similar to: Bug in plot.lm (PR#6640)

I propose the following enhancements and changes to plot.lm(), the most important of which is the addition of a Residuals vs Leverage plot. (1) A residual versus leverage plot has been added, available by specifying which = 5, and not included as one of the default plots. Contours of Cook's distance are included, by default at values of 0.5 and 1.0. The labeled points, if any, are those

Dear All, I would like to plot text with a box around it. I used strwidth and strheight to compute the size of the box which is plotted with rect: z <- rnorm(10) # horizontal text works plot(rnorm(10)) x1 <- 5 y1 <- 0 label <- "Label" cha <- paste(" ", label, " ", sep = "") xh <- strwidth(cha, cex = par("cex")) yh <-

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I've been looking fairly carefully at the Q-Q plots produced by plot.lm() and am having difficulty understanding why plot.lm() is doing what it's doing, specifically scaling the standardized residuals by the prior weights. Can anyone explain this to me ... ? Multiplying by the weights seems to give the wrong plot, at least for binomial

Jean Marc & list, Following up on this- there seems to be an issue with this calculation in mdf.c (Smooth echo energy estimate over time) Pey += Eh*Yh; Pyy += Yh*Yh; it goes too large for floating point, which seems to be caused by extremely large values in st->Rf[j], st->Yf[j] and smaller values in st->Eh[j], st->Yh[j]- things were relatively quiet

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@biostat.ku.dk # ###################################################### The error occurs following the third Hit <Return> below: > y_rnorm(40) >

Hi, folks, As I understand, Least-squares Estimate (second-moment assumption) and the Method of Maximum Likelihood (full distribtuion assumption) are used for linear regression. I do >?lm, but the help file does not tell me the model employed in R. But in the book 'Introductory Statistics with R', it indicates R estimate the parameters using the method of Least-squares. However it

It seems that Tablegen is generating intrinsic ID's off by in DAGISel.inc In DAGISel.inc, I have the following pattern: int64_t CN1 = Tmp0->getZExtValue(); // Pattern: (intrinsic_w_chain:f32 103:iPTR, GPRF32:f32:$src0, GPRF32:f32:$src1, GPRF32:f32:$src2) // Emits: (MACRO_FMA_f32:f32 GPRF32:f32:$src0, GPRF32:f32:$src1, GPRF32:f32:$src2) // Pattern complexity = 8 cost

Hi R, I want to conduct iid tests. So, I went to download 'iid.test' package. And the dependent package is 'ncdf', which is not available. So, how do I conduct the IID tests? Can I do these iid tests in Base R? Thanks in advance, Shubha This e-mail may contain confidential and/or privileged i...{{dropped:12}}

Are you using isTarget = 1 in your intrinsics file? On Apr 14, 2009, at 6:34 PM, Villmow, Micah wrote: > It seems that Tablegen is generating intrinsic ID’s off by in > DAGISel.inc > > In DAGISel.inc, I have the following pattern: > int64_t CN1 = Tmp0->getZExtValue(); > > // Pattern: (intrinsic_w_chain:f32 103:iPTR, GPRF32:f32:$src0, > GPRF32:f32:$src1,

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

Hi I have the result of a linear model "lm98" and I want to plot it to analyse the regression but I'm getting the folowing error message > lm98<-lm(log(U+1)~Bq+Gi+Lv+Tb+Bq:Gi+Bq:Lv+Bq:Tb+Gi:Lv+Gi:Tb+Lv:Bq) > plot(lm98) Error in plot.window(xlim, ylim, log, asp, ...) : need finite ylim values In addition: Warning message: NaNs produced in: sqrt(1 - hii) The

I append below a suggested update for p.adjust(). 1. A new method "yh" for control of FDR is included which is valid for any dependency structure. Reference is Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188. 2. I've re-named the "fdr" method to "bh" but

I downloaded the iid.test, but I can't run it. I get the following message: Error: could not find function "iid.test" Where am I supposed to save this package in order that it works? Thanks, EZ [[alternative HTML version deleted]]

Hi, Is it possible to program ssh tunneling in C or C++ rather than to use shell command? Are there any APIs for ssh tunneling available? I was told by friends that the openssh is working on it, please point me which version of openssh support API ssh tunneling. Thank you. Kind regards, J. yh

Hi: I think I found an error in plot.lm with the option which=5, of course I can be wrong , as usually happen, but I had work on it for a while and show it to some other people that work with R, and so far I don't see what I can be interpreting wrong. I also worked over the plot.lm's code and change some lines to get what I call "the right plot", if any body is

Dear R-Users, I can't understand the behaviour of quasibinomial in lmer. It doesn't appear to be calculating a scaling parameter, and looks to be reducing the standard errors of fixed effects estimates when overdispersion is present (and when it is not present also)! A simple demo of what I'm seeing is given below. Comments appreciated? Thanks, Russell Millar Dept of Stat U.

Hii, I will save the elements of the vector median<-with(x, tapply(V3, grup, median)). The output of this vector is: 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400 425 450 475 500 17.8 17.8 17.5 17.8 17.7 17.6 17.7 17.6 17.8 17.7 17.6 17.7 17.8 17.7 17.8 17.8 17.8 17.8 17.7 17.7 Can anybody help me how to do it. I will save it to a text file...

Hello, I am working from a linux 64 machine on a server with R-2.12 (I can't update to 2.13). I am iterating through many linear mixed models for longitudinal data and I occasionally receive the following convergence error: > BI.lme <- lme(cd4 ~ time + genBI + genBI:time + C1 + C2 + C11 + C12, random =~ 1 + time | IID, data = d) Error in lme.formula(cd4 ~ time + genBI + genBI:time +

Dear R-users, One of the main reasons I moved from GAUSS to R (as an econometrician) was because of the existence of the library LOCFIT for local polynomial regression. While doing some checking between my former `GAUSS code' and my new `R code', I came to realize LOCFIT is not quite doing what I want. I wrote the following example script: