search for: pseudoinverse

...7.3625, 70.7917, 77.0993, 88.3608, 89.7200, 79.1031, 79.7421, 84.2469, 83.9371, 73.8800, 89.3921, 89.3900, 86.8921, 85.7036, 85.2664, 83.8700, 90.5493) > scatter.smooth(YMRS_Sum,resid) Warning messages: 1: at -0.02 2: radius 0.0004 3: all data on boundary of neighborhood. make span bigger 4: pseudoinverse used at -0.02 5: neighborhood radius 0.02 6: reciprocal condition number nan 7: zero-width neighborhood. make span bigger 8: There are other near singularities as well. 1 9: at -0.02 10: radius 0.0004 11: all data on boundary of neighborhood. make span bigger 12: pseudoinverse used at -0.02 13:...

...I met a probem recently and need your help. I would really appreciate it. I kept receiving the following error message when running a program: 'Error in svd(X) : infinite or missing values in x'. However, I did not use any svd function in this program though I did include the function pseudoinverse. Is the problem caused by doing pseudoinverse? Best regards, Tongtong

Dear friends - I'm on windows, R 2.7.0 I try again asking if anyone can explain why a single pig of 16 makes so wild swings. Warnings are issued, and they are 1: pseudoinverse used at 482.1 2: neighborhood radius 242.1 3: reciprocal condition number 0 4: at 360 5: radius 14400 6: all data on boundary of neighborhood. make span bigger 7: There are other near singularities as well. 14400 8: pseudoinverse used at 482.1 9: neighborhood radius 242.1 10: reciprocal conditio...

...to be 9. In Simon Wood's book, the rank of covariance matrix is usually either 9 or 99 (pages 239-230 and 259). Can anyone comment on why so many smooth terms have a denominator degree of freedom involving 9. Simon Wood writes "r is usually determinted numerically, while forming the pseudoinverse of the covariance matrix, or with reference to the effective degrees of freedom of the term" which doesn't really clarify the issue for me at least. Thanks. Brendan Klick Johns Hopkins University School of Medicine. [[alternative HTML version deleted]]

...m of linear equations has an infinity of solutions if a solution exists at all. How that works you can find here: https://en.wikipedia.org/wiki/System_of_linear_equations in the section "Matrix solutions". For your simple example you can do it like this: library(MASS) Ag <- ginv(A) # pseudoinverse xb <- Ag %*% b # minimum norm solution Aw <- diag(nrow=nrow(Ag)) - Ag %*% A # see the Wikipedia page w <- runif(3) z <- xb + Aw %*% w A %*% z - b N <- Null(t(A)) # null space of A; see the help for Null in package MASS A %*% N A %*% (xb + 2 * N) - b For sparse systems you will...

...f linear equations has an infinity of solutions if a solution exists at all. How that works you can find here: https://en.wikipedia.org/wiki/System_of_linear_equations in the section "Matrix solutions". For your simple example you can do it like this: library(MASS) Ag <- ginv(A)??? # pseudoinverse xb <- Ag %*% b # minimum norm solution Aw <- diag(nrow=nrow(Ag)) - Ag %*% A? # see the Wikipedia page w <- runif(3) z <- xb + Aw %*% w A %*% z - b N <- Null(t(A))??? # null space of A;? see the help for Null in package MASS A %*% N A %*% (xb + 2 * N) - b For sparse systems you wi...

...0) and the problem is that the panel for my random effects vs pcat is displayed as a scatterplot rather than as a boxplot. I am getting told to check warnings and these warnings look like: Warning messages: 1: at 0.99 2: radius 0.0001 3: all data on boundary of neighborhood. make span bigger 4: pseudoinverse used at 0.99 5: neighborhood radius 0.01 6: reciprocal condition number -1.#IND 7: zero-width neighborhood. make span bigger I do not get these warnings if I exclude the problematic variable pcat so must be something to do with this. Any ideas? Many thanks Greg [[alternative HTML version dele...

Hi all, I am just learning R and I am trying to work through the book "Applied Longitudinal Data Analysis" by Singer & Willett. I have some code for this book that supposedly works in S-Plus (I don't have S-Plus so I can't verify that) and I am trying to run the examples in R. Most of the examples run, but I have one plot that gives me an error message. I have

...o(elevation,span=0.05 )+disbinary,family=binomial(logit),data=point) #span is 0.05 #22 warnings() > warnings() 1: In lo.wam(x, z, wz, fit$smooth, which, fit$smooth.frame, ... : k-d tree limited by memory; nvmax= 226 2: In lo.wam(x, z, wz, fit$smooth, which, fit$smooth.frame, ... : pseudoinverse used at 14923 3: In lo.wam(x, z, wz, fit$smooth, which, fit$smooth.frame, ... : neighborhood radius 1 4: In lo.wam(x, z, wz, fit$smooth, which, fit$smooth.frame, ... : reciprocal condition number 0 ........................................................ Q1: Their warning messages were...

...ngth(T) dorder <- dorder + 1 # -- calculate filter coefficients -- fc <- (fl-1)/2 # index: window left and right X <- outer(-fc:fc, 0:forder, FUN="^") # polynomial terms and coefficients Y <- pinv(X); # pseudoinverse # -- filter via convolution and take care of the end points -- T2 <- convolve(T, rev(Y[dorder,]), type="o") # convolve(...) T2 <- T2[(fc+1):(length(T2)-fc)] } #----------------------------------------------------------------------- # *** PseudoInvers of a Matrix **...

Dear R-users, I have one question about using SVD to get an inverse matrix of covariance matrix Sometimes I met many singular values d are close to 0: look this example $d [1] 4.178853e+00 2.722005e+00 2.139863e+00 1.867628e+00 1.588967e+00 [6] 1.401554e+00 1.256964e+00 1.185750e+00 1.060692e+00 9.932592e-01 [11] 9.412768e-01 8.530497e-01 8.211395e-01 8.077817e-01 7.706618e-01 [16]

I have a situation in R where I would like to find any x (if one exists) that solves the linear system of equations Ax = b, where A is square, sparse, and singular, and b is a vector. Here is some code that mimics my issue with a relatively simple A and b, along with three other methods of solving this system that I found online, two of which give me an error and one of which succeeds on the

Hi, I am working on a multivariate linear regression of the form y = Ax. I am seeing a great dispersion of y w.r.t x. For example, the correlations between y and x are very small, even after using some typical transformations like log, power. I tried with simple linear regression, robust regression and ace and avas package in R (or splus). I didn't see an improvement in the fit and

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)

Hi, I have a data frame with 6563 observations. I can run a regression with loess using four explanatory variables. If I add a fifth, R crashes. There are no missings in the data, and if I run a regression with any four of the five explanatory variables, it works. Its only when I go from four to five that it crashes. This leads me to believe that it is not an obvious problem with the data,