similar to: Empirical cumulative distribution with censored data

Dear all, I have a problem installing a package required by Hmisc on MacOS 10.3.5 with R 2.00. g77 -fno-common -g -O2 -c avas.f -o avas.o g77 -fno-common -g -O2 -c rlsmo.f -o rlsmo.o gcc -bundle -flat_namespace -undefined suppress -L/usr/local/lib -o acepack.so ace.o avas.o rlsmo.o -L/usr/local/lib -L/usr/local/lib/gcc/powerpc-apple-darwin6.8/3.4.2

Dear all, I am conducting a full factorial analysis. I have one factor consisting in algorithms, which I consider my treatments, and another factor made of the problems I want to solve. For each problem I obtain a response variable which is stochastic. I replicate the measure of this response value 10 times. When I apply ANOVA the assumptions do not hold, hence I must rely on non parametric

Hi, all. I'd like to know if exists a manner to get the first index where a condition is attained in a vector. For example, There is a better solution than first.index <- table(subject[corretor==27])[1] (give me the subject for the first time that corretor is 27)? Thanks, ======================================== Cezar Freitas (ICQ 109128967) IMECC - UNICAMP Campinas, SP - Brasil

Hi all, What kind of input loop is expected for the loop-access pass to run smoothly? Also, what passes (like mem2reg) should be run to ensure the desired form is attained. Is running mem2reg, indvars and loop-simplify enough for that? Any help will be appreciated. Thanks and regards, Shraiysh -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hi, I am new-comer to statistics and R-Project. I would like to know if these features can be attained in R-Project.Please help. 1) beta 1 and Beta 2, or gamma one and gamma two for skewness and kurtosis, respectively, including standard errors and tests for significance (relative to values for a Gaussian distribution). 2) linear correlation 3) quadratic regression 4) polynomial regression

Hi Guy's I was wondering if someone could point me in the right direction. dbinom(10,1,0.25) I am using dbinom(10,1,0.25) to calculate the probabilty of 10 judges choosing a certain brand x times. I was wondering how I would go about simulating 1000 trials of each x value ? regards Brendan -- View this message in context:

The Loop Optimization Working Group had a survey about “canonical” loop required by each loop transformation or analysis. That is, what should we normalize a loop before running a certain loop pass. Here is the survey result: https://tinyurl.com/rhuzny2 (Note that there are two workspaces: “By precondition” and “Breakdown”) Best - Min > On Dec 22, 2019, at 2:40 AM, Shraiysh Vaishay via

Dear alltogether, how can a delay like possible with par(ask=TRUE) be attained while using trellis-plots within a loop or something like that? the following draws each plot without waiting for a signal (mouse-klick), so par() does not work for that: library(nlme) for(i in 1:3) { fitlme <- lme(Orthodont) par(ask=TRUE) # does not work with trellis.... print(

Hello guys. I just ran the updated tests for PL2 and they are not giving the mset order I expect.Now,the thing is, dfr's behavior is a bit hard to predict and so even if I expect a particular order ,it may give another order and still be correct.So,the only way to write correct tests for PL2 is to manually calculate the weight of the documents to decide the expected order.For that,I need to

Op do 3 nov. 2022 om 19:39 schreef Federico Miyara <fmiyara at fceia.unr.edu.ar>: > > > Martijn, > > Currently FLAC already stores and restores most kinds of metadata corruption without problems, so in most cases the conversion is already bit-accurate. However, there are some kinds of corruption it cannot handle. These are the kinds of corruption that invalidate your

Martijn, > Currently FLAC already stores and restores most kinds of metadata > corruption without problems, so in most cases the conversion is > already bit-accurate. However, there are some kinds of corruption it > cannot handle. These are the kinds of corruption that invalidate your > considerations. For example, when a chunk length is incorrect, the > location and length

Roger Marquis wrote: > [snip] > >It takes all of 2 seconds to generate a ssh 2 new session on a >500Mhz cpu (causing less than 20% utilization). Considering that >99% of even the most heavily loaded servers have more than enough >cpu for this task I don't really see it as an issue. > >Also, by generating a different key for each session you get better >entropy,

I have been trying to download the climstats package: https://r-forge.r-project.org/R/?group_id=861 but it doesn't seem to run on R 3.0.2 or 3.0.1 and the zipfile is empty. Does anyone know the status of this package or where I can download it. Thanks ****************** Jenny Williams Spatial Information Scientist, GIS Unit Herbarium, Library, Art & Archives Directorate Royal Botanic

Hi, I have just started using R. Now I have the following problem: I want to create an Empirical Cumulative Distribution Function and I only came so far: F10 <- ecdf(x) plot(F10, verticals= TRUE, do.p = TRUE, lwd=3) x=c(1.6,1.8,2.4,2.7,2.9,3.3,3.4,3.4,4,5.2) Now I'd like to use arguments such as xlabs and main but I don't know how to integrate them. I hope someone can help me, I am

Hi, all ecdf function (Empirical Cumulative Distribution Function) in "stats" package counts from smaller values to larger values. However, I want to draw it by counting from larger value to smaller values and I couldn't find options for this purpose. How can I draw ecdf or ecdf like graph by counting from larger values to smaller values. Thank you in advance. Hyunchul Kim

Sorry for not responding earlier. On 05/20/2016 03:05 PM, Roman Gareev wrote: > Thank you very much for the advices! I could probably try to avoid > using of nonhardware prefetching in the project, if Tobias doesn’t > disagree with it. My understanding is that prefetching isn’t used > explicitly in [1] and, according to [2], in some cases 90% of the > turbo boost peak of the

Dear R Users, Does R have an "inverse empirical cumulative distribution" function, something one can use to invert ecdf ? Thanks in advance, Tolga Generally, this communication is for informational purposes only and it is not intended as an offer or solicitation for the purchase or sale of any financial instrument or as an official confirmation of any transaction. In the event you

2016-05-19 21:45 GMT+05:00 4lbert C0hen <4lbert.h.c0hen at gmail.com>: > One short note. I would advise against spending time on prefetching for x86. > Recent hardware prefetchers are amazingly good at strided accesses in > single-threaded code. Caution: this is not based on objective/published > data, but on personal experience. > > There are open challenges in

Full_Name: Jens Lund Version: 1.3.1 OS: Windows Submission from: (NULL) (194.239.194.210) When we subset a dataset bartlett.test does not calculate the df correctly: X <- rnorm(50) # Generate simulated samples f <- gl(10,5) # Generate a factor with 10 levels and # 5 replications of each level # Works correctly bartlett.test(X,f) # Correct number of df = 9 #

Dear R users, i try to calculate the probabilty to survive a given time by using the estimated survival curve by kaplan meier. What is the right way to do that? as far as is see i cannot use the predict-methods from the survival package? library(survival) set.seed(1) time <- cumsum(rexp(1000)/10) status <- rbinom(1000, 1, 0.5) ## kaplan meier estimates fit <- survfit(Surv(time,