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Hi there ! I've got 7 Samples that may contain 29 differents kinds of material.... every line below corresponds to a differente kind of material, example: Sample 1, 2,3,5 and 6 has material 1 .... So I want to know how can I do a Venn Diagram with the data below .... ps ( it was generated by a csv, where every line is sep with (";")) Can someone helps me ? thanks csv file :

...umn is population (item 1 thorugh item 400) ## 2nd through nth column are all rounds of sampling dat <- matrix(data = NA, nrow = length(pop), ncol = n.samples + 1) dat[,1] <- pop dat ## Take samples of random sizes ## Record results in columns 2 through n ## 1 = sampled (marked) ## 0 = not sampled (not marked) for(i in 2:ncol(dat)) { a.sample <- sample(x = pop, size = sample(x = c(lower.bound:upper.bound), size = 1, prob = c(rep(1/length.ss.interval, length.ss.interval)...

Guys. I have an issue with a te110p card and also some tdm04b cards on the same system: Zttest returns this for the tdm04b cards: [root@mollendo ~]# /usr/src/zaptel-1.2.4/zttest 38 -v Opened pseudo zap interface, measuring accuracy... 8192 samples in 8191 sample intervals 99.987793% 8192 samples in 8192 sample intervals 100.000000% 8192 samples in 8192 sample intervals 100.000000% 8192 samples

Hi, I'm trying the brlr function in a penalized logistic regression function. However, I am not sure why I am encountering errors. I hope to seek your advice here. (output below) Thank you! Your help is truly appreciated. Min-Han #No error here, the glm seems to work fine >

...rough nth column are all rounds of sampling > dat <- matrix(data = NA, > nrow = length(pop), > ncol = n.samples + 1) > > dat[,1] <- pop > > dat > > ## Take samples of random sizes > ## Record results in columns 2 through n > ## 1 = sampled (marked) > ## 0 = not sampled (not marked) > for(i in 2:ncol(dat)) { > a.sample <- sample(x = pop, > size = sample(x = c(lower.bound:upper.bound), > size = 1, > prob = c(rep(1/length.ss.int...

...nrow = length(pop), > > > ncol = n.samples + 1) > > > > > > dat[,1] <- pop > > > > > > dat > > > > > > ## Take samples of random sizes > > > ## Record results in columns 2 through n > > > ## 1 = sampled (marked) > > > ## 0 = not sampled (not marked) > > > for(i in 2:ncol(dat)) { > > > a.sample <- sample(x = pop, > > > size = sample(x = c(lower.bound:upper.bound), > > > size = 1, > > >...

...Interface No interface for {56a868b4-0ad4-11ce-b03a-0020af0ba770}! > fixme:quartz:AsyncReader_QueryInterface No interface for {56a868b5-0ad4-11ce-b03a-0020af0ba770}! > fixme:quartz:Parser_QueryInterface No interface for {56a868b5-0ad4-11ce-b03a-0020af0ba770}! > err:quartz:ACMWrapper_ProcessSampleData Error sending sample (80040227) > err:quartz:AVISplitter_Sample Error sending sample (80040227) > err:quartz:ACMWrapper_ProcessSampleData Error sending sample (80040227) > err:quartz:AVISplitter_Sample Error sending sample (80040227) > err:quartz:ACMWrapper_ProcessSampleData Error se...

...>> dat <- matrix(data = NA, >> nrow = length(pop), >> ncol = n.samples + 1) >> >> dat[,1] <- pop >> >> dat >> >> ## Take samples of random sizes >> ## Record results in columns 2 through n >> ## 1 = sampled (marked) >> ## 0 = not sampled (not marked) >> for(i in 2:ncol(dat)) { >> a.sample <- sample(x = pop, >> size = sample(x = c(lower.bound:upper.bound), >> size = 1, >>...

