search for: 0.0256

Thanks. In this case below, what is "x"? I tried rownames(out) which did not work. Sorry. Does this sound like homework to you? On 7/12/2024 5:09 PM, Uwe Ligges wrote: > > > On 12.07.2024 10:54, Steven Yen wrote: >> Below is part a regression printout. How can I use "grep" to identify >> rows headed by variables (first column) with a certain label. In

Below is part a regression printout. How can I use "grep" to identify rows headed by variables (first column) with a certain label. In this case, I like to find variables containing "somewhath", "veryh",?"somewhatm", "verym", "somewhatc", "veryc","somewhatl", "veryl". The result should be an index 6:13 or

Hi, I have values on y axe from 0.0001 to 3.086. When I do plot I have writen values: 0.001, 0.050,1.000 ..., but how I can write on graph the minimum value and maximum value, with all decimals (I don't want to use the format 1e-0x)? I am using log scale. For example, if I have the values: 0.0001 0.0015 0.0256 0.0236 .... 0.0201 2.9668 3.0086 I need have each 'x' value put on y axe,

On 12.07.2024 10:54, Steven Yen wrote: > Below is part a regression printout. How can I use "grep" to identify > rows headed by variables (first column) with a certain label. In this > case, I like to find variables containing "somewhath", > "veryh",?"somewhatm", "verym", "somewhatc", "veryc","somewhatl",

Hello at all, working with a dataset I try to optimize a non-linear function with constraint. test<-read.csv2("C:/Users/Herb/Desktop/Opti/NORM.csv") fkt<- function(x){ a<-c(0) s<-c(0) #Minimizing square error for(j in 1:107){ s<-(test[j,2] - (x[1] * test[j,3]) - (x[2] * test[j,4]) - (x[3]*test[j,5]) - (x[4]*test[j,6]) - (x[5]*test[j,7]))^2 a<- a+s} a<-as.double(a)

Dear all, The standard call to ARIMA in the base package such as arima(y,c(5,0,0),include.mean=FALSE) gives a full 5th order lag polynomial model with for example coeffs Coefficients: ar1 ar2 ar3 ar4 ar5 0.4715 0.067 -0.1772 0.0256 -0.2550 s.e. 0.1421 0.158 0.1569 0.1602 0.1469 Is it possible (I doubt it but am

Could not get "which" to work, but my grep worked. Thanks. > which(grep("very|somewhat",names(goprobit.p$est))) Error in which(grep("very|somewhat", names(goprobit.p$est))) : argument to 'which' is not logical > grep("very|somewhat",names(goprobit.p$est)) [1] 6 7 8 9 10 11 12 13 28 29 30 31 32 33 34 35 50 51 52 53 54 55 56 57 On 7/12/2024

With the case-insensitive file system patch from http://llvm.org/bugs/show_bug.cgi?id=9656#c15 applied to dragonegg 2.9, the following Polyhedron 2005 benchmarks are seen on x86_64-apple-darwin10 under gcc 4.5.3svn using the dragonegg plugin... ================================================================================ Date & Time : 8 Apr 2011 19:52:56 Test Name :

Hi, I am new to model selection by coefficient shrinkage method such as lasso. And I became particularly interested in variable selection in Cox regression by lasso. I became aware of the coxpath() in R package glmpath does lasso on Cox model. I have tried the sample script on the help page of coxpath(), but I have difficult time understanding the output. Therefore, I would greatly appreciate if

Using caret on the Titanic data from Kaggle, I tried various models, including rfRules which produces a model, partly described as such: > caret.rfRules.cv$finalModel $model len freq err [1,] "2" "0.0368" "0" [2,] "2" "0.032" "0.05" [3,] "2"

Now I've found another way to make it work. All I need is to pick up the names in the column (x.1.age...). > v<-pr(goprobit.p); v Maximum-Likelihood Estimates weighted = FALSE iterations = 5 logLik = -14160.75 finalHessian = TRUE Covariance matrix is Robust Number of parameters = 66 Sample size = 17922 est se t p g sig x.1.age 0.0341 0.0138 2.4766 0.0133 -3.8835e-04 ** x.1.sleep

Yes. Any of the following worked. The pipe greater than (|>) is neat! Thanks. > v<-goprobit.p$est > names(v) |> grep("somewhat|very", x = _) ?[1]? 6? 7? 8? 9 10 11 12 13 28 29 30 31 32 33 34 35 50 51 52 53 54 55 56 57 > v |> names() |> grep("somewhat|very", x = _) ?[1]? 6? 7? 8? 9 10 11 12 13 28 29 30 31 32 33 34 35 50 51 52 53 54 55 56 57 >

A big compile time regression. Any ideas? Ciao, Duncan. On 22/07/11 19:13, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/253/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 253 0 2011-07-22 16:22:04

Greetings, We are performing a meta-analysis of mink pup survival data versus chemical concentration. We have modeled percent survival successfully using nls as shown below and the plot. What we need to do is construct a confidence interval on the concentration at which we get 50% survival (aka the EC50, although we may want other percent survivals in the future). My first question is, what seems

Hi Jack, thanks for the numbers. Any chance of analysing why gcc does better on those where it does much better than dragonegg? Ciao, Duncan. > With the case-insensitive file system patch from http://llvm.org/bugs/show_bug.cgi?id=9656#c15 > applied to dragonegg 2.9, the following Polyhedron 2005 benchmarks are seen on x86_64-apple-darwin10 > under gcc 4.5.3svn using the dragonegg

Thank you very much for your reply. Your code work well with this example. I modified a little to fit my real data, I got an error massage. Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) : Group length is 0 but data length > 0 On Thu, Jan 31, 2013 at 12:21 PM, arun kirshna [via R] < ml-node+s789695n4657196h87@n4.nabble.com> wrote: > Hi, > Try this: >

On Jul 24, 2011, at 3:02 AM, Duncan Sands wrote: > A big compile time regression. Any ideas? > > Ciao, Duncan. False alarm. For some reason that I have not yet been able to figure out, these tests run significantly more slowly when I run them during the daytime, which I did for that run. I checked a few of the worst regressions reported here and they all recovered in subsequent

I've searched the Web with Google and do not find what might cause this particular error from an invocation of cenboxplot: cenboxplot(cu.t$quant, cu.t$ceneq1, cu.t$era, range=1.5, main='Total Recoverable Copper', ylab='Concentration (mg/L)', xlab='Time Period') Error in if (n > 0) (1L:n - a)/(n + 1 - 2 * a) else numeric() : argument is of length zero I do

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

On 23.11.2012, at 15:12, john skaller <skaller at users.sourceforge.net> wrote: > > On 23/11/2012, at 5:46 PM, Sean Silva wrote: > >> Adding LLVMdev, since this is intimately related to the optimization passes. >> >>> I think this is roughly because some function level optimisations are >>> worse than O(N) in the number of instructions. >>