similar to: inverse binomial in R

Hi all, I am wondering if do exist a function in R that allow me to sample or choose randomly the rows (i.e., samples) inside a given matrix. Thank you very much in advance. Cheers, -- Gian [[alternative HTML version deleted]]

Hi ! I'd like to create a vector that has this kind of numeration 001 002 003 . . . 099 I have looked at format help page but couldn't get any hint on how to do it. Thanks Anna Anna Freni Sterrantino Ph.D Student Department of Statistics University of Bologna, Italy via Belle Arti 41, 40124 BO. [[alternative HTML version deleted]]

Hi all! I've got this error while running example(Glm) library("rms") > example(Glm) Glm> ## Dobson (1990) Page 93: Randomized Controlled Trial : Glm> counts <- c(18,17,15,20,10,20,25,13,12) Glm> outcome <- gl(3,1,9) Glm> treatment <- gl(3,3) Glm> f <- glm(counts ~ outcome + treatment, family=poisson()) Glm> f Call: glm(formula = counts ~

Hello Chris,  I 've found two other issues  with MAE and CRPS, giving warning  when running examples. I've the same issue on my data.  Hope that you could find some time to take a look here. Thank you Anna > library(ensembleBMA) Loading required package: chron > example(MAE) MAE>   data(ensBMAtest) MAE>   ensMemNames <-

Hello R community, I have a question regarding correlation and regression analysis. I have two variables, x and y. Both have a standard deviation of 1; thus, correlation and slope from the linear regression (which also must have an intercept of zero) are equal. I want to probe two particular questions: 1) Is the slope significantly different from zero? This should be easy with the lm

Hello !! I have this problem: A matrix on True/False and as many numerical vectors as columns, but of different length. What I 'd like to get is this: set.seed(12) > dat <- as.data.frame(matrix(as.logical(sample(T:F, 30, T)),5,6)) > colnames(dat) <- letters[1:6] > rownames(dat) <- paste(letters[1:5],1:5, sep="") > dat a b c d e f a1

Hello! I'm having some problems with  pit function in ensembleBMA. When I run on mu data I have the same warnings as below, when I type the example(pit). Any suggestion on how to resolve it? > library(ensembleBMA) Loading required package: chron > example(pit) pit>   data(ensBMAtest) pit>   ensMemNames <-

Dear all, I have a dataset representing several geographical points (locations), each one having a specific value after performing a PCA. Now I'm trying to understand how to separately represent on a geographic map (of Europe) the pattern of PC1,2, ecc.with colors (e.g.heatmap) I have to add that the dataset includes relatively few points. Therefore, I would also like to interpolate the values

Hello, I have a matrix with the distances among sites. And I have another matrix with the presence and absence of each species in each site. I would like to test the spatial autocorrelation among sites. I have tried to use the function gearymoran of the ade4 package, but error messages keep popping up. Do you know any function for me to test the spatial autocorrelation of my data? Thanks,

Hi, I am writing this in a wrong way, can someone please correct me? > A <- matrix() > length(A) <- 6 > dim(A) <- c(3,2) > colnames(A) <- c("X","Y") > A X Y [1,] NA NA [2,] NA NA [3,] NA NA > A$X Error in A$X : $ operator is invalid for atomic vectors > Thanks, cruz

Hi, I am very new to R and wanted to know if there is a package that, given very long nucleotide sequences, searches and identifies short (7-10nt) motifs.. I would like to look for enrichment of certain motifs in genomic sequences. I tried using MEME (not an R package, I know), but the online version only allows sequences up to MAX 60000 nucleotides, and that's too short for my needs..

Hi Everyone, I was hoping someone could help me with the settings for Tinn-R. I see in the screen shots that it has syntax help, or something similar (tips on what functions, etc). I can not seem to get this to turn on in the program, and I am wondering if I have to set up a few options. I quickly read through the help and could not figure it out. Many thanks! - Brock P.S. It appears as

Hi, I'm a new user. I've been reading through the manual and looking at various examples but am still trying to make sense of the most efficient ways to handle matrices of data. If I have a 2D matrix of data, how do I get the mean, min, max value of each row? I see the "mean" function on a matrix will give me averages by row, but min and max give me the value for the entire

Hi, Why this function doesn't work? function (x) { if (is.factor(x)) { if (!is.ordered(x)) { warning("La mediana non si puo' calcolare!!!") return(NA) } me <- median(unclass(x)) if (me - floor(me) != 0) { warning("Mediana indeterminata") return(NA) } else

Hi everybody, I am trying to fit a GAM model without intercept using library mgcv. However, the result has nothing to do with the observed data. In fact the predicted points are far from the predicted points obtained from the model with intercept. For example: #First I generate some simulated data: library(mgcv) x<-seq(0,10,length=100) y<-x^2+rnorm(100) #then I fit a gam model with

Hi all, I have some data x, which are actualy consisted of numerical enties. But the class of this matrix is set to be "factor" by someone else. I used "class(x)", it turns out to be "factor". So I can not calculate them. How can I turn them into numerical data so that I can apply math operations on them? Thanks a lot for your help. Selina [[alternative HTML

Full_Name: Wolfgang Resch Version: R 2.8.1 GUI 1.27 OS: OS X 10.4.11 Submission from: (NULL) (137.187.89.14) Strange behavior of qbinom: > qbinom(0.01, 5016279, 1e-07) [1] 0 > qbinom(0.01, 5016279, 2e-07) [1] 16 > qbinom(0.01, 5016279, 3e-07) [1] 16 > qbinom(0.01, 5016279, 4e-07) [1] 16 > qbinom(0.01, 5016279, 5e-07) [1] 0

a=c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9) b=c(0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1) cor(a,b)= -1 a'=qbinom(a, 1, 0.5) b'=qbinom(b, 1, 0.5) why cor(a',b') becomes -0.5 ? -- View this message in context: http://r.789695.n4.nabble.com/qbinom-tp4651460.html Sent from the R help mailing list archive at Nabble.com.

Hi, I found the following strange effect with qbinom & partial argument matching p0 <- pbinom(0, size = 3, prob = 0.25) qbinom(p0, size = 3, prob = 0.25) ## 0 o.k. qbinom(p0-0.05, size = 3, prob = 0.25) ## 0 o.k. ## positional matching: qbinom(p0, 3, 0.25) ## 0 o.k. ## partial argument matching: qbinom(p0 , s = 3, p = 0.25) ## 1 ??? qbinom(p0-0.05,

Hi, All: For most but not all cases, qbinom is the inverse of pbinom. Consider the following example, which generates an exception: > (pb01 <- pbinom(0:1, 1, .5, log=T, lower.tail=FALSE)) [1] -0.6931472 -Inf Since "lower.tail=FALSE", Pr{X>1} = 0 in this context, and log(0) = -Inf, consistent with the documentation. However, the inverse of this does NOT