similar to: Converting Between Character and Numeric Objects

Hello, I'm trying to do some differential equation modeling in R using the package 'deSolve.' Briefly, I'm trying to use the law of mass action (the details of which aren't really important) to structure a vector of rate equation which will be passed into an ODE function and solved with associated functions from 'deSolve.' Roughly what this entails is scanning

Hi all, I'm new to PCA in R, so this might be a basical thing, but I cannot find anything on the net about it. I need to make a PCA plot with two response variables (df$resp1 and df$resp2) against eight metabolites (df$met1, df$met2, ...) and I don't have a clue how to do... and I've only used the simplest PCAs before, like this: pcaObj=prcomp(t(df[idx, c(40:47)]))

I have to work with really huge matrices (about 1000*1000 or more). And I want to save those matrices in some file on my computer. I tried to do so by using the command write.tabe(SMatrix,file="C:/Programme/rw1061/SMatrix.txt",sep=" ",quote=FALSE,row.names=FALSE,col.names=FALSE) SMatrix is the matrix I want as a file. Unfortunately this does not work. Error message: Error:

hello all, I have a data frame and I want to create a column which assigns a letter based upon the value in another column. The data column has velocities ranging from 0 to 1000. So for example, for velocities between 0 and 300 I'd like to assign the letter "A" in the new column, for 300-600, "B" and so on and so forth. How would I do this? Thank you very much! Mehdi

Hi, I am an astronomer and somewhat new to boostrap statistics. I understand the basic idea of bootstrap resampling, but am uncertain if it would be useful in my case or not. My problem consists of maximizing a likelihood function based on the velocities of a number of stars. My assumed distribution of velocities of these stars is:

Hello: I would like to run a Cox proportional hazards regression on crayfish dislodgement at different water velocities by crayfish size class and substrate (rock) type. Additionally, there is a covariate variable, rock movement that may be influencing crayfish dislodgment. So... I have crayfish size class (CFSZCL) and substrate type (SUBSZ) as fixed factors influencing the dislodgment of

Hello R users, I'm trying to extract random samples from a big array I have. I have a data frame of over 40k lines and would like to produce around 50 random sample of around 200 lines each from this array. this is the matrix ID xxx_1c xxx__2c xxx__3c xxx__4c xxx__5T xxx__6T xxx__7T xxx__8T yyy_1c yyy_1c _2c 1 A_512 2.150295 2.681759 2.177138 2.142790 2.115344 2.013047

Rers: I have some ecological data (stream velocity vs. % cover of submerged weeds) that shows strong evidence of a thresholding step-function, e.g. below some velocity, % cover ranges from 0% to 100% (with no apparent relationship to velocity within this range of velocities), but above a certain "threshold" velocity, the % cover does not appear to exceed, say, 10%. There are good

I am attempting to create a contour plot using R with this code: > contour <- as.matrix(read.csv("contour.csv", row.names=1, header=TRUE)) > library(gplots) > filled.contour(contour, main="Flume 1 Flow Velocities") Now this produces the image/plot that I am looking for perfectly. However, the both axes only go from 0 to 1 instead on of using the row and column

Had a nasty memory leak that was seriously slowing down the browser and eating up a couple megabytes every reload. I was using: $$(''.dyntable'').each(function(elm) { new DynTable(elm) }); To set up my behavior, but discovered that just running: $$(''.dyntable''); Caused the memory leak all by itself. The fix I found for it was to change Element.extend to a

Hello everyone (and Hadley in particular), I often need to plot data from multiple datasets on the same graph. A common example is when mapping some values: I want to plot the underlying map and then add the points. I currently do it with base graphics, by recording the maximum region in which my map+point will fit, plotting both with these xlim and ylim parameters, adding par (new=T)