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Is there an "execute" command such that: execute(paste("X[3]<-5")) or execute(paste("anything")) where "anything" is executed by R?? Thanks, Alex Ahgarin ________________________________________________________________ Data Management ISC Inc. Washington, DC. ----------------------------------------------- FREE! The World's Best Email Address

Hello all, For presentation and printing purposes I'd like to be able to open a new window device and write text and/or include an object (mostly matrices or lists). I've tried around windows(), but which I'd expect to work doesn't: windows(text(results)), or windows(parse("Final result:",results)) -"results" is a numeric object-. Thanks for any help, Alex

I need some help here. >From a vector of variable length (say, c(A,B,C,D), I need to obtain all posible combinations (order doesn't matter) of the elements. I would like a function such as: function(x,c(A,B,C,D)) to give a matrix (for x=3) like: A B C A B D A C D B C D or for x=2 A B A C A D B C B D C D And so on. Any ideas?? Thank you, Alex Ahgarin Data Management I.R.W. Co.

Hello . I have a little question .. I work with Anova (aov) and Manova (manova) and want to print some result in a text file. I see the results with the summary function or the summary.aov function but i do not know how to save the differents probabilities of error in a value. Sorry for my poor English Thanks a lot. -- Lamoral Julien

I apologize in advance to those for who this wuestion may be too stupid. Running R 0.091 on Windows 95 (a very old machine I'm afraid 486-66 16M) When constructing a big matrix I get: Error: heap memory (6144 Kb) exhausted [needed 7087 Kb more] See "help(Memory)" on how to increase the heap size. Help(Memory) tells me I must: (Quote) Use command line options to set the memory

Hello, I faced something I can't understand. When I use boxplot.stats(1:10) and quantiles(1:10) the results are different for 25% and 75%: > boxplot.stats(1:10) $stats [1] 1.0 3.0 5.5 8.0 10.0 > quantile(1:10) 0% 25% 50% 75% 100% 1.00 3.25 5.50 7.75 10.00 Actually, I expected the value 3 for 25% and 8 for 75% as results of quantile(1:10). Can you please explain me

Hi, I am a total beginner with this whole thing so please have patience! I am trying to run an S-plus program with a certain line: spline(1:nrow(y), y[,1],n=100); This crashes with: Error: NAs in foreign function call (arg 8) Apparently, this is caused by the last command of spline: u <- seq(xmin, xmax, length.out = n) .C("spline_eval", z$method, length(u), x = u, y =

Hello everyone, I am using the following code to try to calculate the mean : dat<-read.table(file="C:\\Documents and  Settings.....txt") dat<-as.numeric(dat) x1.m<mean(dat) I am getting the following error message Error in eval.with.vis(expr,envir,enclos): (list) object cannot be coerced to type"double' I do not understand what is wrong as I thought that I have changed

Is the following behaviour of the quantile function what one would expect? > a <- 1:100 > quantile(a,.6) 60% 60.4 Philippe -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the

Hi, Dear R-users, I have a problem when I drawing a boxplot. I want to extend the whisker to the 5% and the 95% quantiles and only show the most extreme outlier, like 0.01% and 99.99% percentiles. What should I do? I saw something on boxplot.stat, but even I define the parameter in boxplot.stat, what I should do next? how to connect it to boxplot? Thank you very much! Catherine -- View

Hi, I'm stuck on creating a plot with x tick labels for every Nth tick mark - how is that done? I don't see a simple solution to this in help(plot) or help(par) and what I've tried is not working, eg, the following does not work, although it seems intuitive to me that it should work: x <- seq(-100,1000,25) y <- x * x % find all the x values that are multiples of 100 tmp <-

Hello, this forum was very helpful yesterday with a simple question I had on working with tables. What function will I need to use to do the following. Move matrix a: A B C D x 1 2 3 4 y 5 6 7 8 to the mean of B&C and C&D: BC CD x 2.5 3.5 y 6.5 7.5 and then to the mean of B&CB, C&BC, C&CD, and D&CD: AAB BAB

I am trying to fit a very simple broken stick model using the package "segmented" but I have hit a roadblock. > str(data) 'data.frame': 18 obs. of 2 variables: $ Bin : num 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 ... $ LnFREQ: num 5.06 4.23 3.50 3.47 2.83 ... I fit the lm easily: > fit.lm<-lm(LnFREQ~Bin, data=id07) But I keep getting an error

Using str() in a function. I am in the early phase of learning R, and I find I spend a lot of time trying to figure out what is actually in objects I have created or read in from a file. I'm trying to make a simple little function to display a couple of things about a object, let's say the summary() and the str(), sequentially, preferably without a bunch of surplus lines between them. I

Hi, I have few questions on the specification. * Just to understand : - What is the interest of super blocks ? Is it to save place when recording coded block flags (7.3) ? - What is the advantage of using the coded order ? it is more often easier with raster order (especially to find neighbor in 7.8.1 for exemple). Is it to simplify the correspondence between block, macro block

I've only begun investigating R as a substitute for SPSS. I have a need to identify for each CASE the closest (or most similar) 5 other CASES (not including itself as it is automatically the closest). I have a fairly large matrix (50000 cases by 50 vars). In SPSS, I can use Correlate > Distances to generate a matrix of similarity, but only on a small sample. The entire matrix can not

Hi R users, I'm here trying to understand correlated residuals in nonlinear estimation. I'm reading/studying the book Bates, D. M. and D. G. Watts, (1988), /Nonlinear regression analysis and its applications/, Wiley, NY. pages 92-94, trying to reproduce the figures and to find out the code in R to perform the necessary calculations. I also consulted Pinheiro and Bates, but without

Dear group, Here is my df : df3 <- structure(list(DESCRIPTION = c("COPPER May/10", "COTTON NO.2 Jul/10", "CRUDE OIL miNY May/10", "GOLD Jun/10", "ROBUSTA COFFEE (10) Jul/10", "SOYBEANS Jul/10", "SUGAR NO.11 Jul/10", "SUGAR NO.11 May/10", "WHEAT Jul/10", "SPCL HIGH GRADE ZINC USD",

Hi I am trying to build a regression model. My data looks like this: A B C D E 1 1 1 2.57 5 2 0 0 1.64 3 0 5 1 4.8 1 1 3 0 3.56 168 1 1 1 2.13 1 0 3 1 5 168 2 0 0 7.75 28 4 0 2 2.85 168 3 0 1 1.89 6 1 1 1 2.33 3 3 2 2 1.77 168 1 0 0 1.38 0.04 0 6 1 4.57 168 2 2 2 2.86 1 3 1 1 4.11 168 3 0 2 3 84 1 1 1 2.5 56 where E is a response variable and A, B, C,

I've a matrix like this: 1985 1.38 1.27 1.84 2.10 0.59 3.47 1986 1.05 1.13 1.21 1.54 0.21 2.14 1987 1.33 1.21 1.77 1.44 0.27 2.85 1988 1.86 1.06 2.33 2.14 0.55 1.40 1989 2.10 0.65 2.74 2.43 1.19 1.45 1990 1.55 0.00 1.59 1.94 0.99 2.14 1991 0.92