similar to: nlme R vs S plus

Hi, I would like to know how (if) I can extract some of the information from the summary of my nlme. at present, I get a summary looking something like this: > summary(fit.nlme) Nonlinear mixed-effects model fit by maximum likelihood Model: MLKYLD ~ W4(DIM, logA, B, C) Data: ADHIS.x0 AIC BIC logLik 265314 265401.6 -132647 Random effects: Formula: list(logA ~ 1 , B ~

*Problem-1* CASE-I---------(works fine) > var1<-"tom" > var1 [1"tom" > var1<-as.character(var1) > var1 [1] "tom" > var2<-c("tom","harry","kate") > logc<-(var1 %in% var2) > logc [1] TRUE > typeof(var1) [1] "character" > typeof(var2) [1] "character"

I have found some "issues" (bugs?) with nls confidence intervals ... some with the relatively new "port" algorithm, others more general (but possibly in the "well, don't do that" category). I have corresponded some with Prof. Ripley about them, but I thought I would just report how far I've gotten in case anyone else has thoughts. (I'm finding the code

Hi I'm sorry about the triviality of my problem. I have a vector (v) of three columns (logA, logB, id). I want to compute (and plot) the correlation between logA and logB for different thresholds of id (e.g. >30, etc). So I tried: for(i in 1:100){ points(cor(v$logA[v$id>i], v$logB[v$id>i], use="complete.obs"), i)) } (i created a plot object already) but it comes with

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

Is there an R-language interface to the R API C-language functions Rf_logspace_add() and Rf_logspace_sub()? I don't see one but I may not looking under the right name. Various packages have functions which do that same sort of thing (log(exp(x)+exp(y)) and log(exp(x)-exp(y)) without unnecessary floating point errors). They have names like matrixStats::logSumExp(lx, na.rm=FALSE, ...)

Dear developers Neither Math.Rd nor Log.Rd mention the branch cuts that appear for complex arguments. I think it's important to include such information. Please find following two context diffs for Log.Rd and Math.Rd. [The pedants amongst us will observe that both sqrt() and log() have a branch point at complex infinity, which is not mentioned in the patch. Comments anyone?] rksh

Hi all, I have noticed a problem with the newly released version 2.1.1p4 (as well as with 2.1.1p3) : If sshd is run from inetd, all interactive sessions are automatically closed right after (successful) login. The problem disappears, if sshd is run from the command line (ie. no -i option) and did not exist in 2.1.1p2. This was noticed on a linux x86 box. I have appended a typescript of the

I've noticed that when using Lucene full text search, most queries use the indexes and/or header cache and are fast: . SEARCH BODY test . OK Search completed (0.001 secs). . SEARCH SUBJECT test . OK Search completed (0.053 secs). . SEARCH BODY test SUBJECT test . OK Search completed (0.002 secs). . SEARCH OR SUBJECT test FROM test . OK Search completed (0.093 secs). But an OR query that

Dear R users, according the help(log), the function log2(x) should give the natural logarithm of x. I expect in case of x=2 to to get 0.6931, however, R gives me 1 as a result. Similar, logb(2,2) gives 1 again. I'm wondering if I have missed something ? Yours Frank -- Frank Mattes, MD e-mail: f.mattes at ucl.ac.uk Department of Virology fax 0044(0)207 8302854 Royal Free Hospital

Survfit had a bug in some prior releases due to the use of both unique(times) and table(times); I fixed it by rounding to 15 digits per the manual page for as.character. Yes, I should ferret out all the usages instead, but this was fast and it cured the user's problem. The bug is back! A data set from a local colleage triggers it. I can send the rda file to anyone who wishes. The

Dear All, According to the Sturges rule, the number of classes of a histogram is the closest integer to 1 + logb(n,base=2) where n is the number of observations. The function hist(), by default, uses the Sturges rule. However, the code x <- 1:200 hist(x) produces a histogram with 10 classes and not 9 classes as determined by the Sturges rule. What am I missing? Thanks in advance, Paul

[ Not sure why, but the first time I sent this it never seemed to go through; apologies if you're seeing this twice ... ] I have some fully functional code that I'm guessing can be done better/quicker with some savvy R vector tricks; any help to make this run a bit faster would be greatly appreciated; I'm particularly stuck on how to calculate using "row-wise" vectors

What is wrong in the following nested if-else statements: if (Condition_1) { # begin IF_1 statement_1 statement_2 statement_3 if (Condition_2) { # begin IF_2 a<- a +1 } # end IF_2 statement_4 statement_5 statement_6 statement_7 if (Condition_3) {

Dear R-users, I am using R 2.13.0 and rms 3.3-0 , but can not reproduce figure 7.8 of the handouts *Regression Modeling Strategies* ( http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/course2.pdf) by the following code. Could any one help me figure out how to solve this? setwd('C:/Rharrell') require(rms) load('data/counties.sav') older <- counties$age6574 + counties$age75

I have a MLE task that for a small number of parameters finishes in a reasonable amount of time, but for my "real" case (with 17 parameters to be estimated) either takes far too long (over a day), or fails with "computationally singular" errors. So a) are there any parallel implementations of optim() (in R or otherwise) and b) how can I make my function more robust?

x <- rnorm(1e6); y <- log(x); # or logb(x) or log1p(x) w <- warnings(); print(object.size(w)); ## [1] 480 str(w); $ NaNs produced: language log(x) - attr(*, "dots")= list() - attr(*, "class")= chr "warnings" y <- log2(x); # or log10(x) w <- warnings(); print(object.size(w)); ## [1] 8000536 str(w); ## List of 1 ## $ NaNs produced: language

https://bugs.freedesktop.org/show_bug.cgi?id=109951 Bug ID: 109951 Summary: Death in Thrive (Game) Causes Hang Followed By Crash Product: Mesa Version: 18.3 Hardware: x86-64 (AMD64) OS: Linux (All) Status: NEW Severity: normal Priority: medium Component: Drivers/DRI/nouveau

Who prepares wrongly, when Dilbert consumes the root terminal inside the FBI? Go interface a connector! The UDPs, webmasters, and telephones are all disgusting and untamed. To be filthy or secure will save soft iterations to lazily fetch. Never close neatly while you're formating inside a robust procedure. Why will you transport the important huge texts before Greg does? Norm wants