similar to: simple if question

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

Hi everyone, Does anyone know how to get "black and white theme" (grey scale,, I would say) graphs using lattice or ggplot2, as it is shown in this webpage: http://lmdvr.r-forge.r-project.org/figures/figures.html? I am using Sweave, and I cannot get that color configuration. I have added the following option: trellis.device(color=FALSE) but I got a pdf file with color graphs. Thank

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 everyone, Does anyone know if there is a package to compute raking weights using R? What I need is to create a variable with weights base in some demographic variables (e.g. sexo, age group, area) using the raking procedure. Thank you in advance! -- Sebasti?n Daza

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

Dear List Members, I have two small samples (n=20), the distributions are highly skewed. Does it make any sense to do a boostrap test to check for difference in means? And if so, could this be done like this: x <- numeric(10000) for(i in 1:10000) { x[i] <- mean(sample(sample1,replace=TRUE)) - mean(sample(sample2,replace=TRUE)) } (mean(sample1)-mean(sample2))/sd(x) Regards, Erika

Hello everyone, I am new in R and I am trying to create a id number (a correlative sequence of numbers) by group, and a correlative sequence of numbers inside each group (my idea is to get statistics by group without having to aggregate the database). Here an example: group id_group correlative_group A 1 1 A 1 2 A 1 3 A 1 4 B 2 1 B 2 2 B 2 3 C 3 1 C 3 2 C 3 3 C 3 4 C 3

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 everyone, I am trying to build a table putting standard errors horizontally. I haven't been able to do it. library(memisc) berkeley <- aggregate(Table(Admit,Freq)~.,data=UCBAdmissions) berk0 <- glm(cbind(Admitted,Rejected)~1,data=berkeley,family="binomial") berk1 <- glm(cbind(Admitted,Rejected)~Gender,data=berkeley,family="binomial") berk2 <-

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 am trying to build a table putting standard errors horizontally. I haven't been able to do it. library(memisc) berkeley <- aggregate(Table(Admit,Freq)~.,data=UCBAdmissions) berk0 <- glm(cbind(Admitted,Rejected)~1,data=berkeley,family="binomial") berk1 <- glm(cbind(Admitted,Rejected)~Gender,data=berkeley,family="binomial") berk2 <-

Hi everyone, I am just trying to figure out how to do a xyplot where in addition to dots and lines I can change dots' colors according to an individual variable (e.g., marital disruption across time, a dummy 0/1). When I use "groups" specification (see below), I get two different lines for each individual based on groups, and what I want is to get one line connecting dots, and

Hi everyone, I need to get a between-component variance (e.g. random effects Anova), but using lmer I don't get the same results (variance component) than using random effects Anova. I am using a database of students, clustered on schools (there is not the same number of students by school). According to the ICC1 command, the interclass correlation is .44 > ICC1(anova1) [1] 0.4414491