similar to: bootstrap for confidence intervals of the mean

Dear useRs, I wrote a function that simulates a stochastic model in discrete time. The problem is that the stochastic parameters should not be negative and sometimes they happen to be. How can I conditionate it to when it draws a negative number, it transforms into zero in that time step? Here is the function: stochastic_prost <- function(Fmean, Fsd, Smean, Ssd, f, s, n, time, out=FALSE,

Hello, I want to find the parameters mu and sigma that minimize the following function. It's important, that mu and sigma are strictly positive. ----------------- optimiere = function(fmean,smean,d,x,mu,sigma) { merk = c() for (i in 1:length(d)) merk=c(merk,1/(d[i]^2)*(d[i]-1/(fmean*(1-plnorm(x[i],mu,sigma))))^2) return(sum(merk)) } ----------------- To do that I'm using the nlm

Hi all, I am trying to run some plots on data, but when loading he CSV data file R is stopping and I am getting an out of memory error. Anyway to tweak this somehow to get it to run? Using WinXP with 4 GB RAM Tnx Bruce

Hi all, I would like to set up an iterative read & write sequence to avoid reading and writing each file one at a time. Hundreds of data sets to re-calculate.? The code I have works well individually, but would like to set up an iterative read, calculate and write changing the input and output file names each iteration. I? think I have read that there is an R? feature using

How do people deal with R and memory issues? I have tried using gc() to see how much memory is used at each step. Scanned Crawley R-Book and all other R books I have available and the FAQ on-line but no help really found. Running WinXP Pro (32 bit) with 4 GB RAM. One SATA drive pair is in RAID 0 configuration with 10000 MB allocated as virtual memory. I do have another machine

Hi all, How can I set open files that contain values of Zero =0? These are valid values for the parameters I need to evaluate. I have tried CSV and tab formats. Trying XL Connect and/or XLConnectJars dies not seem to work to open Excel files so I am at a loss on how to get the data into a DF. Sample of data with 0 values: Filename Dur TBC Fmax Fmin Fmean Fc S1 Sc Pmc g8221843.13#

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

I need to construct confidence intervals for the binomial variance. This is the usual estimate v = x*(n-x)/n or its unbiased counterpart v' = x*(n-x)/(n-1) where x = binomial number of successes observed in n Bernoulli trials from proportion p. The usual X^2 method for variance confidence intervals will not work, because of the strong non-normal character of the sampling

Dear R users, I am new to R. I would like to calculate bootstrap confidence intervals using the BCa method for a parameter of interest. My situation is this: I already have a set of 1000 bootstrap replicates created from my original data set. I have already calculated the statistic of interest for each bootstrap replicate, and have also calculated the mean for this statistic across all the

Hi all Forgive me, I'm a total R newbie, and this seems to be a straightforward simple bootstrap problem, but after a whole day of trying to figure out how to do it I'm ready to give up. Part of the problem is that every example and every help page seems to be about doing something more far more complex. I'm got a table with 40 columns and 750 rows. I sum all the values across the

How can I performing Bootstrap Confidence Intervals for the estimates of nonparametric regression y=f(x) such as loess and spline smoothing Thanks in advance [[alternative HTML version deleted]]

Dear R-Experts, Thanks to Prof. Bonnett, I have got an R script working to calculate confidence intervals around the semipartial correlation coefficients. Now, I would like to calculate BCa bootstrap CIs using the boot library and the boot.ci(results, type="all") function. How could I modify my R script (here below reproducible example) to get the BCa bootstrap CIs ? CIsemipartcorr

Hello all, could you please tell me how to create a 95 percent confidence interval using R, if I have the next data: > blue [1] 4 69 87 35 39 79 31 79 65 95 68 62 70 80 84 79 66 75 59 77 36 86 39 85 74 [26] 72 69 85 85 72 > red [1] 62 80 82 83 0 81 28 69 48 90 63 77 0 55 83 85 54 72 58 68 88 83 78 30 58 [26] 45 78 64 87 65 Build a confidence interval of 95 % for the difference of the

Hello, I am using R.12.2.0. I am trying to generate bootstrap confidence intervals using bootcov from the rms package. I am able to impute the missing data using aregImpute and to perform a linear regression on the imputed datasets using fit.mult.impute, but I am unable to use bootcov to generate the confidence intervals for the R-squared. Here is a small example that should duplicate the

Hi all. This is my first post in this forum. Finally I find a forum in the web about R, although is not in my language. Now I'm working with Bootstrap CI. I'd like to know how I can calculate a Bootstrap CI for any statistic, in particular, for Kurtosis Coeficient. I have done the following code lines: > library(boot) > x=rnorm(20) > kurtosis=function(x)

Hello; Our survey is structured as : To be investigated area is divided into 6 regions, within each region, one urban community and one rural community are randomly selected, then samples are randomly drawn from each selected uran and rural community. The problems is that in urban/rural stratum, we only have one sample. In this case, how to do bootstrap? Any comments or hints are greatly

Hi All: I have to compute bootstrap confidence intervals, the statistic (incremental cost effectiveness ratio) is computed from two samples (intervention and control) of different sizes. All the bootstrap functions that I have seen use one dataset as argument. I may go ahead and get the desired number of bootstrap replications separately. I would appreciate if you could point me to a source of a

Hi, All: I only have 4 samples. I wish to get a confidence interval around the mean. Is it reasonable? If not, is there a way to compute a confidence interval for such small sample size's mean? Many thanks, U [[alternative HTML version deleted]]

There appear to be reports in the literature that transform continuous independent variablea by the use of splines, e.g., assume the dependent variable is hot dogs eaten per week (HD) and the independent variable is waistline (WL), a normal linear regression model would be: nonconfusing_regression <- lm(HD ~ WL) One might use a spline, confusion_inducing_regression_with_spline <- lm(HD

I'm trying to find a bootstrap based confidence band for a linear model. I have created a data set with X and Y X=runif(n,-1.25,1.25) e=rnorm(n,0,1) Y=exp(3*X)+5*sin((30*X)/(2*pi))+2*e fit=lm(Y~X) summary(fit)   I define a bootstrap function named PairedBootstrap which is not listed here. Than I try many ways to find the confidence band. One way is to predict Y using the model I get above for