search for: samplesizes

...error message:Error in res[distribution, test, samplesize] <- results : subscript out of boundsCan anyone tell me where I'm going wrong, please?!size.power.test<-function(){k<-1000distributions<-c("Normal","Uniform")tests<-c("t","Wilcoxon")samplesizes<-c(10,30,40)res<-array(0,c(length(distributions),length(tests),length(samplesizes)))dimnames(res)<-list(distributions,tests,samplesizes)for(distribution in distributions){for(test in tests){for(samplesize in samplesizes){for(i in 1:k){results<-size()res[distribution,test,samplesize]<...

...error message:Error in res[distribution, test, samplesize] <- results : subscript out of boundsCan anyone tell me where I'm going wrong, please?! size.power.test<-function(){k<-1000distributions<-c("Normal","Uniform")tests<-c("t","Wilcoxon")samplesizes<-c(10,30,40)res<-array(0,c(length(distributions),length(tests),length(samplesizes)))dimnames(res)<-list(distributions,tests,samplesizes)for(distribution in distributions){for(test in tests){for(samplesize in samplesizes){for(i in 1:k){results<-size()res[distribution,test,samplesize]<...

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

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

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 =

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

I am trying to use R to do loglogistic hazard estimation. My plan is to generate a loglogistic hazard sample data and then use survreg to estimate it. If everything is correct, survreg should return the parameters I have used to generate the sample data. I have written the following code to do a time invariant hazard estimation. The output of summary(modloglog) shows the factor loading of

I generate random vector from Weibull distribution sampWB <-urweibull(sampleSize, shape=shape.true, scale=scale.true, lb=0, ub=Inf) how can I create subvector containing 30% of samplesize of sampWB which should be assigned as Censored data? The probability for each value in sampWB can be uniform to be included in the subvector.

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

Hello, guys! I'm a novice at programming, learning about the speex and its applications now. Specifically, my current goal is to encode microphone input on iOS device in real-time, saving result to a file frame-by-frame. So far, I've managed to: 1) compile speex static library (it works, at least for iPhone/iPad, but not on iOS simulator; this is fine for now); 2) encode pcm data from

Hello, guys! I'm a novice at programming, learning about the speex and its applications now. Specifically, my current goal is to encode microphone input on iOS device in real-time, saving result to a file frame-by-frame. So far, I've managed to: 1) compile speex static library (it works, at least for iPhone/iPad, but not on iOS simulator; this is fine for now); 2) encode pcm data from

Hello, guys! I'm a novice at programming, learning about the speex and its applications now. Specifically, my current goal is to encode microphone input on iOS device in real-time, saving result to a file frame-by-frame. So far, I've managed to: 1) compile speex static library (it works, at least for iPhone/iPad, but not on iOS simulator; this is fine for now); 2) encode pcm data from

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'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, guys! I'm a novice at programming, learning about the speex and its applications now. Specifically, my current goal is to encode microphone input on iOS device in real-time, saving result to a file frame-by-frame. So far, I've managed to: 1) compile speex static library (it works, at least for iPhone/iPad, but not on iOS simulator; this is fine for now); 2) encode pcm data from

I was experiencing a strange pattern of slowdowns when using sample.int(), where sampling from a one population would sometimes take 1000x longer than taking the same number of samples from a slightly larger population. For my application, this resulted in a runtime of several hours rather than a few seconds. Looking into it, I saw that sample.int() is hardcoded to switch algorithms when the

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

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 +