similar to: How to conduct a hypothesis test : Ho:|E(X)|=|E(Y)|<->H1:otherwise NOT R question

Dear R-list, I apologize for my many emails but I think I know how to desctribe my problem differently and more clearly. My question is how to compare linear models with intercept and those without intercept using maximizing adjusted R^2 strategy. Now I do it like the following: > library(leaps) > n=20 > x=matrix(rnorm(n*3),ncol=3) > b=c(1,2,0) > intercept=1 >

Hi, everyone When I was using cv.lm(DAAG) , I found there might be something wrong with it. The problem is that we can't use it to deal with a linear model with more than one predictor variable. But the usage documentation hasn't informed us about this. You can find it by excuting the following code: xx=matrix(rnorm(20*3),ncol=3) bb=c(1,2,0) yy=xx%*%bb+rnorm(20,0,10)

Hi, everybody, 3 questions about R-square: ---------(1)----------- Does R2 always increase as variables are added? ---------(2)----------- Does R2 always greater than 1? ---------(3)----------- How is R2 in summary(lm(y~x-1))$r.squared calculated? It is different from (r.square=sum((y.hat-mean (y))^2)/sum((y-mean(y))^2)) I will illustrate these problems by the following codes:

Dear R-list, I'm not sure what I've found about a function in DAAG package is a bug. When I was using cv.lm(DAAG) , I found there might be something wrong with it. The problem is that we can't use it to deal with a linear model with more than one predictor variable. But the usage documentation hasn't informed us about this. The code illustrates my discovery: > library(DAAG)

Hi, all When I am using mle.cv(wle), I find a interesting problem: I can't do leave-one-out cross-validation with mle.cv(wle). I will illustrate the problem as following: > xx=matrix(rnorm(20*3),ncol=3) > bb=c(1,2,0) > yy=xx%*%bb+rnorm(20,0,0.001)+0 > summary(mle.cv(yy~xx,split=nrow(xx)-1,monte.carlo=2*nrow(xx),verbose=T), num.max=1)[[1]] mle.cv: dimension of the split subsample

Hi All, I am trying to reproduce this using R instead. [image: Full-size image (38 K)] I tried using the following code *SChla <- read.csv("SM_Chla_data.csv")* *Atlantis <- SChla[16:66,]* *head(Atlantis)* * * Seamount Station Depth Pico Nano Micro Total_Ch dbar Latitude Longitud 16 Atlantis 1217 Surface 0.0639 0.1560 0.0398 0.2597 2.082 -32.71450 57.29733

By accident, I left out the lines defining n1 and n2. Here it is as a function. Peter B. hotelling <- function(d1,d2){ k <- ncol(d1) n1 <- nrow(d1) n2 <- nrow(d2) xbar1 <- apply(d1,2,mean) xbar2 <- apply(d2,2,mean) dbar <- xbar2-xbar1 v <- ((n1-1)*var(d1)+(n2-1)*var(d2))/(n1+n2-2) t2 <- n1*n2*dbar%*%solve(v)%*%dbar/(n1+n2) f <-

This is a proposal for either adding a new, or updating an existing command line option such that a diagnostic can optionally be produced for unused -D macros. Long-lived large projects with thousands of files and many contributors have a tendency to accumulate build options over time. As time passes some build options like macros become replaced, obsolete, or simply no longer used. At the same

R-help, I have the following file I want to import to R (some lines removed) Calibrated CTD data for station:00280001 Calibrated:23/8 2001, Salinity Unsmoothed, Fluorescence Uncalibrated Maximum observed depth: 36 m QUAL has one digit for each of pressure, temp., sal. and fluor. QUAL=1:Uncal., QUAL=2:OK, QUAL=6:Interp., QUAL=9:No data

Dear All, I am running a simulation to obtain coverage probability of Wald type confidence intervals for my parameter d in a function of two parameters (mu,d). I am optimizing it using "optim" method "L-BFGS-B" to obtain MLE. As, I want to invert the Hessian matrix to get Standard errors of the two parameter estimates. However, my Hessian matrix at times becomes

?The idea itself is indeed good. Regarding to escaping: I think we should have it. Imagine the following example (I've took it from D73828). --- !ELF FileHeader: Class: ELFCLASS[[BITS]] Data: ELFDATA2LSB Type: ET_EXEC Machine: EM_386 # RUN: yaml2obj %s --docnum=4 -D BITS=32 -o %t-32bit.o # RUN: yaml2obj %s --docnum=4 -D BITS=64 -o %t-64bit.o Without escaping it would

Suppose I have the following hat matrix: H=X(X'X)^{-1}X' X is a n by p matrix, where n >= p and X_{i,1} = 1 I'm wondering why H1 = 1. (Here, 1 is column vector, whose each element is the number 1) Thank you!

Dear all, I've been delighted to just notice that Cohen's formulas for Effect Size 'w' and the associated power have been implemented in the 'pwr' package (thanks to St?phane Champely and others).. There is one aspect, though, that perplexes me. I'm doing some last minute post hoc analyses, meaning that my sample size (N=3404) has been long fixed, and I'm

Hi, I tried the steps explained in this link: http://wiki.rubyonrails.org/rails/pages/HowtoConnectToMicrosoftSQLServer The article t refers me to: src/lib/dbd_ado/ADO.rb But the file: dbi-0.1.0.tar.gz does not contain the src folder. Instead, it contains this file: \lib\dbd/ADO.rb Am I missing some steps, or this is a document inconsistency? Thank you, Alan -- Posted via

Unfortunately not all our documentation is up to date. We try to move more to our wiki and update it there but I'm not sure if there is winegcc stuff there already. You have roughly two options. The easiest one is to dynamically load the dll using LoadLibrary and load the functions you need using GetProcAddress. Another way is to create a dummy dll against which you can link. You need

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Hello, LLVMers! How can I force a front end to generate VAARG for accessing varargs parameters? I compile a simple C-code: #include <stdarg.h> int FnVarArgs(int a, ...) { int i,tmp=0; va_list ptArgument; va_start(ptArgument,a); for(i=0;i<9;i++) tmp+= va_arg(ptArgument,int); return tmp; } And then have this bytecode: ; ModuleID = 'main.bc' target

Hi All I am using RHEL , kernel 2.6.9-5, asterisk 1.2.4 , zaptel 1.2.3 installed , when I give modprobe for zaptel and ztdummy , I do not any error message my iax.conf contains the entry for trunking as [hoportal] type=friend host=192.168.20.32 secret=mysecret context=local trunk=yes my extensions.conf contains the entry for trunking as exten =>

Hi, maybe this has already been discussed already.. I _did_ search the archives, found no solution... I have a 4.1 channel track, I rip it to 5 separate mono wav files. Is it possible to encode these independant files to one single multi-channel flac file with flac? I heard a bit about ripping to wav, converting all mono tracks to flac separately (i had to use --channel-map=none to get this to

Due to missing data with some of my predictor variables I first do multiple imputation as follows: library(foreign) library(Amelia) library(norm) set.seed(666) M=10 impdat <-