similar to: dependence matrix

You should send this to r-help@stat.math.ethz.ch. On 03/09/2012 09:21 AM, Andrea Sica wrote: > Hello everybody, I'm looking for someone who is able with MCA and > would like to gives some help. > > If what I'm doing is not wrong, according to the purpose I have, I > need to understand how to create a dependence matrix, where I can > analyze the > dependence between

Dear all, I hope to be the clearest I can. Let's say I have a dataset with 10 variables, where 4 of them represent for me a certain phenomenon that I call Y. The other 6 represent for me another phenomenon that I call X. Each one of those variables (10) contains 37 units. Those units are just the respondents of my analysis (a survey). Since all the questions are based on a Likert scale, they

Hello, I'm an absolute beginner with R and neophite in data analysis, so please bear with me if I ask stupid question. I'm trying to do a correspondence analysis using R and MASS corresp function, but I get an error message which I'm unable to interpret: > data(weblog) > library(MASS) > corresp(~ url + fromurl, data=weblog) Error in svd(t(t(x1 * Dr) * Dc)) : error 306 in

Dear all, I tried out the example in the help document for mca (the multiple correspondence analysis of the MASS package): farms.mca <- mca(farms, abbrev=TRUE) farms.mca plot(farms.mca) But the graphic that I get seems unfeasible to me: I cannot recognize the numbers (printed in black) because they are all overlapping and concealing each other. I don ?t dare using my own data, which

Enio Jelihovschi" eniojelihovs@gmail.com Date: Fri, 20 Oct 2006 11:28:12 -0200 Subject: CORRESPONCE ANALYSIS Dear All I am new R user, trying to do correspondence analysis using the function mca of the package MASS. My question is: In the following example farms.mca <- mca(farms, abbrev = T) # Use levels as names plot(farms.mca, cex = rep(0.7, 2), axes = F) How can I change the

###Dear R users ###I have been using SensoMineR package from CRAN for most of my work in sensory data analysis and from my usage experience, I encountered some areas for improvement and considered ###modifying the function in SensoMineR package for my personal use. I felt that it could be useful to share this to the community for enabling adoption by other users where they might require a

Hi During using the R(vision 2.0.1), I meet a problem. I would like to do the Multiple Correspondence Analysis, but when I use the "< mca(lf, nf = 2, abbrev = FALSE)", the sentence "Error: couldn't find function 'mca'" will appear. So, please tell me how can I use the "mca()", thanks! Any help appreciated....

Carlos ?mca states that mca works on a dataframe. As you've written it is.data.frame(de) returns FALSE Try de <- data.frame(d,e) instead of de <- factor(c(d,e)) HTH Peter Alspach > -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Carlos > Mauricio Cardeal Mendes > Sent: Wednesday,

Greetings. I'm about to embark on my first big (to me at least!) R project, which will be to write a function to plot three-dimensional multiple correspondence analysis (mca) plots in a manner similar to scatterplot3d(). (plot.mca() plots only two dimensions, even though mca() will calculate more.) Before I do so, however, I would love to know that I'm not reinventing the wheel or any

Hi, I would like to do a multiple correspondence analysis with the mca function in the MASS library on data that I have as a contingency table (which I've tried converting to a data frame). For example, ========= > data(HairEyeColor) > hair.df <- as.data.frame(HairEyeColor) > hair.df Hair Eye Sex Freq 1 Black Brown Male 32 2 Brown Brown Male 38 3 Red

Andy, Take a look at Greenacre, Theory and Applications of Correspondence Analysis. He has many example of dealing with all sorts of data. Basically, the technique is relevant for 2-way tables -- MCA is an extenstion. It is not clear in your example whether CA is really appropriate -- you want to make an observation (if at all possible) fall in one cell, treating the others layers as

- This mail is in HTML. Some elements may be ommited in plain text. - WorldCIST Conference WorldCIST'15 - 3rd World Conference on Information Systems and Technologies, to be held at Ponta Delgada, S?o Miguel, Azores, Portugal, 1 - 3 April 2015, is a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences and concerns in

- This mail is in HTML. Some elements may be ommited in plain text. - WorldCIST Conference WorldCIST'15 - 3rd World Conference on Information Systems and Technologies, to be held at Ponta Delgada, S?o Miguel, Azores, Portugal, 1 - 3 April 2015, is a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences and concerns in

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- This mail is in HTML. Some elements may be ommited in plain text. - -------------------------------------------------------------------- WorldCIST'15 Workshops Azores, April 1-3, 2015 http://www.aisti.eu/worldcist15/index.php/workshops -------------------------------------------------------------------------------- WorldCIST'15 WorldCIST'15 - 3rd World Conference on Information

Greetings. I'm using plot.mca (from MASS) to construct some correspondence analyses of data drawn from focus-group transcripts. My question is simple albeit rather open-ended: I'm wondering what options there might be to plot.mca for tailoring the plots to my needs. The documentation to the function is somewhat sparse. Thanks- Andy Perrin

Dear list, Working the example in Stats complements to V&R 3rd ed., I found this : > library(MASS) > library(mva) > data(farms) > plot(mca(farms,abbrev=TRUE),cex=rep(0.7,2)) # ... Works OK # Sheer curiosity ... > plot(mca(farms,abbrev=TRUE,nf=4),cex=rep(0.7,2)) Error in rep(p * X.svd$d[sec], c(n, n)) : invalid number of copies in "rep" A bit of exploration in the

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

Greetings, again. This is not strictly an R question, so please feel free to ignore it if you like. My question is about the substance of correspondence analysis. Specifically, is it appropriate to use ca on a matrix of values such that the columns and/or rows are not mutually exclusive? To be more detailed: - The standard use of ca is illustrated in the example of corresp() (from MASS):

Hi Andrea, Thanks for your input. On Wed, Nov 21, 2018 at 12:43:52PM +0000, Andrea Di Biagio wrote: [... snip ...] > About the suggested design: > I like the idea of being able to identify code regions using a numeric > identifier. > However, what happens if a code region spans through multiple basic blocks? The current patch does not take into consideration cases where the region