similar to: Working with tables with missing levels

I suppose everybody who makes a package for the first time thinks "I can change anything!" and then runs into this same question. Has anybody written out information on how a package can override functions in R base in the R 2.14 (mandatory NAMESPACE era)? Suppose I want to alphabetize variables in a summary.data.frame, or return the standard deviation with the mean in summary output.

I have the following code: --8<---------------cut here---------------start------------->8--- d <- rep(10,10) for (i in 1:100) { a <- sample.int(length(d), size = 2) if (d[a[1]] >= 1) { d[a[1]] <- d[a[1]] - 1 d[a[2]] <- d[a[2]] + 1 } } --8<---------------cut here---------------end--------------->8--- it does what I want, i.e., modified vector d 100 times.

Maybe its slightly off-topic, but can anybody help with computing entropy on matrix of probabilities? Guess we have a matrix of probabilites, A, 2x2, something like this: z x 0 1 2 3 4 0 0.063 0.018 0.019 0.016 0.000 1 0.011 0.162 0.040 0.042 0.003 2 0.015 0.030 0.164 0.033 0.002 3 0.012 0.035 0.036 0.159 0.002 4 0.004 0.021 0.018 0.013 0.082 sum(A)=1 Can i

On Thu, Nov 14, 2013 at 01:48:17AM -0800, edwintorok wrote: > Following the link to builder.ml from your blogpost I noticed the make_random_password () function, and I have some suggestions, well nitpicks really. See the 2 commits from this pull request. > > 1. Using Ocaml&#39;s buffered I/O means that one make_random_password() call reads 64k bytes from /dev/urandom which drops the

Hello I need to plot a histogram, but insted of using bars, I'd like to plot the data points. I've been doing it like this so far: h <- hist(x, plot = F) plot(y = x$counts / sum(x$counts), x = x$breaks[2:length(x$breaks)], type = "p", log = "xy") Sometimes I want to have a look at the "raw" data (avoiding any kind of binning). When x

Hi all, I apologies in advance if I am missing something very simple here, but since I failed at resolving this myself, I'm sending this question to the list. I would appreciate any help in understanding how the rpart function is (exactly) computing the "improve" (which is given in fit$split), and how it differs when using the split='information' vs split='gini'

Hello I'm plotting some data using matplot with a logarithmic scale on the y-axis. This is the call to matplot I'm using: matplot(turns, distances, type = "l", log = "y", lty = "solid", ylab = "", xlab = "Time steps", col = c("black")) The values for the 'distances' vector are always between 0 and 1. The

Hello: I am trying to work with a couple of microarray data sets, using platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.1 year 2003 month 11 day 21 language R In the shortcut for invoking R I have set

Hello Say I have the following data, and it's distribution given by table(): > x <- c(1, 1, 1, 2, 2, 3) > tx <- table(x) > tx x 1 2 3 3 2 1 Now say I have new data, > y <- c(3, 3, 3, 3, 4) > ty <- table(y) > ty y 3 4 4 1 Is there a way to "combine" tx and ty in such a way to give me the distribution below? 1 2 3 4 3 2 5

At the risk of being beaten about the face and body, can somebody explain why the middle example: log2(2^3); floor(log2(2^3)) is different than examples 1 and 3? > log2(2^2); floor(log2(2^2)) [1] 2 [1] 2 > log2(2^3); floor(log2(2^3)) [1] 3 [1] 2 > log2(2^4); floor(log2(2^4)) [1] 4 [1] 4 > DrC [[alternative HTML version deleted]]

I have question about log2 transformation and performing mean on log2 data. I am doing analysis for ELISA data. the OD values and the concentration values for the standards were log2 transformed before performing the lm. the OD values for samples were log2 transformed and coefficients of lm were applied to get the log2 concentration values. I then backtransformed these log2 concentrations and the

Does anyone know of another version of the Mcnemar test that provides: 1. Odds Ratios 2. 95% Confidence intervals of the Odds Ratios 3. Sample probability 4. 95% Confidence intervals of the sample probability Obviously the Odds Ratios and Sample probabilities are easy to calculate from the contingency table, but I would appreciate any help on how to calculate the confidence

I figured out something new that I would like to see if I can do this more easy with R then Excel. I have these huge files with data. For example: DataFile.csv ID Name log2 1 Fantasy 5.651 2 New 7.60518 3 Finding 8.9532 4 Looeka -0.248652 5 Vani 0.3548 With like header1: ID, header 2: Name, header 3: log2 Now I need to get the $ID out who have a &log2 value higher then 7. I

Dear all, I have a dataset where the interaction is more than obvious, but I was asked to give a p-value, so I ran a logistic regression using glm. Very funny, in the outcome the interaction term is NOT significant, although that's completely counterintuitive. There are 3 variables : spot (binary response), constr (gene construct) and vernalized (growth conditions). Only for the FLC construct

Hi list, Can somebody explain why there are these warning messages?? I just don't get it.? I'm using R 2.15.1 on WinXP. Thanks! Tao > x [1] -2.143510 -1.157450 -1.315581? 1.033562 -1.225440 -1.179909 >? ifelse(x>0, log2(x), -log2(-x)) [1] -1.099975 -0.210950 -0.395700? 0.047625 -0.293300 -0.238675 Warning messages: 1: In ifelse(x > 0, log2(x), -log2(-x)) : NaNs produced

Hello. My name is Akashah. i work at metabolic laboratory. From my study, i found that volcano plot can help a lot in my section.  i already studied about the volcano plot and get the coding to run in R software, unfortunately, there is may be something wrong with the coding. This is because  no graph appear, but no error (blue color text) was shown on the R console. Below is the coding for

i want to calculate shannon entropy which is H1,H2,H3....upto H7? if there is any function or any package in which i can find this entropy directly. do you have any information please share this and i will be very thankful to you. Regards, ++++++++++++++++++++++++++++++++++++++++++++++ MUHAMMAD FAISAL Department of Statistics and Decion Support system, University of

Hi, I have a dataset with 2194651x135, in which all the numbers are 0,1,2, and is bar-delimited. I used the following approach which can handle 100,000 lines: t<-scan('fv', sep='|', nlines=100000) t1<-matrix(t, nrow=135, ncol=100000) t2<-t(t1) t3<-as.data.frame(t2) I changed my plan into using stratified sampling with replacement (col 2 is my class variable: 1 or 2).

HELLO, anybody... could you help me to check the below coding for volcano. what is the mistake? what the plot could not display? # volcano_plot.r # # Author: Amsha Nahid, Jairus Bowne, Gerard Murray # Purpose: Produces a volcano plot # # Input: Data matrix as specified in Data-matrix-format.pdf # Output: Plots log2(fold change) vs log10(t-test P-value) # #

Hi, I'm trying to develop an histogram method for a class called "FLQuant" which is used by the package FLCore (http://flr-project.org). FLQuant is an extension to "array". There is an as.data.frame method that coerces flquant into a data.frame suitable for lattice plotting. The problem is that when I coerce the object and plot it after it works but if the method is