similar to: Clusteranalysis

Dear all, i performed a clusteranalysis - which worked so far... i plotted the dendrogram and sooo many branches, a rough sketch would be enough ;) i tried max.levels therefore which worked, but not for the plot... i used the following plot(hcd,nodePar =nP, str(hcd,max.level=1)) the output on the terminal was: --[dendrogram w/ 2 branches and 196 members at h = 2.70] |--[dendrogram w/ 2

Hi The short version of my questions is this: How can I run a chi-square test over a matrix (table) to get the distanaces between rows and then run a SingleLinkage (or other fusion algorithm over the resulting table? ------------ The long-version of my question: My data consists of different data of different countries so I have stuff like how many people can read, write in X,Y,Z countries

HI, (1) ...thanks for the last comments to tcltk & Win2000. (2) - i'm newbie in tcl/tk but would like learn fast and develop (learning by doing) a tcl/tk clusterAnalysis application which combine some clusterMethods from different Rpackages. i use the tkttest.R example as "reference" and become not finished for my "first" step???? (3) how is it possible that the code

Full_Name: Jeff Schwarz Version: R1.3.1 OS: Windows 2000 Submission from: (NULL) (129.22.170.115) Error message (using predict and predict.nnet) > predict (smalltest, smallx[-jj,]) Error in matrix(NA, length(keep), nout, dimnames = list(rn, dimnames(object$fitted)[[2]])) : length of dimnames[1] not equal to array extent *** all relevant code and data source is given below *** I

Dear Rhelp,   I did a daisy on 5 lifestyle variables, 3 of which were nominal and 2 were ordinal and assigned types “nominal” and “ordinal” for the variables, respectively.  I got an output indicating their types as “I” for interval(?). Doing it on the Rdata example “flower” gave the same types in the output as the types they were assigned to.  Why is this so? Below are the codes and outputs.  

Hi. First of all, excuse me if I do any mistakes, but English is not a language I use very often. I have a data frame with numbers. A small part of the data frame is this: nominal ordinal 2 2 2 1 2 1 2 2 So, I want to use the gower distance function on these numbers. Here (

This is an OpenPGP/MIME signed message (RFC 2440 and 3156) --------------enig5A700CC15DEABD01445A1B70 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi, I have modified the code originally posted to include capability for arbitrary weightings for the rows and columns (which apply in the nominal-ordinal and the ordinal-ordinal cases). The code is pasted

I have a consulting client with data containing a mixture of continuous, nominal, and ordinal variables, who wishes to do some correlational analyses. Ignoring for the moment whether this is wise or useful, is there some way to calculate a matrix using, for example, polychoric correlations for pairs of nominal variables, or correlations based on normal scores for ordinal variables? [The PRELIS

Hi folks, I have five imputed data sets and would like to apply the same recoding routines to each. I could do this sort of thing pretty easily in Stata using MIM, but I've decided to go cold turkey on other stats packages as a incentive for learning more about R. Most of the recoding is for nominal variables, like race, religion, urbanicity, and the like. So, for example, to recode race

Hi volks, i have a question about the step() fkt. Is there a possibility to save the last model generated from this method. I have a loop and so i generate 100 different models with the step fkt and i want to know which model is the most common. CODE: ... missStep -> numeric(100) for (j in 1:100) { trainindex <- sample(c(1:462),300) train <- data[trainindex,] test <-

Dear all, i have a little trouble with cutting an understanding the handling of dendrogram and hclust. plotting clusters does work and it delivers a dendrogram. the problem is that the dendrogram shows lots of pretty small clusters and only some larger ones. i know that this has to do with my data. But is there possibility to tell the plot function just to plot the clusters with more than

Hi! I'm looking for a solution to analyse data, which consists of dichotomous responses (yes/no) for 2 multinomial ordinal variables. I was trying glm() and got hierarhical models treating all variables as nominal, but I can't figure out how to tell glm() to use a model for ordinal data like this: log(Mij) = intercept + X + Y + Z + beta*(x-x')*(y-y') where beta is a

hi merlin, Check out the hetcor package. Jim On Sat, Sep 2, 2017 at 6:25 AM, <merlinverdecia at infomed.sld.cu> wrote: > I would be very grateful if you would tell me how I can find the degree of > correlation between a nominal dependent variable and an independent ordinal > variable. The nominal variable has only two levels: YES and NO. > thank you very much in advance >

Hello All: I am looking for you help. I am trying to replicate the results of an example found in Alan Agresti's "Categorical Data Analysis" on pages 267-269. The example is one of a 2 x 2 cross-classification table of ordinal counts: job satisfaction and income. I am able to get Agresti's results for the independence model (G^2 = 12.03 with df = 9) assuming as he does that

I have been asked to analyse some questionnaire data- which is not data I'm that used to dealing with. I'm hoping that I can make use of the nabble expertise (again). The questionnaire has a section which contains a particular issue and then questions which are related to this issue (and potentially to each other): 1) importance of the issue (7 ordinal categories from -3 to +3) 2) impact

Hello at all, working with a dataset I try to optimize a non-linear function with constraint. test<-read.csv2("C:/Users/Herb/Desktop/Opti/NORM.csv") fkt<- function(x){ a<-c(0) s<-c(0) #Minimizing square error for(j in 1:107){ s<-(test[j,2] - (x[1] * test[j,3]) - (x[2] * test[j,4]) - (x[3]*test[j,5]) - (x[4]*test[j,6]) - (x[5]*test[j,7]))^2 a<- a+s} a<-as.double(a)

Hi I would like to analyse an ordinal categorical variable. I know how I can analyse a nominal categorical variable (with multinom or if there are only two levels with glm). Does somebody know which command I need in R to analyse an ordinal categorical variable? I want to describe the variable y with the variables x1,x2,x3 and x4. So my model looks like: y ~ x1+x2+x3+x4. y: ordinal factor

Hi, I was hoping someone could help me. I am a graduate student new to using R, and I'm trying to figure out how to recode a continuous variable to make it into an ordinal variable with 3 categories. I literally have no idea how to proceed--could anyone possibly advise me? Please copy me on any responses, as I have just subscribed to the R-help email list but don't know whether the

Hi all I have a dataset whose response is a categorical variable of the ordinal scale type (6 levels). I'm interested in building classification models, and I'm wondering if there is something implemented in R (or its packages) that I'm not aware of, to treat the ordinal scale measurements straightforwardly. I can think of the alternative of building a conditional (hierarchical)

Hello All: I know the function loglin() and loglm() from librarary(MASS) performs analysis on nominal categorical data. Are there any libraries, functions or examples available for analysis of ordinal categorical data? I have in mind procedures that can replicate results in Alan Agresti (1984) "Analysis of Ordinal Categorical Data." Thanks, ANDREW