search for: samplesize

...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 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(distri...

...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 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(distr...

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 = DoAvgCalcs(IndexData, Lookback=13, SampleSize=TestSamples , Iterations=TestIterations ) ResultList21 = DoAvgCalcs(IndexData, Lookback=21, Sa...

...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$bic chisq = res$chisq chisqpvalue = res$chisqpvalue chisqdf = res$chisqdf if (!is.null(estimate) && !is.null(sd) && !is.null(loglik) && !is.null(aic) && !is.null(bic) && !...

...s? There might be more to be said, but I thought I'd just add what I could from playing around with it a little bit. For anyone who wishes to give it a try, I suggest this implementation of the autocorrelation tester which is about 80 times faster: DistributionAutocorrelation_new <- function(SampleSize)????{ ? ? Cor <- replicate(1e5, function() {X <- rnorm(SampleSize)? ? return(cor(X[-1], X[-length(X)]))})? ? return(Cor)} I have the same Stats package version installed. - (Thomas) William BellHons BSc Candidate (Biology and Mathematics)BA Candidate (Philosophy)McMaster University # Hi,#?#?#...

...s? There might be more to be said, but I thought I'd just add what I could from playing around with it a little bit. For anyone who wishes to give it a try, I suggest this implementation of the autocorrelation tester which is about 80 times faster: DistributionAutocorrelation_new <- function(SampleSize)????{ ? ? Cor <- replicate(1e5, function() {X <- rnorm(SampleSize)? ? return(cor(X[-1], X[-length(X)]))})? ? return(Cor)} I have the same Stats package version installed. - (Thomas) William BellHons BSc Candidate (Biology and Mathematics)BA Candidate (Philosophy)McMaster University # Hi,#?#?#...

...0.45,0.45,0.45,0.45)) param <- data.frame(alpha = 0.99, size = 50)    # size is basically no of simulations n          <- length(exposure$id) id         <- exposure$id ead        <- exposure$ead lgd        <- exposure$lgd pd         <- exposure$pd alpha      <- param$alpha samplesize <- param$size ## generate random numbers s.t. 1 = Default, 0 = no-default. L <- matrix(data=NA, nrow=n, ncol=samplesize, byrow=TRUE) for(i in 1:n)     L[i,] <- rbinom(n=samplesize, size=1, prob=exposure$pd[i]) # ________________________________________________________________ # compu...

...,correct=F)$statistic Ntotal<-sum(data1) df<- ncol(nc-1)*nrow(nr-1) ifelse(df==1,w<- sqrt(chi2/Ntotal), w<-sqrt(chi2/(chi2+Ntotal))) Ntotal1<-900 #change this if power not enough alpha <-0.05 #change this for One tailed =0.05 ncp<-0 chicrit<-NULL power<-NULL n<-NULL samplesize<-NULL for (i in 1:Ntotal1){ ncp[i] <- w^2 * i chicrit<-qchisq(1-alpha,df) power[i] <- 1-(pchisq(chicrit , df, ncp[i])) n[i]<-i samplesize<-cbind(n, ncp,power) } samplesize plot(n,power,type="l",col="red", lwd=1, panel.first = grid(10,10), main=&q...

...beta of loglogistic regression. Any comments? Thanks a lot, Jiakai Chen Code: ============BEGIN============== # A time invariant loglogistic estimation library(survival) # Clear the workspace rm(list = ls(all = TRUE)) # Totally 100K samples which may die during 100 periods timeline = 100 samplesize = 100000 dfico <- rnorm(samplesize, mean = 0, sd = 25) dficoeff <- 0.005 # Baseline loglogistic hazard function stored in bhaz a<- 20 b <- 4 time <- 1:timeline bhaz <- ((b/a) * (time /a) ^ (b-1))/(1+(time/a)^b) # Event time stored in endtime. Baseline hazard function is contr...

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.

...lt;- c(0.3, 0.5, 0.7) # loads of observed variables on trait factors traitCorrs <- c(0.0, 0.4, 0.7) # correlations between traits methodLoads <- c(0.2, 0.3, 0.4) # loads of observed variables on method factors methdCorrs <- c(0.0, 0.2, 0.4) # correlations between methods SampleSize <- 500 # Sample size myMaxIter <- 500 # Maximal number of interactions in every model estimation nCond <- length(traitLoads)* length(traitCorrs)* length(methodLoads)* length(methdCorrs) myRes <- as.numeric(gl(nCond, 1, myRep*nCond)) myloadT...

