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Hello, can anyone tell me if R has any special function for simulating the structure of human populations? Something like the genetic algorithm? I need to simulate a sample of a population with a specific structure. Is there something on R that can help me? Thanks to everyone. __________________________________________________________

Hello, I'm new with R. I need some help; I have a matrix of data to wich i want to apply the function dudi.acm to perform multiple correspondence analysis. However to use it all variables must be factors, so how can i turn each column of the matrix into a factor? I've tried as.factor. It works isolated for each column, but when I form the matrix of all factors it doesnt work. Please

Hello, can someone tell me how to extract a partial table from a multiway table? Something else: I tried to do basic operations on cross-tables, like subtracting two cross tables with same dimensions and i was unable to do so. Is there another way? thanks! __________________________________________________________

hello, i'm new in writting functions in R. I read the R-help manuals but still couldn't get it right. The problem is: I have data on 16 categorical variables for 335 individuals. the data are lines for individuals and columns for variables. I want to calculate the correlation between two variables using a formula given by Agresti for ordinal categorical variables. I have to calcule the

Hi all, I've searched this problem and still I can't understand my results, so here goes: I have some time series I've imported from excel with the dates in text format from excel. Data was imported with RODBC, sqlQuery() function. I have these dates: >adates [1] "01/2008" "02/2008" "03/2008" "04/2008" "05/2008"

Hi all I have a data frame with duplicate columns and i want to remove duplicates by adding rows in each group of duplicates, but have lots of NA's. Data: dfrm <- data.frame(a = 1:4, b= 1:4, cc= 1:4, dd=1:10, ee=1:4) names(dfrm) <- c("a", "a", "b", "b", "b") dfrm[3,2:3]<-NA dfrm a a b b b 1 1 1 1 1 1 2 2 2 2 2 2 3