similar to: Beginning lm

Hey all, i used http://bits.xensource.com/oss-xen/release/3.4.2/linux-2.6.18-xen-3.4.2.tar.gz to try and compile my Xen kernel on debian 5.0.2 everything went well when i ussed make, make modules, make modules_install, and make install. The following files were created in the boot directory. vmlinuz-2.6.18.8-xen config-2.6.18.8-xen System.map-2.6.18.8-xen however in the arach/x86_64 there is

Dear R experts, Excuse me if my question will be stupid... I'd like to fit data with x^2 polynomial: d <- read.table(file = "Oleg.dat", head = TRUE) d X T 3720.00 4.113 3715.00 4.123 3710.00 4.132 ... out <- lm(T ~ poly(X, 4), data = d) out Call: lm(formula = T ~ poly(X, 2), data = d) Coefficients: (Intercept) poly(X, 2)1 poly(X, 2)2

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Dear all, I am using poly() in lm() in the following form. 1> DelsDPWOS.lm3 <- lm(DelsPDWOS[,1] ~ poly(DelsPDWOS[,4],3)) 2> DelsDPWOS.I.lm3 <- lm(DelsPDWOS[,1] ~ poly(I(DelsPDWOS[,4]),3)) 3> DelsDPWOS.2.lm3 <- lm(DelsPDWOS[,1]~DelsPDWOS[,4]+I(DelsPDWOS[,4]^2)+I(DelsPDWOS[,4]^3)) 1 and 2 lead to identical but wrong results. 3 is correct. Surprisingly (to me) the residuals

Hi all, I want to fit data called "metal" with a polynominal function as dP ~ a.0 + a.1 * U0 + a.2 * U0^2 + a.3 * U0^3 + a.4 * U0^4 The data set includes, the independant variable U0 and the dependant variable dP. I've seen that the combination of lm() and poly() can do that instead of using the nls() function. But I don't get how to interpret the results from the linear

Hi, I would like to fit my data with a 4th order polynomial. Now I have only 5 data point, I should have a polynomial that exactly pass the five point Then I would like to compute the "fitted" or "predict" value with a relatively large x dataset. How can I do it? BTW, I thought the model "prodfn" should pass by (0,0), but I just wonder why the const is

Hi, I wonder if someone has already figured out a way of making summary(mylm) # where mylm is an object of the class lm() to print the "(Intercept)" at the last line, rather than the first line of the output. I don't know about, say, biostatistics, but in economics the intercept is usually the least interesting of the parameters of a regression model. That's why, say, Stata

I noticed that the text() command adds text to a plot. Is there a way to either make the plot blank or add text to a "blank sheet". I would like to "plot" a page that contains just text, no plot lines, labels, etc. Suggestions? Kevin [[alternative HTML version deleted]]

Given a Date object or simply a year is there an R function to tell me if the it is a leap year or not? I was hoping for something like 'is.leapyear'. I probably can build my own function (year divisible by 4 etc.) but I would rather use an existing function if it is available. Thank you. Kevin

Why does: (shape/scale) * (1:365/scale)^(shape - 1) return a vector of numbers but calling a function hasard(1:365,shape,scale) defined like: hazard <- function(x,shape,scale) { return (shape/scale) * (x/scale)^(shape - 1) } Only return a single value? It is like x becomes a single value passed as an argument. Thank you. Kevin

Would someone be so kind as to explain in English what the ANOVA code (anova.lm) is doing? I am having a hard time reconciling what the text books have as a brute force regression and the formula algorithm in 'R'. Specifically I see: p <- object$rank if (p > 0L) { p1 <- 1L:p comp <- object$effects[p1] asgn <-

Dear colleagues: I've been storing up this question for a long time and apologize for the length and verbosity of it. I am having trouble in consulting with graduate students on their research projects. They are using surveys to investigate the sources of voter behavior or attitudes. They have predictors that are factors, some ordered, but I am never confident in telling them what

Hi all! I'm fairly new to R and not too experienced with regression. Because of one or both of those traits, I'm not seeing why some terms are being dropped from my model when doing a regression using lm(). I am trying to do a regression on some experimental data d, which has two numeric predictors, p1 and p2, and one numeric response, r. The aim is to compare polynomial models in p1

Hi all, We know that Hermite polynomial is for Gaussian, Laguerre polynomial for Exponential distribution, Legendre polynomial for uniform distribution, Jacobi polynomial for Beta distribution. Does anyone know which kind of polynomial deals with the log-normal, Student抯 t, Inverse gamma and Fisher抯 F distribution? Thank you in advance! David [[alternative HTML version deleted]]

I noticed that when I fit a linear model using 'lm' there is an attribute called "factors" that is added to the "term". It doesn't seem to appear for 'model.matrix', just 'lm'. I have been unable to find where it gets constructed or what it means? It looks like a two dimensional array that I may be able to use so I would just like to get some

https://bugs.freedesktop.org/show_bug.cgi?id=42479 Bug #: 42479 Summary: piglit/glean/readPixSanity fails - dmesg PGRAPH - TRAP_TPDMA - TP0: Unhandled ustatus 0x00020000 Classification: Unclassified Product: xorg Version: unspecified Platform: x86 (IA32) OS/Version: Linux (All) Status: NEW

Full_Name: Jens Keienburg Version: 2.3.0 OS: Windows XP Submission from: (NULL) (193.174.53.122) I used the function lm() to calculate the coefficients of a polynome. If I used the function poly(t,2) to denote a polynome of form 1 + x + x^2, the coefficients are wrong. I appended an excerpt below: > t=1:100 > p=-20 - 10 * t + 2 * t^2 > p [1] -28 -32 -32 -28 -20 -8 8

I had a question about the basic power functions in R. For example from the R console I enter: -1 ^ 2 [1] -1 but also -1^3 [1] -1 -0.1^2 [1] -0.01 Normally pow(-1, 2) return either -Infinity or NaN. Has R taken over the math functions? If so I would think that -1^2 is 1 not -1 and -0.1^2 is 0.01 not -0.01. Thank you. Kevin

R users, I was a bit surprised to find that when I attempted to add a polynomial term to a linear model using either lm or glm as could be done in S resulted in a fit without that term included and without warning(!!), e.g. > lm(response ~ x + x^2, data). As far as I can gather, there is no poly() yet in R, and if lm/glm do not allow functions of variables as their formula arguements, is

Hi All, I need to evaluate a series expansion using Legendre polynomials. Using the 'orthopolinom' package I can get a list of the first n Legendre polynomials as character strings. > library(orthopolynom) > l<-legendre.polynomials(4) > l [[1]] 1 [[2]] x [[3]] -0.5 + 1.5*x^2 [[4]] -1.5*x + 2.5*x^3 [[5]] 0.375 - 3.75*x^2 + 4.375*x^4 But I can't figure out how to