similar to: read table and import of a text file

Hi, I sometimes play around with extreme parameters for distributions and found that qbeta is not always monotone as the following example shows. I don't know whether this is serious enough to submit a bug report (as this example is near to the limitations of floating point arithmetic). Josef > x <- qbeta((0:100)/100,0.01,5) > x [1] 0.000000e+00 1.253990e-201 1.589622e-171

Hello, I have a problem with the hist() function and showing densities. The densities sum to 50 and not to 1! I use R version 2.9.1 (2009-06-26) and I load the seqinR library. My data is the following vector: [1] 0.1400000 0.2000000 0.2200000 0.2828283 0.1600000 0.1600000 0.3600000 [8] 0.1600000 0.2200000 0.2600000 0.2000000 0.3000000 0.2200000 0.2342342 [15] 0.1800000 0.2200000 0.1600000

Hi All, Is there an easy way to reduce a data.frame to 1 'id' per row while keeping information from the other rows of that same variable, if applicable? e.g.: # data multi[1:15,] id r n wi wi.tau z k alliance a.rater eml treatment outcome o.rater german 1 100 0.2800000 44 41 21.72514 0.2876821 210 <NA> <NA> <NA> <NA>

Hello, I am trying to get a linear model of y ~ log(x). *> lm (y~log(x))* However, I always get an error report: /Error in model.frame.default(formula = y ~ log(x), drop.unused.levels = TRUE) : variable lengths differ (found for 'log(x)')/ *Here was my y:* > y [1] 0.4500000 0.0500000 0.5000000 0.4000000 0.0000000 0.5000000 0.4000000 [8] 0.0500000

Hi All, I am interested in aggregating a data frame based on 2 categories--mean effect size (r) for each 'id's' 'mod1'. The 'with' function works well when aggregating on one category (e.g., based on 'id' below) but doesnt work if I try 2 categories. How can this be accomplished? # sample data id<-c(1,1,1,rep(4:12)) n<-c(10,20,13,22,28,12,12,36,19,12,

Dear R users, I have a function (simplified here) that accepts two arguments and performs various calculations: foo <- function(y, x) { a <- y*sqrt(x) b <- a+2 c <- a*b return(c) } If I call the function as follows I get the result I desire: > foo(.1, 1:12) [1] 0.2100000 0.3028427 0.3764102 0.4400000 0.4972136 0.5498979 0.5991503 0.6456854 0.6900000 0.7324555 0.7733250

Hello! I cannot install rails. `gem install rails` returns: ERROR: While executing gem ... (TypeError) incompatible marshal file format (can''t be read) format version 4.8 required; 0.0 given Interpreter is installed from http://rubyinstaller.org/ . Fresh installation, `gem list --local` returns: *** LOCAL GEMS *** minitest (1.6.0) rake (0.8.7) rdoc (2.5.8) `gem env`

Hi all: I'm struggling with getting my data re-formatted using functions in reshape/reshape2 to get from: 1957 0.862500000 1958 0.750000000 1959 0.300000000 1960 0.287500000 1963 0.675000000 1964 0.937500000 1965 0.025000000 1966 0.387500000 1969 0.087500000 1970 0.275000000 1973 0.500000000 1974 0.362500000 1976 0.925000000 1978 0.712500000 1979 0.337500000 1980 0.700000000 1981 0.425000000

Given a vector of numeric of length n, I need to find segments that are >= 0.2, compute the average of individual segments, and replace the original values in each segment by their corresponding averages. For example, there are three segments that are >= 0.2, the average of 1st segment is 0.3, 2nd is 0.5, and the 3rd is 0.5333333 >

This does not use reshape/reshape2, but it is pretty straightforward. Assuming X is your example data: > Y <- split(X[, 2], X[, 1]) > vals <- sapply(Y, length) > pad <- max(vals) - vals > Y2 <- lapply(seq_along(Y), function(x) c(Y[[x]], rep(NA, pad[x]))) > names(Y2) <- names(Y) > X2 <- do.call(cbind, Y2) > X2[, 1:6] 1957 1958 1959

Hi all, I have a problem using rdirichlet{gtools}. For Dir( a1, a2, ..., a_n), its mode can be found at $( a_i -1)/ ( \sum_{i}a_i - n)$; The means are $a_i / (\sum_{i} a_i ) $; I tried to study the above properties using rdirichlet from gtools. The code are: ############## library(gtools) alpha = c(1,3,9) #totoal=13 mean.expect = c(1/13, 3/13, 9/13) mode.expect = c(0, 2/10, 8/10) #

Is this what you want? temp<-c(rep("Low",2),rep("Medium",2),rep("High",2)) light<-rep(c("Dark","light"),3) avg<-dat.avg2[,3] # se<-dat.avg2[,4] dat.avg.temp<-data.frame(cbind(avg,se)) dat.avg.temp<-data.frame(cbind(temp,light,dat.avg.temp)) dat.plot<-qplot(light,avg, fill=factor(temp),data=dat.avg.temp, geom="bar",

I'm sory for my weak english. I need to analyze this subject : x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 y 0 0 1 0 0 1 0 0 1 0 czarne 1 1 0 0 0 0 1 0 0 0 rude 0 0 1 0 0 1 1 0 0 0 braz 0 0 1 0 1 0 1 0 0 0 blond 1 0 0 0 0 1 0 0 0 1 rude 1 1 0 0 0 0 0 0 0 1 blond 0 0 1 1 0 0 0 0 1 0 czarne 1 0 0 1 0 0 1 0 0 0 blond 0 0 1 0 0 1 1 0 0 0 blond 1 0 0 0 0 1 1 0 0 0 czarne 0 0 1 0 0 1 0 0 0 1 czarne 1 0 1 0 0 0

Hi, In Splus if I call the function crosstabs() the output is a contigency table; in each cell of the table is printed: N, N/RowTotal, N/ColTotal, N/Total. N is the number of observations in each cell. The same call to xtabs() in R will produce the contigency table but the only entry in each cell is N. How can I get the same relative frequencies that crosstabs() gives? Thanks, mike --