similar to: should I use rbind in my example?

Hi All, I have a problem with rbind. I have data that consist of weight height .. etc of 1000 patients. I would like to find the mean and the standard deviation ( for the weight , height etc) for each gender. data<-read.table("data.txt", header=T, sep='\t') fdata=NULL for (i in 1:50){ nn<-names(X)[i] m<-tapply(X[,i],data$gender,mean,na.rm=T) s<-tapply(X[,i],

One of the products of Project MOSAIC (funded by an NSF CCLI grant) has been the development of an R package with the goal of making it easier to use R, especially in teaching situations. We're not quite ready to declare that we've reached version 1.0, but version 0.4 does represent a fairly large step in that direction. You can find out more about the package on CRAN or by installing

One of the products of Project MOSAIC (funded by an NSF CCLI grant) has been the development of an R package with the goal of making it easier to use R, especially in teaching situations. We're not quite ready to declare that we've reached version 1.0, but version 0.4 does represent a fairly large step in that direction. You can find out more about the package on CRAN or by installing

Hello. I have the following distance matrix between 8 points: [1,] 0.000000 3.162278 7.280110 8.544004 7.071068 9.899495 6.403124 8.062258 [2,] 3.162278 0.000000 5.000000 6.403124 4.472136 8.944272 6.082763 8.062258 [3,] 7.280110 5.000000 0.000000 1.414214 1.000000 5.000000 4.242641 5.830952 [4,] 8.544004 6.403124 1.414214 0.000000 2.236068 4.123106 4.472136 5.656854 [5,] 7.071068 4.472136

Dear List, I have been trying to extract associations from a matrix whereby individual locations are within a certain distance threshold from one another. I have been able to extract those individuals where there is 'no interaction' (i.e. where these individuals are not within a specified distance threshold from another individual) and give these individuals a unique Group ID containing

Hi All, I have a function that is used with data frames having multiple id's per row and it aggregates the data down to 1 id per row. It also randomly selects one of the within-id values of a variable (mod), which often differ within-id. Assume this data frame (below) is much larger and I want to repeat this function, say 100 times, and then derive the mean values of r over those 100

Dear list, I have following data set, which I want to plot the "Scale" variable on the x-axis and "Mean"´on the y-axis for each Ageclass and for each sex. The Mean value of each Ageclass for each sex would be connected by a line. Totally, there should be 6 lines, from which three present the Mean values of each Ageclass for respective sex. Are there any easy ways to do

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

Dear R Help, I have noticed some inconsistent behaviour of add1 and drop1 with a weighted linear model, which affects the interpretation of the results. I have these data to fit with a linear model, I want to weight them by the relative size of the geographical areas they represent. _________________________________________________________________________________________ > example

I could not figure out what was the default when I ran hclust() without specifying the method. For example: I just have a code like: hclust(dist(data)) Any input would be appreciated:) [[alternative HTML version deleted]]

Dear R users, I have huge dataset like the bellow (prepared in notepad in txt format: 31;39;00N+65;40;00E T 36;31;42N+69;04;21E T 34;10;00N+69;41;00E T 34;34;00N+69;06;00E T 31;40;00N+65;44;00E T 35;00;00N+69;07;00E T 34;00;00N+69;53;00E T These are geographical coordinates, degree minute, seconds.   latitude      longitude 31;39;00N+65;40;00E T I would like to plot them on the map. I would

useR's, I have several matrices of size 4x4 that I want to calculate means of their respective positions with. For example, consider I have 3 matrices given by the code: mat1 <- matrix(sample(1:20,16,replace=T),4,4) mat2 <- matrix(sample(-5:15,16,replace=T),4,4) mat3 <- matrix(sample(5:25,16,replace=T),4,4) The result I want is one matrix of size 4x4 in which position [1,1] is the