similar to: polynom Fit

Hi, I can not comprehend the linear fitting results of polynoms. For example, given the following data (representing y = x^2): > x <- 1:3 > y <- c(1, 4, 9) performing a linear fit > f <- lm(y ~ poly(x, 2)) gives weird coefficients: > coefficients(f) (Intercept) poly(x, 2)1 poly(x, 2)2 4.6666667 5.6568542 0.8164966 However the fitted() result makes sense: >

Dear list, I am getting weired with fitting data with a 1/x-polynomial. Suggest I have the following data: x <- c(1,2,3,4,5,6,7) y <- c(100,20,4,2,1,.3,.1) I may fit this with a linear model fit1 = lm(y ~ I(x)) Getting plot out of this model I applied library(polynom) pol1 = polynomial(fit1$coefficients) f1 = as.function(pol1) plot(x,y) lines(x, f1(x), col = 2) Clearly, this model

Hello, I have 3 columns : a, b and a*b I would like to find the pair (a,b) so that a*b is the minimum but not from the points I measured but from the fit of the curve (I have more points that the ones given below but I fit only on this part because I know that the minimum a*b is in this interval). I thought doing it this way : - to fit a*b=f(a) abfit<-lm(ab ~ poly(a,8,raw=T)) - to use the

When doing polynomial regression I believe it is a good idea to use the poly function to generate orthogonal polynomials. When doing this in Splus there is a handy function (transform.poly I think) to convert the coefficients produced by regression with the poly function back to the original scale. Has somebody written something similar for R ? Robert

Dear R people: I am trying to download the package polynom to R version 1.0.1.1. on Windows. Here are my commands and output: > install.packages("polynom",lib="c:\rw1011\library",CRAN="http://www.r-project.org/src/contrib/") Error in start[k]:(start[k + 1] - 1) : NA/NaN argument In addition: Warning message: Download had nonzero exit status in:

Dear R-users, is there any package that allows for a division of two polynomials? Regards, M. Fischer Dr. Matthias Fischer Friedrich-Alexander-Universit?t Erlangen-N?rnberg Lehrstuhl f?r Statistik und ?konometrie Lange Gasse 20 90403 N?rnberg Telefon: 0911 / 5302-271 Telefax: 0911 / 5302-277 E-Mail: Matthias.Fischer at wiso.uni-erlangen.de

I've packaged Bill's polynom add-on for R (i.e., converted man pages and took care of TITLE and INDEX). Apart from the missing poly() everything should be fine ... The package can be found in the CRAN src/contrib area. I've also added it to the Debian ix86 r-contrib package. -k =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- r-devel mailing list --

Merci beaucoup, Jean, for the bug report -- which I'm no "completeing" to R-bugs >>>>> "Jean" == Jean Coursol <coursol@cristal.math.u-psud.fr> >>>>> on Thu, 24 Jun 2004 15:22:37 +0200 (CEST) writes: Jean> I was exploring the polynom library with students: <and found a segmentation fault from parsing a long expression>

I'm one of the many who is far more interested in the reason for the request than in the numerical method to use. The way I see it, the methodology of, say, polyroot() suffices in a mathematical sense. If your coefficients are mathematically exact (as opposed to measured values), the near-singularities and such don't really matter, assuming infinite precision math. So, what

I am trying to plot the linear fit by id of the following data (test.l): id year incidents 100 1 0 101 1 1 102 1 21 103 1 27 104 1 3 105 1 12 100 2 5 101 2 5 102 2 19 103 2 10 104 2 2 105 2 12 100 3 0 101 3 0 102 3 22 103 3 14 104

As I understood it is 2nd case: > Or are there specified binaries where the libraries are builtin to, for examle: > --nonshared-binary=testparm, smbtorture, smbd and so on ? But in the case of binaries list, all binaries mentioned at the list will be linked statically with ALL libraries, not only 3 (in my case) . Am I right ? ??, 12 ????. 2019 ?. ? 20:19, Andrew Bartlett <abartlet at

i've seen that opening a socket on the asterisk server i can originate a call from one extension to another in a specific context. Is it possible to transfer an existing call from the extension ... SIP/xxx to another extension in a specific context? thanks

Dear All, I have a rather unusual problem. I have a set of data for a class in subsurface processes. From that dataset, I must calculate the slope of the best-fit line (which is the parameter of interest). The problem I have is twofold: 1) for the purposes of the exercise, I must force my best-fit line to go through the origin (0,0), and 2) the line must be linear, even though the data is

What function and package I use to conduct ordinal regression?? My data is composed 2colums and 180rows. The first colum indicate level of mass and second colum is intensity. So, I want to calculate how much intensity are related mass. [[alternative HTML version deleted]]

Should I specify _libraries_ what I want to build directly to binaries, for example: --nonshared-binary=replace,winbind-client,wbclient ? Or are there specified binaries where the libraries are builtin to, for examle: --nonshared-binary=testparm, smbtorture, smbd and so on ? ??, 12 ????. 2019 ?. ? 02:25, Andrew Bartlett <abartlet at samba.org>: > On Tue, 2019-11-12 at 02:05 +0300,

How do I add to a trellis plot the best fit line from a robust fit? I can use panel.lm to add a least squares fit, but there is no panel.rlm function. -- Alan S Barnett <asb at mail.nih.gov> NIMH/CBDB

I am trying to extract coefficients from lars fit and can't find how to get intercept. E.g. y = rnorm(10) x = matrix(runif(50),nrow=10) X = data.frame(y,x) fit1 = lars(as.matrix(X[,2:6]),as.matrix(X[,1])) fit2 = lm(y~.,data=X) Then, if I do: > predict(fit1,s=1,mode='fraction',type='coefficients')$coef X1 X2 X3 X4 X5 0.3447570

Dear all, I've replicated the cheese tasting example on p175 of GLM's by McCullagh and Nelder. This is a 4 treatment (rows) by 9 ordinal response (cols) table. Here's my simple code: #### cheese library(MASS) options(contrasts = c("contr.treatment", "contr.poly")) y = c(0,0, 1, 7, 8,8,19, 8,1, 6,9,12,11, 7,6, 1, 0,0, 1,1, 6, 8,23,7,

Dear R-helpers, I try to perform glm's with negative binomial distributed data. So I use the MASS library and the commands: model_1 = glm.nb(response ~ y1 + y2 + ...+ yi, data = data.frame) and predict(model_1, newdata = data.frame) So far, I think everything should be ok. But when I want to perform a glm with a subset of the data, I run into an error message as soon as I want to predict

Dear list members I have a doubt on how p-values for t-statistics are calculated in the summary of Linear Models. Here goes an example: x <- rnorm(100,50,10) y <- rnorm(100,0,5) fit1<-lm(y~x) summary(fit1) summary(fit1)$coef[2] # b summary(fit1)$coef[4] # Std. Error summary(fit1)$coef[6] # t-statistic summary(fit1)$coef[8] # Pr(>|t| summary(fit1)$df [2] # degrees of freedom #