similar to: How to compare linear models with intercept and those without intercept using minimizing adjs R^2 strategy

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:

Hallo, I just made a boxplot with the following command: boxplot(vsnrg$M~col(vsnrg$M)) When I now look at the plot. On the x-axis I just find numbers 1-6 because I have 6 datasets plotted. Now I want to add there the corresponding names which can be found in colnames(qrg). How can I realize this the online help does not help me. Thanks Corinna

Somehow, I've managed to have my .Rdata files become `disassociated' from the R program. I am running Windows XP Pro. I have re-installed R 2.4 in an attempt to have it re-associate itself with .Rdata files, but to no avail. .Rdata files are now associated with a file compression program or alternatively with Rgui.exe. .Rdata files are represented by the blue R, but double-clicking on

Has anyone developed a function to generate a forest plot, the one used a lot in meta-analysis? Joseph F. Lucke, PhD Biostatistician Center for Clinical Research and Evidence-based Medicine Department of Pediatrics School of Medicine University of Texas Health Science Center at Houston Voice: 713-500-5651 Email: Joseph.F.Lucke@uth.tmc.edu Postal Mail: PO Box 20708, Houston, TX 77225-0708

I'm working with genotype data in a frequency table: > a=matrix(1:16, nrow=4) > rownames(a)=c("AA","AT","TA","TT") > a [,1] [,2] [,3] [,4] AA 1 5 9 13 AT 2 6 10 14 TA 3 7 11 15 TT 4 8 12 16 'AT' and 'TA' are essentially the same, and I'd like to combine (add) the rows to reflect

I would like to explore dendrogam plots interactively. For example, click on a node and return information about all of the children of that node. Is there a high level wrapper for locator() or click() that will return the nearest dendrogram node on a plot? If not, is there a way to obtain the [x,y] coordinates of all the nodes on a plot? Thanks, David David J. States, M.D., Ph.D. Professor

Why? And how to solve it? The code and result are following, > data=rnorm(50) > > kde=density(data,n=20,from=-1,to=10) > > kde$x;kde$y [1] -1.0000000 -0.4210526 0.1578947 0.7368421 1.3157895 1.8947368 [7] 2.4736842 3.0526316 3.6315789 4.2105263 4.7894737 5.3684211 [13] 5.9473684 6.5263158 7.1052632 7.6842105 8.2631579 8.8421053 [19] 9.4210526 10.0000000 [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)

Dear R-list, I am sorry for my shortage of stat knowlege. I want know how to conduct a hypothesis test : Ho:|E(X)|=|E(Y)|<->H1:otherwise. Actually, in my study, X and Y is two observations of bias, where bias=u^hat-u, u is a parameter I concerned. Given X=(u^hat_xi - u) and Y=(u^hat_yi - u), I want to know which bias is smaller, or the absolute expection of which is smaller. Due to limit

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)

Dear R-Help, I am trying to compute a new variable, let's call it "target cannon orientation (tco)" based conditionally on old variables, "TargetColor," "CannonOriB," and "CannonOriR." For every case in the data set, if TargetColor is "B" then I want tco to equal the value for that case of CannonOirB, else CannonOriR. I've tried writing

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

The normality of the residuals is important in the inference procedures for the classical linear regression model, and normality is very important in correlation analysis (second moment)... Washington S. Silva > Thank you all for your replies.... they have been more useful... well > in my case I have chosen to do some parametric tests (more precisely > correlation and linear regressions

I know my subject line seems odd, but I want to replace an expression—such as a variable—with its value. For example, I want to paste() some strings together, assign the result to a variable s, then use the value of s as a variable to hold another value. Something like this: import_subjects <- function (start, iterations) { for (i in 1:iterations) { # iterate from start to end s <-

Hi Chris, Thanks. I will see what I can do for this. Junjie On Wed, May 19, 2010 at 3:45 PM, Chris Lattner <clattner at apple.com> wrote: > > On May 19, 2010, at 2:38 PM, Junjie Gu wrote: > >> Many compilers support per-loop pragma, such as loop unrolling (ie >> #pragma unroll=2).  Is there any LLVM project/effort going on >> in this area ? What is the expected

Hi, I want to make model selection with regsubsets. My code is: a<-regsubsets(Gesamt ~ CommunistSocialist + CountrySize + GNI + Lifeexp + Schoolyears + ExpMilitary + Mortality + PopPoverty + PopTotal + ExpEdu + ExpHealth, data=olympiadaten, nbest=2) summary(a) plot(a,scale="adjr2") (output attached) The problem is now, that I want to fit the best model again "manually"

I'd like to add a pragma support in llvm. I am thinking about using a llvm intrinsic to represent each pragma, such as llvm.pragma (metadata, ...) where metadata describes a pragma. So if an application has: #pragma p1 .. #pragma p2... for (...) The llvm IR would be llvm.pragma (metadata..) // for p1 llvm.pragma (metadata..) // for p2 llvm IR for "for

On Tue, Jun 1, 2010 at 5:42 PM, Junjie Gu <jgu222 at gmail.com> wrote: > Any suggestions/ideas ? IIUC, Chris suggested something like following ... header: br i1 %x, label %then, %label endif then: ... br i1 %y, label %loop_exit, label %header, !loop_pragma !1 endif: ... br i1 %z, label %loop_exit, label %header, !loop_pragma !2 loop_exit: ret i32 1 Where !1 and !2 are the

Hello, I am using the 'regsubsets' function (from leaps package) to get the best linear models to explain 1 variable from 1 to 5 explanatory variables (exhaustive search). Is there anyone who can tell me on which criterion is based the 'regsubsets' function ? Thank you. samuel Samuel BERTRAND Doctorant Laboratoire de Biomecanique LBM - ENSAM - CNRS UMR 8005

Many compilers support per-loop pragma, such as loop unrolling (ie #pragma unroll=2). Is there any LLVM project/effort going on in this area ? What is the expected way for implementing per-loop pragma, or general pragma ? Suggestions/comments ? Thanks Junjie