similar to: Aggregating a data frame (was: Re: new R-user needs help)

Dear all, I have a dataset Exp name id x1 x2 x3 x4 x5 1 a 1 23 24 23 22 30 1 b 2 25 26 27 23 24 1 c 3 32 19 23 25 28 2 a 4 28 32 24 26 27 2 b 5 23 24 25 26 28 2 c 6 23 31 30 38 23 3 a 7 24 25 31 27 29 3 b 8 28 25 26 32 28 3 c 9 21 31 28 23 29 where each name a,b and c is repeated three times. Now I want to calculate the the averages of

Hi, I am a newbie to the world of R. I have a data converted in csv format. Few cells in some of the rows of the data are blank ( in the sense that there is no value available for the particular experiment). When i try to open the file in R. I get an warning message. The specific warning message i get is {Warning message: NAs introduced by coercion } My data more or less looks like this -

Hi, I''m using heatmap.2 to cluster my data, using the centroid method for clustering and the maximum method for calculating the distance matrix: library("gplots") library("RColorBrewer") test <- matrix(c(0.96, 0.07, 0.97, 0.98, 0.50, 0.28, 0.29, 0.77, 0.08, 0.96, 0.51, 0.51, 0.14, 0.19, 0.41, 0.51), ncol=4, byrow=TRUE)

Good day, For lack of a better solution (or perhaps I am ignorant to something more elegant), I have been bootstrapping panel data by hand so to speak and I would like to know if there is a way to define multiple variables in a loop using the loop variable. I found a post (here: https://stat.ethz.ch/pipermail/r-help/2002-October/026305.html ) that discussed naming multiple variables but it

I need to be able to detect a well-behaved loop. (i.e one where exp1 assigns a value to an int i, exp2 compares i with a loop constant, exp3 adjusts i by a loop constant, and the inner block has no assignments to i.) I need this because in Sun's Java VM garbage collection only takes place at safepoints, so a potentially unbounded loop must call safepoint() some time. However, safepoints are

Hi, i have a dataframe such as: Exp1 Exp2 Exp3 name1 12.6 78.0 45.6 name2 11.9 19.0 21.0 name3 10.0 14.0 17.0 ... ... ... Real datasets might be quite large - 20,000 rows by 100 columns I want to calculate metrics such as the variation *row-wise*. So, var for name1, var for name 2, var for name3 etc. Can someone kindly guide me on how best to code this? Also, once such a metric has

I have 4 dell 2970 server , three server harddisk is 146Gx6 ,one hard disk is 72Gx6: each server mount info is /dev/sda4 on /exp1 type xfs (rw) /dev/sdb1 on /exp2 type xfs (rw) /dev/sdc1 on /exp3 type xfs (rw) /dev/sdd1 on /exp4 type xfs (rw) /dev/sde1 on /exp5 type xfs (rw) /dev/sdf1 on /exp6 type xfs (rw) I create a gluster volume have 4 stripe gluster volume create test-volume3 stripe 4

Hello everybody, I need an help because I don´t know if the command for the ANOVA analysis I am performing in R is correct. Indeed using the function aov I get the following error:"In aov (......) Error() model is singular" The structure of my table is the following: subject, stimulus, condition, sex, response Example: subject stimulus condition sex response

below is a code to compute hessian matrix , which i need to generate 29 number of different matrices for example first element in x1 and x2 is use to generate let say matrix (M1) and second element in x1 and x2 give matrix (M2) upto  matrix (M29) corresponding to the total number of observations and b1 and b2 are constant.  can some one guide me or help to implement this please. I did not

Dear Sirs, I am using both the Bioconductor adds on (Affy, AffyPLM,...) and the 'standard' R-package. I am trying to select a list of genes which all have expression values below a certain threshold. I have done this by creating a vector which has 0s where the expression is greater than the threshold and 1s where it is less than or equal to it. Multiplying this vector by the expression

Dear users, I'm trying to learn how to use the "...". I have written a function (simplified here) that uses doBy::summaryBy(): # 'dat' is a data.frame from which the aggregation is computed # 'vec_cat' is a integer vector defining which columns of the data.frame should be use on the right side of the formula # 'stat_fun' is the function that will be run to

Hello Everyone- I'm calculating summary statistics on a dataset (~4000 records, observations are not uniformly distributed) using summaryBy and trying to add a column with the number of observations to the output as well. What occurs to me is to use nrow(), but this doesn't appear to be working I'm able to replicate the same results with an example from the summaryBy docs:

OK, this is really weird! here's an example code: t1<-c(1,2,3,4) t2<-c(4,2,4,2) plot(t1~t2, xlab="exp1", ylab="exp2") dev.copy2eps(file="test.eps") that all seems fine... until you look at the eps file created, where for some weird reason, if you scroll down to the end, the code reads: /Font1 findfont 12 s 0 setgray 214.02 18.72 (e) 0 ta -0.360 (xp1) tb

Hi Folks, I'm trying to cut my data inside the summaryBy function. Perhaps formulas don't work that way? I'd like to avoid adding another column if possible, but if I have to, I have to. Any ideas? Thanks, Allie require(doBy) df = dataframe(a <- rnorm(100), b <-rnorm(100)) summaryBy(a ~ cut(b,c(-100,-1,1,100)), data=df) # preferred solution, but it throws an

Dear R experts, I would like to please ask for your help with repeating steps in an apply statement. I have a dataframe that lists multiple variables for a given id and visit, as well as drug treatment. > head(exp) id visit variable1 variable2 variable3 variable4 drug 1 3 1 13 10 7 11 0 2 3 5 10 15 9 9 0 3 3 12

Dear Soren and R users: I am trying to use the summaryBy function with weights. Is this possible? An example that illustrates what I am trying to do follows: library(doBy) ## make up some data response = rnorm(100) group = c(rep(1,20), rep(2,20), rep(3,20), rep(4,20), rep(5,20)) weights = runif(100, 0, 1) mydata = data.frame(response,group,weights) ## run summaryBy without weights:

Hello, I ran two lme analyses and got expected results. However, I saw something suspicious regarding p-level for fixed effect. Models are the same, only experimental designs differ and, of course, subjects. I am aware that I could done nesting Subjects within Experiments, but it is expected to have much slower RT (reaction time) in the second experiment, since the task is more complex, so it

Full_Name: Marc Paterno Version: 2.9.2 OS: Mac OS X 10.5.8 Submission from: (NULL) (99.53.212.55) summaryBy() produces incorrect results when given some data frames. Below is a transcript of a session showing the result, in a data frame with 2 observations of 2 variables. ------------------- thomas:999 paterno$ R --vanilla R version 2.9.2 (2009-08-24) Copyright (C) 2009 The R Foundation for

Hi, since I have quite large tables and the processing takes quite a while I am curious if I can improve the performance of this aggregation somehow: At the moment I am using summaryBy from the doBy package under R 2.4.0, Win2K. summaryBy(soc_s6aq5 + soc_s6aq7 + soc_s6aq9 + soc_s6aq11 ~ hh + comgroup,soc6a,postfix=c("","","",""),FUN=sum, na.rm=T) The

I'm trying to use a self-written function with the summaryBy function (doBy package). I have lots of data from Monte Carlo experiments comparing different estimators across different (combinations of) parameter values, similar to the following form: colnames(mydata) <- c("X", "b0", "b1", # parameter combination, corresponding (true) parameter values