search for: sample_time

...uts were completely different than expected; and when commenting the new stuff out, the OK-behaviour came back. Here a diff of the code: ===================================== oliver at siouxsie:~/R-BUG$ diff ok.R buggy.R 64c64 < res.txt <- paste(# "\n\n Anzahl der Messungen:", sample_times, "\n", --- > res.txt <- paste("\n\n Anzahl der Messungen:", sample_times, "\n", oliver at siouxsie:~/R-BUG$ ====================================== You see, I only wanted to print out a variable. But the output is much bigger now: *************************...

...d; and when commenting the new stuff out, > the OK-behaviour came back. > > > Here a diff of the code: > > ===================================== > oliver at siouxsie:~/R-BUG$ diff ok.R buggy.R > 64c64 > < res.txt <- paste(# "\n\n Anzahl der Messungen:", sample_times, "\n", > --- > >> res.txt <- paste("\n\n Anzahl der Messungen:", sample_times, "\n", >> > oliver at siouxsie:~/R-BUG$ > ====================================== > > You see, I only wanted to print out a variable. > > &gt...

Hello, here I have some code, which behaves quite strange, IMHO. There is a "res.txt" which will be collected before printing it. There is a paste-statement, which has a comment at the end of the line, which is this one: "# !!!!!HERE!!!!!". If you throw out the first hash-mark "#" on that line, the printout behaves like if it is done for each of the for-loop

Hi, I have run into this problem a couple of times now and hope you can help! If I want to plot mulitiple series with differing x-axis values (however, all in the same range) in 1 plot with one common axis R obstruses the plots. E.g. if I plot water content against time and I start with the sampler at 5 cm depth, it plots that one right. But the next depth, 15 cm, is measured at slightly

...and how I might viably analyse this dataset: The experiment has 3 blocks with a three level main plot treatment, split to a two level treatment. In this case most parameters are also measured repeatedly at 1-3 monthly intervals. Ideally I would like to test the full response~treatment_1*treatment_2*sample_time model, but don't know what the structure should be, or even whether this is a viable analysis. I should probably add that neither the main or sub plots are randomised. Thanks Mike Palmer