similar to: How to avoid a for-loop?

I need some real help on this, really stuck how are the coefficients for ur.ers(y, type = c("DF-GLS", "P-test"), model = c("constant", "trend"), lag.max = 0) The max lag is set at zero, so the regression should simply be Diff(zt) = a*z(t-1) where a is the value i'm trying to find and z(t)'s are the detrended values. but through performing

I want to display cumulative summary functions with lattice. First I tried to get cumulated data: library(lattice) data(barley) d.cum <- with( barley, by( yield, INDICES=list(site=site,year=year), FUN=cumsum ) ) I got a list of vectors. I tried to get a dataframe which I could use in xyplot. But neither of the following functions led to the goal: d.cum.df1 <-

Dear R-users, Has anyone written a function for multifractal detrended fluctuation analysis? The "fractal" package does mono-fractal DFA, but not multifractal as far as I can tell. The MF-DFA approach is presented in: J. W. Kantelhardt, S. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, "Multifractal Detrended Fluctuation Analysis of

bob<-read.csv('shi.csv', header=T) newmean<-matrix(0, test, dim(bob)[2]-6);a<-0; for (i in c(4,8:(dim(bob)[2]))) {a<-a+1;newmean[,a]<-tapply(bob[,i], bob$Exam, mean)} colnames(newmean)<-colnames(bob)[c(4,8:(dim(bob)[2]))] Could anyone please help me what does the above code does ... I want to find mean ... but would like to know what exactly is the above code doing.

Dear colleagues, I have a data set that looks roughly like this; mydat<-data.frame(state=c(rep("Alabama", 5), rep("Delaware", 5), rep("California", 5)), news=runif(15, min=0, max=8), cum.news=rep(0, 15)) For each state, I'd like to cumulatively sum the value of "news" and make that put that value in cum.news. I'm trying as follows but I get

hello, please can anyone help me out. Am a new user of R program. Am having problem with this code below, not getting the expected results. 1. Each m, the cumulative sum should be 1.000 but the 2nd and 3rd m returned 2.000 and 3.000 instead of 1.000. 2. to get the LCL(m) and UCL(m) for each m base on these instructions if out.cum > 0.025 then LCL(m)= y-1 if out.cum >0.975

Hi, I hope someone knows the answer to this or has a real good reference about it (I am using Legendre & Legendre, Numerical Ecology, 1998).... My data is a data.frame with locations as rows and vegetation assemblages / species as columns. I've done a PCA, a correspondance analysis (CA) using ca in ca package and a detrended correspondance analysis (DCA) using decorana from vegan package.

Hello, I have what should be an easy question. I'm a new r user and making the transition from menus to the command line so as to do batch processing of tons of data. One of my data streams needs to be detrended. It's a vector of numbers that follows a negative exponential decay. I need to fit a curve to it and use the residuals as an object. The data looks something like this: foo.dat

hello, please can anyone help me out. Am a new user of R program. Am having problem with this code below, not getting the expected results. Each m, the cumulative sum should be 1.000 but the 2nd and 3rd m returned 2.000 and 3.000 instead of 1.000. thanks Aruike pp=function(x,n,M){z=1.0;a=2.3071430;b=7.266064;H=3 out.h=c() out.y=c() out.m=c() out.prob=c()

Hello R-Community, we are running aprogram to fit Non-linear differential equations to Aphid population Data and to estimate the birth and death parameters, here is the code: dat<-data.frame(Time=c(0:60),Cur=c(5,6.2,59,39,38,44,20.4,19.4,34.2,35.4,38.2,48.2,55.4,113.2, 97,112,115,126,136.6,140.6,147.2,151.6,157.8,170,202,210.4,221.2,224.4,248.2,266,

Dear members, I am performing multivariate analysis on marine benthic populations using R. At first glance I found ca and VEGANO packages to be the suitable for the task, but neither has incorporated Detrended Canonical Correspondence Analysis (DCCA), which is just the method I want to apply on my data. I've looked for alternative packages containing the method, but my suspicion is that

I have a data set like the following: subject visit x1 1 1 0.5 1 2 1.2 1 3 0.7 2 1 0.4 2 2 0.6 2 3 1.0 ..... where x1 is the interval between the two visits. Now I want to calculate the cumulative intervals since the beinging, for example subject visit x1 cum 1 1 0.5 0.5 1 2 1.2 0.5+1.2 1 3 0.7 0.5+1.2+0.7 2 1 0.4 0.4 2 2 0.6 0.4+0.6 2 3 1.0 0.4+0.6+1.0 ..... is there an easy to generate the

Dear R-users, I'm studing a DB, structured like this (just a little part of my dataset): _____________________________________________________________________________________________________________ Site Latitude Longitude Year Tot-Prod Total_Density dmp Dendoudi-1 15.441964 -13.540179 2005 3271.16 1007 16993.25 Dendoudi-2 15.397321 -13.611607

Hi, My objective is to calculate "Relative (Cumulative) Frequency of Event Occurrence" - something as follows: Sample.Number 1st.Fly 2nd.Fly Did.E.occur? Relative.Cum.Frequency.of.E 1 G B No 0.000 2 B B Yes 0.500 3 B G No 0.333 4 G B No 0.250 5 G G Yes 0.400 6 G B No 0.333 7 B B Yes 0.429 8 G G Yes 0.500 9 G B No 0.444 10 B B Yes 0.500 Please refer to the code below:

Hi all, I have written a simple R script to help me analyze a large data set. I would like to have the script pause to allow the user to input a character string that is subsequently used as a filename when saving tables. I have tried to use the "readline" command - this seems to work fine when entering commands one by one, but when I copy and paste the entire script into R,

Dear all, I'm performing a detrended correspondence analysis on vascular plant community data (296 species), and I have a question on the species scores projected in the ordination diagram. When I run a ordiplot all species are projected in the output graph, but I'd like to restrict the number of species plotted in the final graph. Some species are so rare in the data, that no relevant

Hi, The function cummax() seems to have a bug. > x <- c(NA, 0) > storage.mode(x) <- "integer" > cummax(x) [1] NA 0 The correct result of this case should be NA NA. The mistake in [ https://github.com/wch/r-source/blob/trunk/src/main/cum.c#L130-L136] may be the reason. Best Regards, Dongcan -- Dongcan Jiang Team of Search Engine & Web Mining School of Electronic

Good day, I'm using a VAR model to forecast sales with some extra variables (google trends data). I have divided my dataset into a trainingset (weekly sales + vars in 2006 and 2007) and a holdout set (2008). It is unclear to me how I should predict the out-of-sample data, because using the predict() function in the vars package seems to estimate my google trends vars as well. However, I want

Hi, Maybe I have not been looking in the right spot, but, I have not been able to fine a command to automatically calculate the running cumulative sum of a vector. Is there such a command? Example of current code: > eig$values [1] 678.365651 6.769697 2.853783 > prop<-eig$values/sum(eig$values) > prop [1] 0.986012163 0.009839832 0.004148005 >

Hi, I am interested in implementing a special variant of balloonplot.  Let me explain with an example dataset from the reference manual : library(gplots) data(Titanic) dframe<-as.data.frame(Titanic) survived<-dframe[dframe$Survived=="Yes",] attach(survived) balloonplot(x=Class,y=list(Age,Sex),z=Freq,sort=TRUE,show.zeros=TRUE,cum.margins=FALSE,             main="BalloonPlot :