similar to: Weibull- Random Censoring

sampleSize <- 20 shape.true <- 1.82 scale.true <- 987 sampWB <- rweibull(sampleSize, shape=shape.true, scale=scale.true) print(sampWB) censidx <- sample(1:length(sampWB), length(sampWB)*0.3) Censored.data <- sampWB[censidx] noncensidx <- defines the rest values of the vector which is not included at Censored.data? [[alternative HTML version deleted]]

Hi, I write below code for simulation for weibull- estimating parameters by weibullMLE function, Although I define metrix for the variables still I got this message: number of items to replace is not a multiple of replacement length Any suggestion > est=matrix (NA, 2,2) > se=matrix (NA, 2,2) > for ( p in 1:2) { + sampleSize <- 20 + shape.true <- 1.82 + scale.true <- 987 +

Hi, Below is the code I write, I am trying to create a metric of h and t while the values is out of function R. First I have message warning Second, the metric is not created > h <- seq(0.1, 0.9, by=0.1) > t <- seq(0,11000, by=100) > z <- cbind(t) > eta=10000 > beta=2 > R <- array (1:1100, dim= c(110,10)) > R= exp(-(z/eta*(1-h))^(beta*(1-h))) Warning messages: 1:

I made simulation with Weibull and create Matrix, How can I create mean/min/max/stdev on column or rows of matrix?, Thanks,

Hi, I' am doing a stats project using R to work out the size of a t-test and wilcoxon test depending on the distribution and sample size. I just can't get it to work - I want to put my results from the function size() into an array.At the moment I keep getting the error message:Error in res[distribution, test, samplesize] <- results : subscript out of boundsCan anyone tell me where

Hi, I'm trying to get better at things like lapply but it still stumps me. I have a function I've written, tested and debugged using individual calls to the function, ala: ResultList5 = DoAvgCalcs(IndexData, Lookback=5, SampleSize=TestSamples , Iterations=TestIterations ) ResultList8 = DoAvgCalcs(IndexData, Lookback=8, SampleSize=TestSamples , Iterations=TestIterations ) ResultList13

Dear list, I'm having trouble to see how my elements on a 4 dimensional array are listed. For example, I generated the following array: junk.melt=melt(occ.data,id.var=c("Especie", "Site", "Rep", "Año"), measure.var="Pres") y=cast(junk.melt, Site ~ Rep ~ Especie ~ Año) Now, I want to be able to look at how my species (Especie) are listed, in

Here is the function that makes the data.frames in the list: funweek <- function(df) if (length(df$elapsed_time) > 5) { res = fitdist(df$elapsed_time,"exp") year = df$sale_year[1] sample = df$sale_week[1] mid = df$m_id[1] estimate = res$estimate sd = res$sd samplesize = res$n loglik = res$loglik aic = res$aic bic = res$bic chisq =

Hi Hugo, I've been able to replicate your bug, including for other distributions (runif, rexp, rgamma, etc) which shouldn't be surprising since they're probably all drawing from the same pseudo-random number generator. ?Interestingly, it does not seem to depend on the choice of seed, I am not sure why that is the case. I'll point out first of all that the R-devel mailing list is

Hi Hugo, I've been able to replicate your bug, including for other distributions (runif, rexp, rgamma, etc) which shouldn't be surprising since they're probably all drawing from the same pseudo-random number generator. ?Interestingly, it does not seem to depend on the choice of seed, I am not sure why that is the case. I'll point out first of all that the R-devel mailing list is

Hi. Suppose I have a vector that I partition into disjoint, contiguous subvectors. For example, let v = c(1,4,2,6,7,5), partition it into three subvectors, v1 = v[1:3], v2 = v[4], v3 = v[5:6]. I want to find the maximum element of each subvector. In this example, max(v1) is 4, max(v2) is 6, max(v3) is 7. If I knew that the successive subvector maxima would never decrease, as in the example,

Dear colleague, I not sure this R code is correctly ? I would to show the number of Sample Size at Sample Size Axis that line draw from Power Axis (80%) from R code. How I show this and select the most appropriate of this power (.79955687 - 80983575). Thank for your help and answer. Best Regards, Nikom Thanomsieng, Email: nikom at kku.ac.th .... #Power analysis: Sample size for

Dear R helpers exposure <- data.frame(id = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20), ead = c(9483.686,50000,6843.4968,10509.37125,21297.8905,50000,706152.8354, 62670.5625, 687.801995,50641.4875,59227.125,43818.5778,52887.72534,601788.7937, 56813.14859,4012356.056,1419501.179,210853.4743,749961,6599.0862), pd =

On 05/10/2018, 09:45, "R-help on behalf of hmh" <r-help-bounces at r-project.org on behalf of hugomh at gmx.fr> wrote: Hi, Thanks William for this fast answer, and sorry for sending the 1st mail to r-help instead to r-devel. I noticed that bug while I was simulating many small random walks using c(0,cumsum(rnorm(10))). Then the negative

Dear R users, I am trying to simulate some multitrait-multimethod models using the packages sem and psych but whatever I do to deal with models which do not converge I always get stuck and get error messages such as these: "Error in summary.sem(M1) : coefficient covariances cannot be computed" "Error in solve.default(res$hessian) : System ist f?r den Rechner singul?r: reziproke

Hi. I have the following code in a loop. It splits a vector into subvectors of equal size. But if the size of the original vector is not an exact multiple of the desired subvector size, then the first few subvectors have one more element than the last few. I know that the cut function could be used to determine where to break up the vector, but it doesn't seem to provide control over

Hi all, Since two days I am trying to find a solution to be able to create beanplots for my data. When I call the beanplot function the following error appears: > beanplot(y1 ~ x1, log="", what=c(1,1,1,0), ylim=c(0,1)) > Error in bw.SJ(x, method = "dpi") : sample is too sparse to find TD What is really strange: I have 32 different vectors and the problem occurs for

Hi I'm conducing a meta-analysis using the meta package. Here's a bit of code that works fine - tmp <- metacont(samplesize.2, pctdropout.2, sddropout.2, samplesize.1, pctdropout.1, sddropout.1, data=Dataset, sm="WMD") I would now like to control for a couple of variables (continuous and categorical) that aren't in the equation. Is meta

Hello dear R-helpers, I haven't been able to figure out of find a solution in the R-help archives about how to delete all the characters contained in groups of parenthesis. I have a vector that looks more or less like this: myvector<-c("something (80 km/h, sd) & more (6 kg/L,sd)", "somethingelse (48 m/s, sd) & moretoo (50g/L , sd)") I want to extract all

Hi, I've got a list of ~20000 elements that look like this: [1] "A00096:A00096:A00096:A00096:A02178:A02178:A07776" [2] "A00046:A00076:A01101:A04146:A05671:A07169" [3] "A00038:A00932:A02185:A02370:A02818:A02818:A02818:A02818:A04732:A07142:A07142" [4] "A00096:A01352:A01352:A02023:A05001:A05001:A07776" [5]