search for: singularization

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

>>>>> Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Thu, 14 Sep 2017 10:13:02 +0200 writes: >>>>> Fox, John <jfox at mcmaster.ca> >>>>> on Wed, 13 Sep 2017 22:45:07 +0000 writes: >> Dear Terry, >> Even the behaviour of lm() and glm() isn't entirely consistent. In both cases,

//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

>>>>> Fox, John <jfox at mcmaster.ca> >>>>> on Thu, 14 Sep 2017 13:46:44 +0000 writes: > Dear Martin, I made three points which likely got lost > because of the way I presented them: > (1) Singularity is an unusual situation and should be made > more prominent. It typically reflects a problem with the > data or the

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

Dear Terry, Even the behaviour of lm() and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance matrix: ---------------- > mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), + data=longley) >

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