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Hello everybody! I have 2 >issues< concerning methods applied to missing data. I think they're bugs, but who knows. 1. var(NA) returns Error in var(NA) : missing observations in cov/cor instead of NA. I expanded the summary-function to my.summary including SDev, in order to use it with tapply, which crashes in case of groups with no valid data. 2. is a similar problem. I use

...0/bin/R anyway. It starts, but after "Type 'q()' to quit R." it crashes with a "Segmentation fault (core dumped)". Hope for some help. Thanks in advance. maciej Maciej Hoffman-Wecker Evotec OAI AG Discovery Informatics Schnackenburgallee 114 22525 Hamburg http://www.evotecoai.com Phone +49-40-5 60 81-2 31 Fax +49-40-5 60 81-2 22 E-mail maciej.hoffman-wecker at evotecoai.com

Hi to all. I'm a relative new R user and I would like someone to help me in solving the following simple question: I have a two column (L , T ) data frame. Column L has three levels: a, b , and c and Column T is real valued. L T a 0.8 a 0.9 a 0.1 a -0.8 a -1.0 b -0.3 b 0.7 b 1.0 b 0.4 c -0.5 c -1.0 My aim is to get a data frame of the

Thanks for replies, but i was not precise enough. The problem is not evaluating statistics on data with NA values. The problem is evaluation of statistics on data with length = 0. To make the problem more clear this is what i tried: This works fine: tapply(as.numeric(c(NA,2)), as.factor(c("a","b")), summary) But i need SDev, aswell, so i copied summary.default to

Hello everybody! I have a problem, that might be a quite common one. Often one has some statistics on subpopulations defined by several factors. This is usualy done by tapply resulting in a list. For presentation-tasks, the form of an array might be more advantageos, as ftable in combination with aperm allows for quite nice print. Moreover some html or ps output might be easier to produce. So

Hello s/r users! I've been digging around in the s- and r-mailing lists, but i actually didn't find any answer to my question. So here it is: How can i realize the following: myexec | R r_script > outfile.txt with myexec some executable writing data to stdout "cat data.txt" can serve as a test R

Dear R-users & developers, i'd like to know your opinion on the following suggestion. In short: Wouldn't it be possible to combine the properties of local regression (loess) and resistant regression (lqs) in order to cope with fitting of partially continuous data? I think the lqs-fitting could detect and eliminate the 2 outliers but perserve the discontinuity at t1 in case of the

Dear R-users, I'd appreciate your statistical opinion on the following problem. I'm fitting the four parameter logistic model [f(x) = a + (b - a)/(1 + exp((c - x)*d))] to assay data. We have a lot of samples to fit and my aim is to classify these samples into following groups: 1. no interrelation all results about =~ 0 too low concentration 2. only full