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Hello, I'd like to use DBI to store lm objects in a database. I've to analyze many of linear models and I cannot store them in a single R-session (not enough memory). Also it'd be nice to have them persistent. Maybe it's possible to create a compact binary representation of the object (the kind of format created created by "save"), so that one doesn't need to write

Hi All, I've the following data frame with 54 rows and 4 colums: > x Ratio Dose Time Batch R.010mM.04h.NEW 0.02 010mM 04h NEW R.010mM.04h.NEW.1 0.07 010mM 04h NEW ... R.010mM.24h.NEW.2 0.06 010mM 24h NEW R.010mM.04h.OLD 0.19 010mM 04h OLD ... R.010mM.04h.OLD.1 0.49 010mM 04h OLD R.100mM.24h.OLD 0.40 100mM 24h OLD I'd

Hello, A collegue of mine has compared the runtime of a linear model + anova in SAS and S+. He got the same results, but SAS took a bit more than a minute whereas S+ took 17 minutes. I've tried it in R (1.9.0) and it took 15 min. Neither machine run out of memory, and I assume that all machines have similar hardware, but the S+ and SAS machines are on windows whereas the R machine is Redhat

Hello, I've a data frame with 15 colums and 6000 rows, and I need the data in a single vector of size 90000 for ttest. Is there such a conversion function in R, or would I have to write my own loop over the colums? thanks for your help + kind regards Arne

Dear All, I'm trying to read a text data file that contains several records separated by a blank line. Each record starts with a row that contains it's ID and the number of rows for the records (two columns), then the data table itself, e.g. 123 5 89.1791 1.1024 90.5735 1.1024 92.5666 1.1024 95.0725 1.1024 101.2070 1.1024 321 3 60.1601 1.1024 64.8023 1.1024 70.0593

Hello, I got stuck with a graphics question: I've 3 figures that I present on a single page (window) via 'layout'. The layout is layout(matrix(c(1,1,2,3), 2, 2, byrow=TRUE)); so that the frst plot spans the both columns in row one. Now I'd like to magnify the fist figure so that it takes 20% more vertical space (i.e. more space for the y-axis). How would I do this in R?

Hello, I'm wondering what's the best way to analyse an unbalanced design with a low number of replicates. I'm not a statistician, and I'm looking for some direction for this problem. I've a 2 factor design: Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose level 1 in batch one is replicated 4 times, level 3 is replicated only 2 times. all

Hello, I was wondering wether there's a function in R that takes two vectors (of same length) as input and computes mean values for bins (intervals) or even a sliding window over these vectros. I've several x/y data set (input/response) that I'd like plot together. Say the x-data for one data set goes from -5 to 14 with 12,000 values, then I'd like to bin the x-vector in steps of

Hello, I've come across the following situation in R-1.8.1 (compile + running under RedHat 7.1): > phyper(24, 514, 5961-514, 53, lower.tail=T) [1] 1 > phyper(24, 514, 5961-514, 53, lower.tail=F) [1] -1.037310e-11 I'd expect the later to be 0 or some very small positive number. Is this a number under-flow of the calculation? Do you think I'm safe if I just set the result to 0

Hello, I've a very long character array (>500k characters) that need to split by '\n' resulting in an array of about 60k numbers. The help on strsplit says to use perl=TRUE to get better formance, but still it takes several minutes to split this string. The massive string is the return value of a call to xmlElementsByTagName from the XML library and looks like this: ... 12345

Hello, I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases. I've seen that although there are only 32 explanatory variables the best classification performance is reached when

Hello, I've crearted a boxplot with 84 boxes. So fat everything is as I expect, but there is quite some space between the 1st box and axis 2 and the last box and axis 4. Since 84 boxes get very slim anyway I'd like to discribute as much of the horizontal space over the x-axis. Maybe I've forgotten about a graphics parameter? Thanks for your help, Arne

Hello, I've a problem with a self written routine taking a lot of memory (>1.2Gb). Maybe you can suggest some enhancements, I'm pretty sure that my implementation is not optimal ... I'm creating many linear models and store coefficients, anova p-values ... all I need in different lists which are then finally returned in a list (list of lists). The input is a matrix with 84 rows

Hello, I've the following list n: > n [[1]] [1] "NEW" "OLD" "PRG" [[2]] [1] "04h" "24h" [[3]] [1] "000mM" "010mM" "025mM" "050mM" "100mM" where n <- dimnames(some.multidim.array) I'm trying to define a generic function that generates meaningful names from this list, e.g.

Dear All, I've a question about scaling the input variables for an analysis with svm (package e1071). Most of my variables are factors with 4 to 6 levels but there are also some numeric variables. I'm not familiar with the math behind svms, so my assumtions maybe completely wrong ... or obvious. Will the svm automatically expand the factors into a binary matrix? If I add numeric

Hello, can anybody here explain what a Jonckheere-Terpstra test is and whether it is implemented in R? I just know it's a non-parametric test, otherwise I've no clue about it ;-( . Are there alternatives to this test? thanks for help, Arne

Hello, I was wondering if there is an R-package to automatically calculate the IC50 value (concentration of a substrance that inhibits cell growth to 50%) for some measurements. kind regards, Arne [[alternative HTML version deleted]]

Hello, I'm looking for some guidance with the following problem: I've 2 samples A (111 items) and B (10 items) drawn from the same unknown population. Witihn A I find 9 "positives" and in B 0 positives. I'd like to know if the 2 samples A and B are different, ie is there a way to find out whether the number of "positives" is significantly different in A and B?

Dear R users, I'm trying to cluster 30 gene chips using principal component analysis in package mva.prcomp. Each chip is a point with 1,000 dimensions. PCA is probably just one of several methods to cluster the 30 chips. However, I don't know how to run prcomp, and I don't know how to interpret it's output. If there are 30 data points in 1,000 dimensions each, do I have to

Hello, I was wondering what's the best data structure in R for a multi-dimensional lookup table, and how to implement it. I've several categories say "A", "B", "C" ... and within each of these categories there are other categories such as "a", "b", "c", ... . There can be up to 5 dimensions. The actual value for [A][a]... is then