similar to: A question regarding bootstrap

Hello all, I have what feels like a simple problem, but I can't find an simple answer. Consider this data frame: > x <- data.frame(sample1=c(35,176,182,193,124), sample2=c(198,176,190,23,15), sample3=c(12,154,21,191,156), class=c('a','a','c','b','c')) > x sample1 sample2 sample3 class 1 35 198 12 a 2 176 176

Hi group, This is how my test file looks like: Chr start end sample1 sample2 chr2 9896633 9896683 0 0 chr2 9896639 9896690 0 0 chr2 14314039 14314098 0 -0.35 chr2 14404467 14404502 0 -0.35 chr2 14421718 14421777 -0.43 -0.35 chr2 16031710 16031769 -0.43 -0.35 chr2 16036178 16036237 -0.43 -0.35 chr2 16048665 16048724 -0.43 -0.35 chr2 37491676 37491735 0 0 chr2 37702947 37703009 0 0

This is what I am starting with: initial<- matrix(c(1,5,4,8,4,4,8,6,4,2,7,5,4,5,3,2,4,6), nrow=6, ncol=3,dimnames=list(c("1900","1901","1902","1903","1904","1905"), c("sample1","sample2","sample3"))) And I need to apply a filter (in this case, any value <5) to give me one dataframe with only the

Dear experts, Please forgive the puzzled title and the length of this message - I thought it would be best to be as complete as possible and to show the avenues I have explored. I'm trying to fit a linear model to data with a binary dependent variable (i.e. Target.ACC: accuracy of response) using lrm, and thought I would start from the most complex model (of which "sample1.lrm1" is

Hi Group, I have a data frame below. Within this data frame there are samples (columns) that are measured more than once. Samples are indicated by "idx". So "id1" is present in columns 1, 3, and 5. Not every id is repeated. I would like to create a new data frame so that the repeated ids are averaged. For example, in the new data frame, columns 1, 3, and 5 of the original

I have searched the r-help files but have not been able to find an answer to this question. I apologize if this questions has been asked previously. (Please excuse the ludicrousness of this example, as I have simplified my task for the purposes of this help inquiry. Please trust me that something like this will in fact be useful what I am trying to accomplish. I am using R 2.4.1 in Windows XP.)

Hello, Let's say I've drawn a random sample (sample1.df) from a large data frame (main.df), and I want to create a second random sample (sample2.df) where the values in its ID column *are not* in the equivalent ID column in the first sample (sample1.df). How would I go about doing this? In other words: The values in sample2.df$ID *are not found* in sample1.df$ID, and both samples are

Hi all, I am processing 24 samples data and combine them in single table called CombinedSamples using following: CombinedSamples<-rbind(Sample1,Sample2,Sample3) Now variables Sample1, Sample2 and Sample3 have many different columns. To make it more flexible for other samples I'm replacing above code with a for loop: #Sample is a string vector containing all 24 sample names for (k in

R 2.7.2 Windows XP I am using apply to compute a series of Student's t-test from two matrices, sample1 and sample2. boo<-apply(sample1,1,t.test,sample2) I want to pick of the p-values from the tests, but can't seem to get it to work. I have tried several methods to get the values including: boo<-apply(sample1,1,t.test$t.test,sample2) boo<-apply(sample1,1,t.test,sample2)$t.test

Hi, I have the precision values of a system on two different data sets. The snippets of these results are as shown: sample1: (total 194 samples) 0.6000000238 0.8000000119 0.6000000238 0.2000000030 0.6000000238 ... ... sample2: (total 188 samples) 0.80000001 0.20000000 0.80000001 0.00000000 0.80000001 0.40000001 ... ... I want to check if these results are statistically significant? Intuitively,

Hello R list members, I'm a bio informatics student from the Leiden university (netherlands). We were asked to make a program with different clustering methods. The problem we are experiencing is the following. we have a matrix with data like the following research1 research2 research3 enz sample1 0.5 0.2 0.4 sample2 0.4

Hi, I just started using R for about one week and I have few problems. i)I have a problem in finding right function to convert a table of natural numbers to bitwise. For a simple example; I have the below table:- Column Col1 Col2 Col3 Sample1 5 7 10 Sample2 0 2 1 Sample3 4 0 0 Supposedly i wanted to convert to :- Column Col1 Col2 Col3

Hi everyone, I have just got different samples from a dataframe (independent and exclusive, there aren't common elements among them). I want to create a variable that indicate the sampling selection of the elements in the original dataframe (for example, 0 = no selected, 1= sample 1, 2=sample 2, etc.). I have tried to do it with ifelse command, but the problem is that the second line

I am wanting to plot a 95% confidence band using segplot, yet I am wanting to have groups. For example if I have males and females, and then I have them in different races, I want the racial groups in different panels. I have this minor code, completely made up but gets at what I am wanting, 4 random samples and 4 samples of confidence, I know how to get A & B into one panel and C&D in to

Hi all, As a molecular biologist by training, I'm fairly new to R (and statistics!), and was hoping for some advice. First of all, I'd like to apologise if my question is more methodological rather than relating to a specific R function. I've done my best to search both in the forum and elsewhere but can't seem to find an answer which works in practice. I am carrying out

Dear Dr.Goslee and anyone may intrested in matrix manipulate, I am using your ecodist to do mantel and partial mantel test, I have locality data and shape variation data, and the two distance matrixs are given as belowings. When I run the analysis, it is always report that the matrix is not square, but I didn't know what's wrong with my data. Would you please help me on this. I am quite

Dear R-users I hope someone could help me on this problem. I want to create a multiple kiteChart showing the real values with a scalebar on each indicating the scale . Here are some sample data to show what I want to achieve. Y <- read.table(textConnection("Sample1 Sample2 60 20 150 50 300

Dear R-helper: Suppose we have a matrix: Gene sample1 sample2 Gcnt1 12.0000 52.80000 Max 8.8000 39.10000 Tmem176b 67.9000 304.70000 Shmt2 8.6000 42.40000 Rtn4 11.5000 57.70000 Il17re 7.6000 38.80000 Bclp2 6.2000 32.10000 Mobkl3 4.4000 32.20000 Akr1b10 3.4000 30.10000

Hello, R friends... I am very new to R, and I need some help. I am trying to construct a simulation for my dissertation. I need to create 1000 datasets of 1000 subjects with the following variables... Treatment variable - Drawn from a binomial distribution (1 run, prob=.13) Covariate 1 - Drawn from a normal distribution (mean=100, sd=16) Covariate 2 - Drawn from a normal distribution

Hi group, I am trying to right a code to do the following This is how the test file looks like: Chr start end sample1 sample2 chr2 9896633 9896683 0 0 chr2 9896639 9896690 0 0 chr2 14314039 14314098 0 -0.35 chr2 14404467 14404502 0 -0.35 chr2 14421718 14421777 -0.43 -0.35 chr2 16031710 16031769 -0.43 -0.35 chr2 16036178 16036237 -0.43 -0.35 chr2 16048665 16048724 -0.43 -0.35 chr2 37491676 37491735