similar to: list index rules evaluation behavior

Hi All, I have a vector n, and for each n[i] I want to extract n[i], n[i+1], n[i+2]..., until the cumulative sum of n[i] and subsequent elements exceeds a CheckValue, whereupon I move to the next index and repeat. I am trying to find a Vectorized approach, and have seen similar posts where filter{stat} and rollmean{zoo} were suggested, but, I haven't been able to figure a way to use them

I have split my original dataframe to generate a list of dataframes each of which has 3 columns of factors and a 4th column of numeric data. I would like to use lapply to apply the fitdistr() function to only the 4th column (x$isi) of the dataframes in the list. Is there a way to do this or am I misusing lapply? As a second solution I tried splitting only the numeric data column to yield a list

I am trying to write a function that generates one PDf containing plots from several .csv files within a directory. When I manually execute the code it seems to work, but not when it is a function. I think I need to return() something, but haven't had much luck figuring out what/how. plot.isi<-function(csv.path="~/project/csv by cell") { csv.files<-grep('.csv',

I have split my original dataframe to generate a list of dataframes each of which has 3 columns of factors and a 4th column of numeric data. I would like to use lapply to apply the fitdistr() function to only the 4th column (x$isi) of the dataframes in the list. Is there a way to do this or am I misusing lapply? As a second solution I tried splitting only the numeric data column to yield a

Hello. I am trying to remove from my dataframe, those rows in which the first 7 columns are duplicated even if subsequent columns make those rows unique. df<-data.frame(id=rep(c('amy','bob','joe') , each=5), pet1=sample(LETTERS[1:3],15, replace=T), pet2=sample(LETTERS[1:3],15, replace=T), pet3=sample(LETTERS[1:5],15, replace=T)) >df id pet1 pet2

I have a data frame containing the results of time measurements taken from several cells. Each cell was measured in conditions A and B, and there are an arbitrary number of measurements in each condition. I am trying to calculate the difference of each measurement from the mean of a given cell in a given condition without relying on loops. >my.df id cond time 1

There is a bug in Matrix package, please check it, thanks! Matlab result: x = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 >> lu(x) ans = 21.0000 22.0000 23.0000 24.0000 25.0000 0.0476 0.9524 1.9048 2.8571 3.8095 0.7619

Hi: I have data from an assay in the form of two vectors, one is response and the other is a predictor. When I attempt to fit a 5 parameter logistic model with "nls", I get converged parameter estimates. I also get the same answers with "gnls" without specifying the "weights" argument. However, when I attempt to use the "gnls" function and try to

Dear R Community, I am trying to optimize a water quality model that I am using. Based on conversations with others more familiar with what I am doing I plan to implement DEOptim to do this. The water quality model is interfaced through a GUI. I have the input file necessary to alter parameters and run the model as a text file. To do the optimization I have figured out the general procedure

I'm trying to use odesolve for integrating various series of coupled 1st order differential equations (derived from a system of enzymatic catalysis and copied below, apologies for the excessively long set of parameters). The thing that confuses me is that, whilst I can run the function rk4: out <- rk4(y=y,times=times,func=func, parms=parms) and the results look not unreasonable:

Hi everyone, I'm observing what I believe is weird behaviour when attempting to do something very simple. I want a correlation matrix, but my matrix seems to contain correlation values that are not found when executed on pairs: > test2$P2 [1] 2 2 4 4 1 3 2 4 3 3 2 3 4 1 2 2 4 3 4 1 2 3 2 1 3 > test2$HP_tot [1] 10 10 10 10 10 10 10 10 136 136 136 136 136 136 136 136 136

Hello, Can anyone think of a non-iterative way to generate a decreasing geometric sequence in R? For example, for a hypothetical function dg, I would like: > dg(20) [1] 20 10 5 2 1 where I am using integer division by 2 to get each subsequent value in the sequence. There is of course: dg <- function(x) { res <- integer() while(x >= 1) { res <- c(res, x) x

I have a simple system of linear equations to solve for X, aX=b: > a [,1] [,2] [,3] [,4] [1,] 1 2 1 1 [2,] 3 0 0 4 [3,] 1 -4 -2 -2 [4,] 0 0 0 0 > b [,1] [1,] 0 [2,] 2 [3,] 2 [4,] 0 (This is ex Ch1, 2.2 of Artin, Algebra). So, 3 eqs in 4 unknowns. One can easily use row-reductions to find a homogeneous solution(b=0) of: X_1

Hi. Here are three successive examples of simple quadratic programming problems with the same structure. Each problem has 2*N variables, and should have a solution of the form (1/N,0,1/N,0,...,1/N,0). In these cases, N=4,5,6. As you will see, the N=4 and 6 cases give the expected solution, but the N=5 case breaks down. >cm8 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 1 0

Hi, The following [simplified] query works well, however a variation which includes parenthesis seems to fail, in that it returns hits which should be excluded by the NOT term. This is surprising because in this simple case, the parenthesis shouldn''t change the Boolean evaluation ... any pointers? Working Query: field1:value1 AND NOT field2:value2 Failing Query: field1:value1 AND

OBJECTIVITY CRUTCH: Replace every occurance of "Rails 1.1" with "Zope 3.2" in this post before reading it. Personally, I enjoy having lots of cool new features in each release. But others (like my company) value improved reliability over new features and ask a bunch of questions during software evaluations. Examples: * Are there plans for bugfix-only releases of [Rails

Hello, in the last weeks I evaluated ext3 on the IBM Mainframe S/390 and IBM zSeries. (32 bit, big endian, 64 bit big endian) Ext3 had to face high stress tests, low memory situations, spontaneous resets as well as hardware failure simulations. I tested file system integrity,log replay and stability with ordered and journal mode. All with 32 and 64 bit. I think ext3 has reached a very stable

Hi guys, I have a code in R and it was work well but when I decrease the epsilon value (indicated in the code) , then I am getting this error Error: evaluation nested too deeply: infinite recursion / options(expressions=)? any help please y = 6.8; w = 7.4; z = 5.7; muy = 7; muw = 7; muz = 6; sigmay = 0.8; sigmaz = 0.76; sigmaw = 0.3; betayx = 0.03; betayz = 0.3; betayw = 0.67 s =

I recently started using R (Version 1.3.0 on a Dell machine). After using R for about a week, I started receiving the error message shown below. I can't even run simple commands that I know should work. Is this a bug or did I just mess something up? I've uninstalled and reinstalled R but continue to receive the same errors. The errors are shown below: R : Copyright 2001, The R

hello all i searched the archives and couldn't get a solution to the following question. i have the following function: F=function(z,v) { if (v==-.5) return(1) else return(2*v/z + 1/Recall(z,v-1)) } and while testing whether it works (ie F(z,v) is approx = besselK(z,1+v)/besselK(z,V). the recursion formula allows one to calculate besselK(z,1+v)/besselK(z,V) for large values of z )