similar to: Help Transforming sums into observations

The R function hclust is used to do cluster analysis, but based on R help I see no way to print the actual fusion distances (that is, the vertical distances for each connected branch pairs seen in the cluster dendrogram). Any ideas? I'd like to use them test for significant differences from the mean fusion distance (i.e. The Best Cut Test). To perform a cluster analysis I'm using: x

Basic question: Is it correct to assume that when using cutree to set the # clusters (say k=4), cutree determines the clusters by the largest distances among all potential clusters? I've read the R help for cutree and am using it to define the number of groups to obtain Dunn Index scores (using clValid library) for cluster analysis (using Euclidean Distance and Ward's method) More

>From library(diptest): Shouldn't the following almost always be non-significant for Hartigan's dip test? dip(x = rnorm(1000)) I get dip scores of around 0.0008 which based on p values taken from the table (at N=1000), using the command: qDiptab, are 0.02 < p < 0.05. Anyone familiar with Hartigan's dip test and what I may not be understanding? Thanks, kbrownk

I'm using ks.test (mydata, dnorm) on my data. I know some of my different variable samples (mydata1, mydata2, etc) must be normally distributed but the p value is always < 2.0^-16 (the 2.0 can change but not the exponent). I want to test mydata against a normal distribution. What could I be doing wrong? I tried instead using rnorm to create a normal distribution: y = rnorm

Dear all, I've got a quite large dataframe (stor) with rows subject and rt (reaction time). I would like to split the reaction times per subject into 6 bins of equal size. Right now, I'm using the following code: bindata <- function(rt) { bindata <- rep(-1,length(rt)) binwidth <- length(rt)/6 bindata[order(rt)[(0*binwidth)+1:(1*binwidth)]] <- 1

Dear list I am having a couple of problems with the package 'bindata'. Having installed it using install.packages("bindata"), I am finding that when I load it up as a library I get the following errors: > library(bindata) Loading required package: e1071 Loading required package: mvtnorm Warning messages: 1: There is no package called `e1071' in: library(package, char

Dear All, I am looking for an R function or any other reference to generate a series of correlated Binomial (not a Bernoulli) data. The "bindata" library can do this for the binary not the binomial case. Thank you, Bernard --------------------------------- [[alternative HTML version deleted]]

Hi. I am trying to generate a correlated binary data set. I've tried to use mvtBinaryEP, binarySimCLF, and bindata packages but none of them works in R version 2.15.1. Do you know any package to generate correlated binary covariates and work in R version 2.15.1, or how to generate it? Thanks, [[alternative HTML version deleted]]

Dear R gurus, to compute the correlation matrix of "n" variables with "n_obs" observations each, possibly including NA, I use cor(M, use="pairwise.obs") where m is a "n" x "nobs" matrix. Now I want to know the number of observations actually used in this computation, namely for each pair of columns in M, say pair (i,j), I want to compute sum(

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

Hi all, I was wondering how to generate samples for two RVs X1 and X2. X1 ~ Bernoulli (p1) X2 ~ Bernoulli (p2) Also, X1 and X2 are correlated with correlation \rho. Regards, Vineet [[alternative HTML version deleted]]

Hi All: Package TSERIES is stated to depend on MVA. However, there is no MVA package to be found under the list of package sources. Best wishes, ANDREW tseries: Package for time series analysis Package for time series analysis with emphasis on non-linear and non-stationary modelling Version: 0.7-6 Depends: ts, mva, quadprog Date: 2001-08-27 Author: Compiled by Adrian

Hello R-Users, The following questions are not R-technical, but more of general statistical nature. 1. NORMALITY I built a normal linear regression model and now I want to check for the residual normality assumption. If I check the distribution graphically and look at the descriptive characteristics (skewness and kurtosis are below 1), I would confirm that the residuals are normally

Dear All, I try to simulate correlated binary data for a clinical research project. Unfortunately, I do not come to grips with bindata(). Consider corr<-data.frame(ID=as.factor(rep(c(1:10), each=5)), task=as.factor(rep(c(1:5),10))) [this format might be more appropriate:] corr2<-data.frame(ID=as.factor(rep(c(1:10),5)), tablet=as.factor(rep(c(1:5),each=10))) Now, I want to

I have an external data (.txt) for annual peak flood. The first column is the year, second column is the observation date, and the last is the observed discharge. My task is to calculate the mean, skewness and kurtosis of the said data. I was advised to use read.table() to read the entire data. Please help me on how to perform the required computation. I am obviously a new user of this statistical

I have 3 columns of data and want to plot each row as a point in a scatter plot and want one column to be represented as a color gradient (e.g. larger values being more red). Anyone know the command or package for this? Thanks, KB

The code I'm using is shown below. I would like to have a larger median at the top of the plot so that I can show the entirity of "title_text". Several times I tried messing with "par(mar", but that seemed to make matters worse. By any chance can anyone provide any insight as to the best way to increase the top/upper/northern margin, so the entirity of the title is

Hi, I have a set of observations which are divided into two sets A and B. I have some code that bins the dataset into 10 bins based on the max and min of the observed values. I would like to make a histogram of A & B using my calculated bins but plot the distribution of B on top of A (like a stacked barplot). This is possible since both sets A & B are binned using the same bin ranges.

Dear all, does any of you know a source for R code for the generation of correlated binary random numbers? Thanks a lot Peter Schlattmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- 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