similar to: how to remove time series trend in R?

Dear R-helpers, I am trying to construct a confidence interval on a prediction of a gam fit. I have the Wood (2006) book, and section 5.2.7 seems relevant but I am not able to apply that to this, different, problem. Any help is appreciated! Basically I have a function Y = f(X) for two different treatments A and B. I am interested in the treatment ratios : Y(treatment = B) / Y(treatment = A) as

Dear all, I have a performance problem in terms of computing time. I estimate mixed models on a fairly large number of subgroups (10000) using lme(.) within the by(.) function and it takes hours to do the calculation on a fast notebook under Windows. I suspect by(.) to be a poor implementation for doing individual analysis on subgroups. Is there an alternative and more efficient way for doing

I'm having no luck getting GARCH parameter estimations. It seems simple enough, but I don't know what I'm doing. I'm a newbie both at R and GARCH models, so whatever is going wrong, it's probably very basic. Here's what I do: 1. I first load the tseries package with: library("tseries") 2. I then load the data with: g <-

Full_Name: Ed Borasky Version: R-beta 2.1.0 2005-04-08 OS: Linux 2.6.11 GCC 3.3.5 Submission from: (NULL) (24.21.57.139) I downloaded the latest R-beta tarball and did a build with the default options. OS is Linux 2.6.11 and compiler is GCC 3.3.5. "make check-all" failed with the following message: make[3]: Entering directory `/home/znmeb/R-beta/tests' running code in

[this message needed manual improvement by the mailing list administrator since it was `HTMLified' .. ``please do not''] Apologies for bothering you about a fairly trivial matter. I have been getting some inconsistencies with the display digits in R V1.5. I have been using the hypergeometric distribution function, and have found that when printing out the results from this

k=lm(y~x) summary(k) returns R^2=0.9994 lm(y~x) is supposed to find coef. a anb b in y=a*x+b l=lm(y~x+0) summary(l) returns R^2=0.9998 lm(y~x+0) is supposed to find coef. a in y=a*x+b while setting b=0 The question is why do I get better R^2, when it should be otherwise? Im sorry to use the word "MS exel" here, but I verified it in exel and it gives: R^2=0.9994 when y=a*x+b is used

Hello, I've encountered a very weird issue with the method subset(), or maybe this is something I don't know about said method that when you're subsetting based on the columns of a data frame you can only use constants (0.1, 2.3, 2.2) instead of variables? Here's a look at my data frame called 'ea.cad.pwr': *>ea.ca.pwr[1:5,] MAF OR POWER 1 0.02 0.01 0.9999 2 0.02

Stock returns and other financial data have often found to be heavy-tailed. Even Cauchy distributions (without even a first absolute moment) have been entertained as models. Your qq function subtracts numbers on the scale of a normal (0,1) distribution from the input data. When the input data are scaled so that they are insignificant compared to 1, say, then you get essentially the

I'm attempting to sort a 3 dimensional array that looks like this > x , , 1 [,1] [,2] [1,] 9 9 [2,] 7 9 , , 2 [,1] [,2] [1,] 6 5 [2,] 4 6 , , 3 [,1] [,2] [1,] 2 1 [2,] 3 2 Such that it ends up like this .... > y , , 1 [,1] [,2] [1,] 2 1 [2,] 3 2 , , 2 [,1] [,2] [1,] 6 5 [2,] 4 6 , , 3 [,1] [,2]

Hi all, I am running R1.5.0 under Unix. I am repeating my earlier question with a few details added. I have the following tree fitted as the tree object 'my.tree': node), split, n, deviance, yval * denotes terminal node 1) root 5807 0.9998 0.0001722 2) V604 < 0.5 5798 0.0000 0.0000000 * 3) V604 > 0.5 9 0.8889 0.1111000 * And I have a data.frame called

Hi, does the shapiro wilk test in R-1.3.0 work correctly? Maybe it does, but can anybody tell me why the following sample doesn't give "W = 1" and "p-value = 1": R> x<-1:9/10;x [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 R> shapiro.test(qnorm(x)) Shapiro-Wilk normality test data: qnorm(x) W = 0.9925, p-value = 0.9986 I can't imagine a sample being