similar to: Diptest- I'm getting significant values when I shouldn't?

Hi all, I am using Hartigan & Hartigan's [1] "dip test" of unimodality via the diptest package in R. The function dip() returns the value of the test statistic but I am having problems calculating the p-value associated with that value. I'm hoping someone here is familiar with this process and can explain it. In the original article there is an example using n=63 and a

Hi, I just got a value for the dip test out of my data of 0.074 for a sample size of 33. I'm trying to work out what this actually means though? Could someone help me relate this to a p-value? Thanks James

Hola a tod en s, estoy intentando averiguar el número de modas en una distribución. Para ello utilizo diptest. Mi duda es que no acabo de entender cuando la información suministrada por los test suponen la existencia o no de unimodalidad/multimodalidad. Una parte de la salidad de diptest es la que pego a continuación (el resto esta en el fichero adjunto con las distribuciones kernels y las

I'm not sure where to begin with this, but I was wondering if someone could refer me to an R package that would test to see if a distribution fits a bimodal distribution better than a unimodal distribution. Thanks, Andrew [[alternative HTML version deleted]]

I need to measure kurtosis, skew, and maybe dip test on some distributions I have. Currently my data is in the form of 2 vectors x and y. Where x is 10 bins and y is the number of observations found in that bin. It seems that the measures I want to run require the actual observations laid out rather than already summed like I have them. Any suggestions on how to transform the data automatically? I

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

Dear R users, I am interested in uni- bi- multimodality tests, for analysing reaction times data. I was lead to Hartigan's dip test (Ann. Statistics, 13, 1985, pp. 70-84, Applied Statistics, 34, 1985, 320-325). Not being a programmer I am unable to translate the Fortran code given in ref. 2 into a R function. I'd be glad to learn that someone already did it, or has devised a better

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

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

Hi I have two questions, the first perhaps dumber than the second. Firstly, I have a data set, and when I plot a histogram it looks like a normal distribution. So I want to overlay a bell-shaped normal distribution on top of it, to demonstrate how similar it is to the normal distribution. I have read the help on dnorm(), rnorm(), pnorm() etc but still can't figure out how to plot a normal

Hi, I am new to bioconductor, trying to install KEGGSOAP package, but got warnings() when installing and error message when trying to load the package, can anyone suggest what went wrong? many thanks John > source("http://bioconductor.org/biocLite.R") Bioconductor version 2.11 (BiocInstaller 1.8.3), ?biocLite for help > biocLite("KEGGSOAP") BioC_mirror:

Has a test for bimodality been implemented in R? Thanks, John NIWA is the trading name of the National Institute of Water & Atmospheric Research Ltd.

Dear Readers: I have two issues in nonparametric statistical analysis that i need help: First, does R have a package that can implement the multimodality test, e.g., the Silverman test, DIP test, MAP test or Runt test. I have seen an earlier thread (sometime in 2003) where someone was trying to write a code for the Silverman test of multimodality. Is there any other tests that can enable me to

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

Hi there, I'm doing some clustering analysis and try to find all the algorithms related to clustering in R. Here is the list of the algorithms I found. But I'm not sure if It's the complete list. Could you please check it and see if there're other ones? Thank you very much! P.S.: List of the algorithms related to clustering: (1) Hierarchical methods hclust

Hi I am trying to install "rms" package but while installing it shows following error package 'survival' 2.36-2 is loaded, but >= 2.36.3 is required by 'rms' what to do? i am using linux OS I have tried by updated r-base-core but it didnt work regards GRR [[alternative HTML version deleted]]

Hi STATISTICA has a function called "Two-way joining" (see http://www.statsoft.com/TEXTBOOK/stcluan.html#twotwo) and the reference material states that this is based on the method as published by Hartigan (found this paper: http://www.jstor.org/pss/2284710 through wikipedia). What is the relationship (if any) between the "heatmap" function in R and this technique? Is there an

Dear UseRs, I wrote the following function to use MLE. --------------------------------------------- mlog <- function(theta, nx = 1, nz = 1, dt){ beta <- matrix(theta[1:(nx+1)], ncol = 1) delta <- matrix(theta[(nx+2):(nx+nz+1)], ncol = 1) sigma2 <- theta[nx+nz+2] gamma <- theta[nx+nz+3] y <- as.matrix(dt[, 1], ncol = 1) x <- as.matrix(data.frame(1,

Hi, I have a transition matrix T for which I want to find the steady state matrix for. This could be approximated by taking T^n , for large n. T= [ 0.8797 0.0382 0.0527 0.0008 0.0212 0.8002 0.0041 0.0143 0.0981 0.0273 0.8802 0.0527 0.0010 0.1343 0.0630 0.9322] According to a text book I have T^200 should have reached the steady state L L

Dear list, I have a data frame like: > log2.ratios[1:3,1:4] Clone a1 a2 a3 1 GS1-232B23 -0.0207500 0.17553833 0.21939333 2 RP11-82D16 -0.1896667 0.02645167 -0.03112333 3 RP11-62M23 -0.1761700 0.08214500 -0.04877000 how to make it to look like: > log2.ratios[1:3,1:4] a1 a2