular")= logi TRUE I cannot find documentation about the exact meaning and the implications of this attribute, and I dont know anything about the inner calculations

Hi all I'm facing an issue while running dovecot inside a singularity (https://sylabs.io/singularity/) container dovecot version is 2.3.4.1 (configuration below) running on debian buster, inside a container made with singularity version 3.4.2 unfortunately, when I try to start dovecot, it gives: Singularity test.sif:~> cat /var/log/mail.log Dec 30 17:23:38 testnode dovecot: master:

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 ~

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

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, If I have remembered correctly, a square matrix is singular if and only if its determinant is zero. I am a bit confused by the following code error. Can someone give me a hint? > a <- matrix(c(1e20,1e2,1e3,1e3),2) > det(a) [1] 1e+23 > solve(a) Error in solve.default(a) : system is computationally singular: reciprocal condition number = 1e-17 Thanks in advance! Feng --

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

...>> lm(formula = Employed ~ GNP + Population + I(GNP + Population), >> data = longley) >> Residuals: >> Min 1Q Median 3Q Max >> -0.80899 -0.33282 -0.02329 0.25895 1.08800 >> Coefficients: (1 not defined because of singularities) >> Estimate Std. Error t value Pr(>|t|) >> (Intercept) 88.93880 13.78503 6.452 2.16e-05 *** >> GNP 0.06317 0.01065 5.933 4.96e-05 *** >> Population -0.40974 0.15214 -2.693 0.0184 * >> I(GNP...

//Referring to the response posted many years ago, copied below, what is the specific criterium used for singularity of the gradient matrix? Is a Singular Value Decomposition used to determine the singular values? Is it the gradient matrix condition number or some other criterion for determining singularity? // //Glenn // / / /> What does the error 'singular gradient' mean

Hi everyone. I'm working with nlme and I have a question regarding nlme fits that fail because of singularity issues. Specifically, there a way to return an nlme object when the estimation process runs into a singular matrix? For example, can the results up to the point of an error such as "Error in solve.default(pdMatrix(a, fact = TRUE)) : system is computationally singular" or

...d ~ GNP + Population + >> I(GNP + Population), >> data = longley) >> >> >> Residuals: >> Min 1Q Median 3Q Max >> -0.80899 >> -0.33282 -0.02329 0.25895 1.08800 >> >> >> Coefficients: (1 not defined because of singularities) >> >> Estimate Std. Error t value Pr(>|t|) >> (Intercept) >> 88.93880 13.78503 6.452 2.16e-05 *** >> GNP 0.06317 >> 0.01065 5.933 4.96e-05 *** >> Population -0.40974 0.15214 >> -2.693 0.0184 * >> I(GNP + Population) NA NA NA...

Dear all, I am trying an automatic model selection for a multiple linear regression using function lm and update. But, I meet a problem when using update. The problem is the function update can not update when variables as a vector(for example,x is a matrix with 100 regression variables). The code is as below: > model<-lm(y~x1,singular.ok=T,na.action=na.omit) > for(i in 1:100){

My colleague runs R 1.7.1 under Windows XP. He remarks: >> A > [,1] [,2] [,3] > [1,] 1 2 3 > [2,] 4 5 6 > [3,] 7 8 9 >> >> b > [1] 1 2 3 >> solve(A,b) > [1] -0.3333333 0.6666667 0.0000000 >> solve(A) > [,1] [,2] [,3] > [1,] -4.5036e+15 9.00720e+15 -4.5036e+15 > [2,] 9.0072e+15

I am posting this here because the other posters in AppDB say the game works fine. For me, the video is slow and jerky, not playable at all. System: Dell XPS630 q9550 dual core-2, 8gm RAM, 2x NVIDIA 9800GT, lots of disk space (meets all system requirements) kUbuntu-10.04, all current updates, nvidia 195.36.24 driver. Have tried kde, gnome, and lxde desktops (compositing disabled), same problem in

Dear R-listers, I have a dxr matrix Z, where d > r. And the product Z*Z' is a singular square matrix. The problem is how to get the left inverse U of this singular matrix Z*Z', such that U*(Z*Z') = I? Is there any to figure it out using matrix decomposition method? Thanks a lot for your help. Fred

...opulation), + data=longley) > summary(mod.lm) Call: lm(formula = Employed ~ GNP + Population + I(GNP + Population), data = longley) Residuals: Min 1Q Median 3Q Max -0.80899 -0.33282 -0.02329 0.25895 1.08800 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 88.93880 13.78503 6.452 2.16e-05 *** GNP 0.06317 0.01065 5.933 4.96e-05 *** Population -0.40974 0.15214 -2.693 0.0184 * I(GNP + Population) NA NA NA...

Quantreggers: I'm trying to run rq() on a dataset I posted at: https://docs.google.com/file/d/0B8Kij67bij_ASUpfcmJ4LTFEUUk/edit?usp=sharing (it's a 1500kb csv file named "singular.csv") and am getting the following error: mydata <- read.csv("singular.csv") fit_spl <- rq(raw_data[,1] ~ bs(raw_data[,i],df=15),tau=1) > Error in rq.fit.br(x, y, tau = tau, ...) :

I'm trying to fit a Gompertz sigmoid as follows: x <- c(15, 16, 17, 18, 19) # arbitrary example data here; y <- c(0.1, 1.8, 2.2, 2.6, 2.9) # actual data is similar gm <- nls(y ~ a+b*exp(-exp(-c*(x-d))), start=c(a=?, b=?, c=?, d=?)) I have been unable to properly set the starting value '?'s. All of my guesses yield either a "singular gradient" error if they

Dear Users, I am a new user of the sem package. I have a model that is being flagged by sem as "S is numerically singular: expect problems" I have checked John Fox's response to a similar problem. Obviously the variance-covariance matrix is singular, but none of the possible reasons seems to hold in my case. Any leads how I could get the model to work? from Prof. John Fox That

Have you tried setting linux capabilities, like NET_BIND_SERVICE,CHOWN,SYS_CHROOT,SETGID? Have you checked the permissions of paths? I had to relocate the run dir with things like these && mkdir /var/dovecot \ && mkdir /var/lib/dovecot \ && (umask 027 ; mkdir /var/dovecot/login) \ && (umask 022 ; mkdir /var/dovecot/empty) \ && (umask