similar to: Distinct combinations for bootstrapping small sets

Hello there, I have a problem concerning bootstrapping in R - especially focusing on the resampling part of it. I try to sum it up in a simplified way so that I would not confuse anybody. I have a small database consisting of 20 observations (basically numbers from 1 to 20, I mean: 1, 2, 3, 4, 5, ... 18, 19, 20). I would like to resample this database many times for the bootstrap process with

Hello there, I have a problem concerning bootstrapping in R - especially focusing on the resampling part of it. I try to sum it up in a simplified way so that I would not confuse anybody. I have a small database consisting of 20 observations (basically numbers from 1 to 20, I mean: 1, 2, 3, 4, 5, ... 18, 19, 20). I would like to resample this database many times for the bootstrap process with

Hello all, I am trying to optimize my logistic regression model by using bootstrap. I was previously using SAS for this kind of tasks, but I am now switching to R. My data frame consists of 5 columns and has 109 rows. Each row is a single record composed of the following values: Subject_name, numeric1, numeric2, numeric3 and outcome (yes or no). All three numerics are used to predict

?text says "'adj' allows _adj_ustment of the text with respect to '(x,y)'. Values of 0, 0.5, and 1 specify left/bottom, middle and right/top, respectively." But it looks like 0, 1 specify top, bottom respectively in the y direction. plot(1:4) text(2,2, "adj=c(0,0)", adj=c(0,0)) text(2,2, "adj=c(0,1)", adj=c(0,1), col=2) #the red

Hello, I am new to using R and I am having problems get boot() to work properly. Here is what I am trying to do: I have statistic called "cs". cs takes a data matrix (154 x 5) and calculates 12 different scores for me. cs outputs the data as a vector (12 x 1). cs doesn't really use weights, per se, however I have included this as one of the 2 arguments cs can take. I try

Hi, I need to resample characters from a dataset that consists of an extremely long string that is written over hundreds of thousands of lines, each of length 50 characters. I am currently doing this by first inserting a space after each character in the dataset and then using the following commands: y <- as.matrix(read.table("data.txt"), stringsAsFactors=FALSE) bstrap <-

Hi- I'm wondering if anyone can help me with my code. I'm coming up dry when I try to get a p-value from the following code. If I make a histogram of my resampled distribution, I find the difference between by groups to be significant. I've ranked the data since I have outliers in one of my groups. mange= c(35, 60, 81, 158, 89, 130, 90, 38, 119, 137, 52, 30, 27,

One of my colleagues has a data set from a two-level nested design from which we would like to estimate variance components. But we'd like some idea of what the inevitable outliers are doing, so we were looking for something in R that uses robust (eg Huber) treatment and returns robust estimates of variance. Nothing in my collection of R robust estimation packages (robust, robustbase and MASS

I'm implementing a custom bootstrap resampling procedure in R. This procedure resamples clusters of data points obtained by different subjects in an experiment. Since the bootstrap samples need to have the same size as the original dataset, `target.set.size`, I select speakers compute their data point contributions to make sure I have a set of the right size. set.seed(1)

I din't have the patience to go through your missive in detail, but do note that it is not reproducible, as you have not provided a "data" object. You **are** asked to provide a small reproducible example by the posting guide. Of course, others with more patience and/or more smarts may not need the reprex to figure out what's going on. But if not ... Cheers, Bert Bert Gunter

I am looking at MM-estimates for some interlab comparison work. The usual situation in this particular context is a modest number of results from very expensive methods with abnormally well-characterised performance, so for once we have good "variance" estimates (which can differ substantially for good reason) from most labs. But there remains room for human error or unexpected chemistry

I am trying to modify some code from Good 2005. I am trying to resample the mean of 8 values and then count how many times the resampled mean is greater than 10. But my count of means above 10 is coming out as zero, which I know isn't correct. I would appreciate it if someone could look at the code below and tell me what I am doing wrong. Many thanks, Graham > LL<-

I have executed a Repeated Measures ANOVA with one DV (latency) and one within subject factor (acoustic condtion: 3 levels) by bootstrapping my sampling distribution of F from the empirical sample distribution. I chose to resample because the sample distribution deviates from normality a lot. The overall F is significant and now I wish to decompose this with contrasts to ask if latencies to

dear group members, I am looking for a function that assess the stability of cluster. The result of hclust function is an hclust object which can be plot as a dendrogram. However to have confidence in the tree topology usualy bootstap is applied. I understand that I can apply bootstarp on the original data and then run hclust(dist() ) as much as I resampled but how to comapre the topologies the I

I apologize if this comes across as confusing. I will try to explain my situation as best I can. I have R bootstrapping my growth data for fish. It's resampling my database of age and length data and then produces several new datasets for me. In this case, it's resampling my data to create three new datasets of age and length data. Here is my code with my original data called

hi,All, I want to know whether Opus has AEC features like Speex? Thanks 2012/10/17 <opus-request at xiph.org> > Send opus mailing list submissions to > opus at xiph.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://lists.xiph.org/mailman/listinfo/opus > or, via email, send a message with subject or body 'help' to

There is now a tutorial on bootstrapping and other resampling methods at: http://www.burns-stat.com/pages/Tutor/bootstrap_resampling.html Corrections and other suggestions are welcome. The project started because a novice asked me about bootstrapping. My response was, "How dare you bug me while I'm playing with my cats, just google for it." My correspondent was not very impressed

I've just finished a little encoding set and the following happened: 22Khz resampled, q-0.4 encode = ~38kbs = one error at the beginning only. 22Khz resampled, q-0.5 encode = ~37kbs = 6 to 7 sleep errors in the first second or so, nothing after that. 22Khz resampled, q-0.6 encode = ~35kbs = 14 to 15 sleep errors then nothing. 22Khz resampled, q-0.8 encode = ~33kbs = Many sleep errors. ---

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

Hello all R-er, I'm trying to run a resampling method on some data. The current method I have takes 2+ days or a lot of memory . I was wondering if anyone has a better suggestion. Currently I take a matrix and get the correlation matrix from it. This will be called rho.A. Each element in this will be tested against the distribution from the resampled correlation B matrix. Some example