Hello all, I have data from a csv that i need to coerce into a multi dimensional array. As you can see from my sample code i have yet to master building efficient code. I can sort of do what i want but its very cumbersome code and i know there is a better way to do it, i am just not expereinced enought yet. So for the following: #Create Data h1i1a1<-sample(1:20, 5, replace=TRUE)

Dear R-helpers: I am trying to write a script that iterates through a dataframe that looks like this: Example dataset called "sample": names <- c("S1", "S2", "S3", "S4") X <- c("BB", "AB", "AB", "AA") Y <- c("BB", "BB", "AB", "AA") Z <- c("BB",

...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) target.sample.size = 1742 count.lookup = rbind(levels(data$subj), as.numeric(table(data$subj))) To this end, I create a dynamic list of resampled subjects, `sample.subjects`, that keep on being selected and appended to the list as long as their summed data point contributions do not exceed `target.set.size`. To conveniently retrieve the number of data points that a given subject contributes I constructed a reference matrix, `count.looku...

Hi, i have used S4 classes to implement a unified access to random number generators (package rstream on CRAN). I have used a construct to allow optional arguments: if(!isGeneric("rstream.sample")) setGeneric("rstream.sample", function(stream,...) standardGeneric("rstream.sample")) setMethod("rstream.sample",

Hi, consider this one as an FYI, or a seed for further discussion. I am aware that many traps on sample() have been reported over the years. I know that these are also documents in help("sample"). Still I got bitten by this while writing sample(units, size=length(units)); where 'units' is an index (positive integer) vector. It works in all cases as expected (=I expect)

# Hello, # I have a list with 6 categories and with different numbers of rows. # I would like to change each of them into a unique data frame in order to match # values with other data frames and perform some calculations. # Or I could make each category or list element have the same number of rows and create one large data.frame. # below is a creation of a sample list # I apologize for the

...I'm having in R. I have a database with 100 observations. I need to sample n=9 sample size, 200 times (i.e. get 200 samples of size 9). N (pop. size) =100 Each sample can't contain the same observation more than one time (i.e. the program needs to check if the obs. has already been sampled into the sample - it it has, it should put it back and sample again another obs.) obviously I need to do this with replacement. Then, I need to draw (I think the best is with a histogram) the distribution of the mean of each os the 200 samples. I guess I need to do a loop for the 'sam...

I installed FC4, ran command, # yum install asterisk. A bunch of stuff happened, but can't locate .conf files. I have a list of files: /usr/share/doc/asterisk-1.2.4/configs/features.conf.sample /usr/share/doc/asterisk-1.2.4/configs/rtp.conf.sample /usr/share/doc/asterisk-1.2.4/configs/extensions.conf.sample /usr/share/doc/asterisk-1.2.4/configs/logger.conf.sample

...I select speakers compute their > data point contributions to make sure I have a set of the right size. > > set.seed(1) > target.sample.size = 1742 > count.lookup = rbind(levels(data$subj), as.numeric(table(data$subj))) > > To this end, I create a dynamic list of resampled subjects, > `sample.subjects`, that keep on being selected and appended to the list as > long as their summed data point contributions do not exceed > `target.set.size`. To conveniently retrieve the number of data points that a > given subject contributes I constructed a reference matri...

There is rumour that some of my mail didn't go where it should have because I don't know how to work email. It was suggested that I summarize here. The issue is with sample when the first argument is length 1. Current behaviour is: > sample('a', 1) Error in sample(x, size, replace, prob) : invalid first argument > sample(3+0i, 1) Error in x >= 1 : illegal comparison

Hi All, I'm trying to use the "sample" function within a loop where the vector being sampled from (the first argument in the function) will vary in length and composition. When the vector is down in size to containing only one element, I run into the "undesired behaviour" acknowledged in the ?sample help file. I don't want sample(10,1) to return a number from within 1:10, b...

Dear List, Is there a way I can sort a sample based on a sort index constructed from the data from which the sample is taken? Basically, I need to take 'many' samples from the same source data and sort them. This can be very time consuming for long vectors. Is there any way I can sort the data only once initially, and use that sort order for the samples? I believe that idea is what is