similar to: Orthogonal Distance Regressions

I'm trying to fit a simple linear regression of just Y ~ X, but both X and Y are noisy. Thus instead of fitting a standard linear model minimizing vertical residuals, I would like to minimize orthogonal/perpendicular residuals. I have tried searching the R-packages, but have not found anything that seems suitable. I'm not sure what these types of residuals are typically called

[Originally sent this to r-help at lists.R-projects.org, but in case that's the wrong list I'm re-posting. Apologies if this becomes a re-post] -------------- next part -------------- An embedded message was scrubbed... From: Jonathon Kopecky <jkopecky at umich.edu> Subject: Need to fit a regression line using orthogonal residuals Date: Mon, 29 Jan 2007 14:52:24 -0500 Size: 1138

Hello all ... I'm trying to join a W2K domain and I'm getting the following error message: sun220:root# smbpasswd -j hern cli_net_req_chal: Error NT_STATUS_INVALID_COMPUTER_NAME cli_nt_setup_creds: request challenge failed modify_trust_password: unable to setup the PDC credentials to machine xxx.xxx.xxx.xxx(IP ommitted). Error was : NT_STATUS_UNSUCCESSFUL. 2002/07/24 09:44:18 :

Dear R helpers, I think that this may be a bit of a math question as the more I consider it, the harder it seems. I am trying to come up with a way to work out the minimum distance between line segments. For instance, consider 20 random line segments: x1 <- runif(20) y1 <- runif(20) x2 <- runif(20) y2 <- runif(20) plot(x1, y1, type = "n") segments(x1, y1, x2, y2)

Hello, I have a problem with an LAN-Server behind an NAT-router. Asterisk Version 1.2.1 or 1.2.2 doesnt matter 10 minutes after starting Asterisk I loose all registrations at external SIP-proxys. The reason seemed to be that Asterisk send every second an request to every sip-proxy "Request: OPTIONS sip:sip.domain.tld". Every request is responded by the sip-proxy. After some minutes

Hello, I came across an interesting problem in my home lab a few weeks ago as I'm prepping for my RHCE exam using Michael Jang study guide. I've been at this for days now, and I still can't wrap my head around how two or more virtual networks in default NAT configuration are even allowed to communicate with each other despite what the libvirt documentation said. Here's the

Hello. I have a very simple code like this: int sum(int i, int j) { int k; k = i + j; } #ifdef LINUX #include <stdio.h> #include <stdlib.h> int main () { int k; k = sum(3,4); return k; } #endif If I emit this to SPARC assembly mnemonics through LLVM, it is shown as follows

I'm using Postfix, Amavisd-new/SpamAssassin, and Dovecot. They all run on the same server (running Linux). The mailboxes are stored on the same server in maildir format. The logical path for incoming messages (as I understand it) is: 1) received by Postfix; handed off to Amavisd-new 2) Amavisd-new checks to see whether or not it's spam/undesirable; hands off to Dovecot deliver 3)

On Wed, 15 May 2019 23:08:17 +0200 Halil Pasic <pasic at linux.ibm.com> wrote: > On Tue, 14 May 2019 10:47:34 -0400 > "Jason J. Herne" <jjherne at linux.ibm.com> wrote: > > Are we > > worried that virtio data structures are going to be a burden on the 31-bit address space? > > > > > > That is a good question I can not answer. Since

On Wed, 15 May 2019 23:08:17 +0200 Halil Pasic <pasic at linux.ibm.com> wrote: > On Tue, 14 May 2019 10:47:34 -0400 > "Jason J. Herne" <jjherne at linux.ibm.com> wrote: > > Are we > > worried that virtio data structures are going to be a burden on the 31-bit address space? > > > > > > That is a good question I can not answer. Since

1) Why did you submit this *twice*, as PR#7826 and PR#7827? Please don't be so careless of the volunteers' time. 2) > print.POSIXct function (x, ...) { print(format(x, usetz = TRUE, ...), ...) invisible(x) } is definitely *not* implicated. (Use of ... in two places is correct.) 3) On FC3: > unusual_and_faults Error: protect(): protection stack overflow >

DeaR useRs: I have a problem, because I have a file in fixed width format and I can read it with 'read.fwf' and after I can use these data to do generate new columns (variables) and when I am going to write this set of data (data.frame) I can't find a function which storages my data.frame in fixed width format. Can you help me? Thanks in advance. Agust??n P??rez Universidad Miguel

Hi, In Asterisk 1.8.0 dialplan command Dial(SIP/peer_name) produces errors if no such peer_name defined instead of just saying "peer not found": [Nov 19 20:01:23] ERROR[7827]: netsock2.c:245 ast_sockaddr_resolve: getaddrinfo("sdf", "(null)", ...): Name or service not known [Nov 19 20:01:23] WARNING[7827]: chan_sip.c:5041 create_addr: No such host: sdf [Nov 19

This very likely falls in the category of an unexpected result due to user ignorance. I generated the following data: time <- 0:10 set.seed(4302007) y <- 268 + -9*time + .4*(time^2) + rnorm(11, 0, .1) I then fit models using both orthogonal and raw polynomials: fit1 <- lm(y ~ poly(time, 2)) fit2 <- lm(y ~ poly(time, degree=2, raw=TRUE)) > predict(fit1, data.frame(time =

Hey, all Will you please tell me how to generate multiple square orthogonal matrices for data transformation usage? Thanks. Fred

Hi, I'm a pgmm (plm) user and would like to know if a orthogonal transformation is available, as in Stata xtabond2. Can someone help me? Thanks! Kinds regards, Eva [[alternative HTML version deleted]]

Hey, R-listers, I am now going to use Bayesian mathod to estimate a matrix parameter C. It is assumed that C is an orthogonal matrix already. We know, if C is an arbitrary column vector, we may use multivariate Gaussian prior on it. However, now it is a matrix, so what can I do to define a proper prior probability density function for C? Thanks for your point. Have a nice day! Fred

I'm working with a orthogonal and nested model (mixed). I have four factors, A,B,C,D; A and B are fixed and orthogonal C is nested in AB interaction and finally, D is nested in C. I would like to model the following Y_ijklm=Mu+A_i+B_j+AB_ij+C_k(ij)+D_l(k(ij))+Error_m(...) I used the next command >summary(aov(abund~A*B + C % in % A:B + D % in % C % in % A:B ,datos)) Is it the correct

Dear all using the orthogonal polymial on a set of data, I get an approximate density which basically is in the form: exp(-polynomial), as you know, the parameters are the converged coeeficients. obviously, It is hard, if not impossible, to use the inverse CDF method to get a sample and then plot density. then how can I plot the approximated density in order to have a graphical comparision

Hello, How I can compute in R the orthogonal complement of one matrix? If A (n x m ) matrix of full column rank (n>m), its orthogonal complement is denoted by A_ . A_ is n X (n-m) matrix of full column rank and such that A'A_=0. I need to compute A_. How I can compute A_ in R? Best Regards, /Florin -- Florin G. Maican Ph.D. candidate, Department of Economics School of