...mChannelsPerFrame: 1 mBitsPerChannel: 16 } ... } ... } sbufToTrackReadiness = 0x0 numSamples = 375 sampleTimingArray[1] = { {PTS = {82626949766094/1000000000 = 82626.950, rounded}, DTS = {INVALID}, duration = {1/44100 = 0.000}}, } sampleSizeArray[1] = { sampleSize = 2, } dataBuffer = 0x6e7a40 b) Actual data which is kept in aforementioned buffer (for the sake of completeness): 0f001400 0b000d00 04000a00 f7ff1300 0a000300 f9ff1c00 1b001c00 f9ffc0ff bfffadff b7ff86ff 7cff5fff 63ff67ff 84ffb5ff bbffd4ff e4ff1500 1c00100...

...hannelsPerFrame: 1 mBitsPerChannel: 16 } ... } ... } sbufToTrackReadiness = 0x0 numSamples = 375 sampleTimingArray[1] = { {PTS = {82626949766094/1000000000 = 82626.950, rounded}, DTS = {INVALID}, duration = {1/44100 = 0.000}}, } sampleSizeArray[1] = { sampleSize = 2, } dataBuffer = 0x6e7a40 b) Actual data which is kept in aforementioned buffer (for the sake of completeness): 0f001400 0b000d00 04000a00 f7ff1300 0a000300 f9ff1c00 1b001c00 f9ffc0ff bfffadff b7ff86ff 7cff5fff 63ff67ff 84ffb5ff bbffd4ff e4ff1500 1c00100...

...mChannelsPerFrame: 1 mBitsPerChannel: 16 } ... } ... } sbufToTrackReadiness = 0x0 numSamples = 375 sampleTimingArray[1] = { {PTS = {82626949766094/1000000000 = 82626.950, rounded}, DTS = {INVALID}, duration = {1/44100 = 0.000}}, } sampleSizeArray[1] = { sampleSize = 2, } dataBuffer = 0x6e7a40 b) Actual data which is kept in aforementioned buffer (for the sake of completeness): 0f001400 0b000d00 04000a00 f7ff1300 0a000300 f9ff1c00 1b001c00 f9ffc0ff bfffadff b7ff86ff 7cff5fff 63ff67ff 84ffb5ff bbffd4ff e4ff1500 1c00100...

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 no...

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 inappropriate for these purposes? If so, based on...

...hannelsPerFrame: 1 mBitsPerChannel: 16 } ... } ... } sbufToTrackReadiness = 0x0 numSamples = 375 sampleTimingArray[1] = { {PTS = {82626949766094/1000000000 = 82626.950, rounded}, DTS = {INVALID}, duration = {1/44100 = 0.000}}, } sampleSizeArray[1] = { sampleSize = 2, } dataBuffer = 0x6e7a40 b) Actual data which is kept in aforementioned buffer (for the sake of completeness): 0f001400 0b000d00 04000a00 f7ff1300 0a000300 f9ff1c00 1b001c00 f9ffc0ff bfffadff b7ff86ff 7cff5fff 63ff67ff 84ffb5ff bbffd4ff e4ff1500 1c00100...

...t together some sample code to show the difference in timing letting sample.int() choose the cutoff point versus manually specifying the use of do_sample2(): ### compare times for sample.int() vs internal function sample2() compareSampleTimes = function(popSizeList=c(1e5, 1e6, 1e7, 1e8, 1e9), sampleSizeList=c(10, 100, 1000, 10000), numReplications=1000) { for (sampleSize in sampleSizeList) { for (popSize in popSizeList) { elapsed1 = system.time(replicate(numReplications, sample.int(popSize, sampleSize)))[["elapsed"]] elapsed2 = system.time(replica...

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

...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 + sampWB <- urweibull(sampleSize, shape=shape.true, scale=scale.true, lb=0, ub=Inf) + sampWBmleWB <- weibullMLE(sampWB) + est[p]= sampWBmleWB$estimate + se [p]= sampWBmleWB$s + } Warning messages: 1: In est[p] = sampWBmleWB$estimate :...