similar to: lsfit w/ rank-deficient x

I'm trying to extract the points above and below a particular lsfit. I can only get the residuals from the original fit though. x = runif(100, 0, 10) plot(x) abline(lsfit(1:100, test)) abline(lsfit(1:100, test + sd(test))) #I want the points above THIS line. Is there a way to use the coefficients from the fit to do this? Thanks for any help.

Hi, in R-3.0 I get the following error when calling lsfit with more observations than variables, which seems to come from an error in the formatting of the error message (note that this was not happening in 2.15.3): > nobs <- 5; nvar <- 6; lsfit(matrix(runif(nobs*nvar), ncol=nvar), runif(nobs), intercept=FALSE) Error in sprintf(ngettext(nry, "%d response", "%d

To the purpose of fitting a 2nd order polynomial (a + b*x + c*x^2) to the chunk of signal falling in a 17 consecutive samples window I wrote the following very crude script. Since I have no previous experience of using Least Square Fit with R I would appreciate your supervision and suggestion. I guess the returned coefficients of the oolynomial are: a = -1.3191398 b = 0.1233055 c = 0.9297401

Dear all, I'm new on R and this mailing list. We work on spatial rainfall estimation with R and Grass. We have a problem with the krige function from the sgeostat package. We would like to limit the estimated area with a polygon limit. I use a 50 points polygon to describe my work area. The krige function work quiet well without limit. But if I use this option I have the following error

Hello experts, as newcomer in pca, i have a question, concerning the princomp algorithm. With a dataset "r" containing 18 "input" parameters and 1 "output" parameter r[19], i got with the following fit ls <- lsfit(r[1:18],r[19]); lsdiag <- ls.diag(ls); lsdiag$std.dev a prediction error of: [1] 8.879561 what is quite reasonable. If i take only two

This is not a solution but your lsfit attempt #Error in lsfit(A, b) : only 3 cases, but 4 variables lsfit(A,b) gave that error because lsfit adds a column of 1 to its first argument unless you use intercept=FALSE. Then it will give you an answer (but I think it converts your sparse matrix into a dense one before doing any linear algebra). Bill Dunlap TIBCO Software wdunlap tibco.com On

ls.print produces error that I don't seem to be able to trace. Output of the commands as follows: (hyeung is a 24x2 matrix of data) ------------------------------------------------- > summary(hyeung) x.1 x.2 Min. : 28.0 Min. : 10.0 1st Qu.: 72.0 1st Qu.: 87.5 Median : 86.5 Median : 92.5 Mean : 81.0 Mean : 82.5 3rd Qu.: 97.0 3rd Qu.:100.0 Max.

ls.print produces error that I don't seem to be able to trace. Output of the commands as follows: (hyeung is a 24x2 matrix of data) ------------------------------------------------- > summary(hyeung) x.1 x.2 Min. : 28.0 Min. : 10.0 1st Qu.: 72.0 1st Qu.: 87.5 Median : 86.5 Median : 92.5 Mean : 81.0 Mean : 82.5 3rd Qu.: 97.0 3rd Qu.:100.0 Max.

Not really r-specific: Z = (b1 - b2) / SQRT ( SEb1^2 + SEb2^2) -------Original Message------- From: Gijsbert Stoet <stoet at volition.wustl.edu> Sent: 07/20/03 09:51 PM To: r-help at stat.math.ethz.ch Subject: [R] how to test whether two slopes are sign. different? > > Hi, suppose I do want to test whether the slopes (e.g. determined with lsfit) of two different population are

Hello, I am working on a project to create some scatter plots. I have syntax for 26 plots, and 22 of them display as they should. But here, for example, is a sample of the command syntax I am using: good <- complete.cases(affect1,adh1scr) plot (jitter(affect1,2.0),jitter(adh1scr,1.0),xlim=c(1,35),ylim=c(1,35),pch=1 6, main='Adherence Score by Affectiveness Level - Visit 1',

Hi All, I have millions of regression lines to fit. So I am looking for the most efficient approach in R. Details: I have a large desing matrix X. The dimension is n by p. Each time when fitting the model, select rows from this matrix X and form a new design matrix, called X_current. There is another binary matrix M, with dim m by n, and each row is a 1*n vector. It helps to determin X_current.

Dear: I want to construct a gee model in R. When I ran the program, there was a warning in the output. The warning is "Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 gee(cbind(hyper, nohyper) ~ I(Ethnic) + I(Gender) + I(drink) + : rank-deficient model matrix" What is the rank-deficient model matrix? What should I do to

I am trying to use lm() for resression followed by stepAIC function. Now when i try to use to predict for some input, predict() gives a warning : prediction from a Rank deficient matrix may be misleading. As I am new to R (or to statistics) How alarming this warning may be? Regards, ____________________________________________________________________________________ The fish are biting.

Hi everyone. I am a new user of R so thanks for your help! I'm running a regression and receive an error that states that the "residuals have rank 45 < 169." What does this mean? Thanks! -- View this message in context: http://n4.nabble.com/deficient-rank-question-tp1562644p1562644.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I would like to fit an lm in which a subset of the explanatory variables are linearly dependent. Thus I would like to include the restriction that all betas of these variables sum to 1. Is there a way to this in R? Or is this what happens automatically if I set singular.ok= T (which is the default, I believe)? Thanks for your help. Lorenz --

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

Take the following example: a <- rnorm(100) b <- trunc(3*runif(100)) g <- factor(trunc(4*runif(100)),labels=c('A','B','C','D')) y <- rnorm(100) + a + (b+1) * (unclass(g)+2) m <- lm(y~a+b*g) summary(m) Here b is discrete but not treated as a factor. I am interested in computing the effect of b within groups defined by the

Hi, I have encountered this error when attempting a One-way Repeated-measure ANOVA with my data. I have read the "Anova in car: SSPE apparently deficient rank" thread by I'm not sure the within-subject interaction has more degrees of freedom than subjects in my case. I have prepared the following testing script: rm(list = ls())

Dear all, I’m not sure if it is O.K. to ask this question here. But where can I find the code for the function ‘C_Cdqrls’ which is called by the R function ‘lsfit‘. Thank you all. Sorry for being naïve if so. -------------------- Ziqiang Zhao 2013-04-23 [[alternative HTML version deleted]]

I have design with two repeated-measures factor, and no grouping factor. I can analyze the dataset successfully in other software, including my legacy DOS version BMDP, and R's 'aov' function. I would like to use 'Anova' in 'car' in order to obtain the sphericity tests and the H-F corrected p-values. I do not believe the data are truly deficient in rank. I