similar to: Dimension Reduction Using MCA

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

Hi,all: I want to perform multiple correspondance analysis via MCA{FactoMineR}. The data is in the attachment. My code: dat<-read.delim("e:\\mydata.txt",header=T) MCA(dat,quanti.sup=7,quali.sup=1:6) Error in `[.data.frame`(tab, , i) : undefined columns selected My question: Why does the error happen? Many thanks. Best. -------------- next part -------------- An embedded and

Hello and Thanks in advance for your suggestions. As a new member, I do not know exactly if such problem has ever been discussed in this forum. I need a small help using FactoMineR and RcmdrPlugin.FactoMineR package to calculate weights for the individual observational units. My data looks like:  You can treat the data spaced instead of tabbed.

Dear all, i'm trying to label specific individuals (supplementary ones) after a PCA with the FactoMiner package. There is not much details (possibilities?) in the R-help of the plot.pca function. There is indeed a "label" parameter but i could only manage to label the supplementary individuals with there "row.names" (i.e. label="indiv.sup") and not with the

I have data set with binary responses. I would like to conduct polychoric principal component analysis (pPCA). I know there are several packages used in PCA but I could not find one that directly estimate pPCA and graph the individuals and variables maps. I will appreciate any help that expand these reproducible scripts. #How to conduct polychoric principal component analysis pPCA using #either

ESTE SCRIPT FUNCIONA PERFECTAMENTE, LA PREGUNTA QUE HACÍA SE REFERÍA A QUE SI SERÍA POSIBLE QUE EN LA PROPIA TABLA CSV PUDIERAN ESTAR LOS NOMBRES DE LOS ANTIBIOTICOS Y QUE EL ANÁLISIS PCA NO LO RECHACE POR SER CUALITATIVO, QUE DE ALGUNA MANERA SE DIERA UNA INSTRUCCIÓN PARA EVITAR TENER QE HACER LO DE rownames manualmente setwd("D:/Public/Documents/R/FactoMineR/") table1 <-

Estimados Al leer este interesante script lo corrí, pero al hacer la elipse me devuelve esa información, no encuentro el porque saludos library(FactoMineR) eu60<- read.csv(file.choose(), header=T, sep=";", dec=".", row.names=1) eu60.pca <- PCA(eu60, quali.sup=19) eu60.data <- cbind.data.frame(eu60[,19], eu60.pca$ind$coord) eu60.ellipse <-

Hi, I created a figure with R and I want to save it in .pdf. I used this code: > pdf("res.pca.pdf",width=10,height=8) > library(FactoMineR) > res.pca<-PCA(acp) > res.pca > dev.off() When I go in my folder, I find an empty file ( 0 Ko). Do you know where is the problem. Thank you in advance -- View this message in context:

Efectivamente. Puedes cambiar el signo de todos los valores de la segunda componente, por ejemplo al hacer un gráfico de dispersión. No creo que se pueda hacer directamente en la función. Saludos Francesc El 01/10/13 17:53, Argel Gastélum Arellánez ha escrit: > Hola Francesc, muchas gracias por tu respuesta. > > Entonces, si quisiera que las gráficas de los resultados de

Este listado de antibióticos lo tengo en una tabla csv CON DETERMINADAS MEDICIONES CUANTITATIVAS POR AñOS Ampicillin Oxacillin Peniclina Ciprofloxacina Amikacina Gentamicina Kanamicina Ceftriaxone Ceftazidima Cefotaxima Cefazolina Cefuroxima Aztreonam Cloranfenicol Vancomicina Eritromicina Clindamicina Fosfomicina Amoxicilina Imipenem Azitromicina

Can't make sense of calculated results and hope I'll find help here. I've collected answers from about 600 persons concerning three variables. I hypothesise those three variables to be components (or indicators) of one latent factor. In order to reduce data (vars), I had the following idea: Calculate the factor underlying these three vars. Use the loadings and the original var

Hi, there: I am wondering if anyone here can provide an example using pca doing dimension reduction for a dataset. The dataset can be n*q (n>=q or n<=q). As to dimension reduction, are there other implementations for like ICA, Isomap, Locally Linear Embedding... Thanks, weiwei -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III

Por definición la primera componente principal es una combinación lineal que maximiza la varianza de modo que si la componente 1 es el vector de coeficientes a, entonces, el vector -a también puede ser dicha componente. Las otras componentes, por ejemplo la segunda, es incorrelacionada con la primera y también maximiza la varianza, luego el signo no importa. Así pues, el signo de cada

Dear all, not really a R question but: If I want to check for the classification accuracy of a LDA with previous PCA for dimensionality reduction by means of the LOOCV method: Is it ok to do the PCA on the WHOLE dataset ONCE and then run the LDA with the CV option set to TRUE (runs LOOCV) -- OR-- do I need - to compute for each 'test-bag' (the n-1 observations) a PCA

Hi :-) I'm new to R and I started using it for a project (I'm the CS guy in a group of statisticians helping them find out how to solve issues as they come out). This is my first post to the list and I am starting to learn R. Well, they were used to doing MCA analysis in other programs where the data seems to be preprocessed automatically before running MCA. So, they need to process a

Hello! I want to do Principal Component Analysis With FactoMiner package in . My data has 923 observation and 12 quantitatives variables. In data frame is there no missing values but are there Zeros as observed values for some units of analysis. When i execute the function >PCA(testeff) is there one error: #error in V* poids: non-numeric argument to binary operator. Can anybody help?

Instalo ade4 correctamente y no me abren los datos como por ejemplo data(bsetal97) ¿Qué piensan de eso? De: r-help-es-bounces en r-project.org [mailto:r-help-es-bounces en r-project.org] En nombre de Francesc Carmona Enviado el: Tuesday, October 01, 2013 7:30 AM Para: r-help-es en r-project.org Asunto: Re: [R-es] Análisis de componentes principales con ade4 y FactoMineR Por definición

Hola, He mirado por encima el conjunto y el posible error. Como alternativa, mira la solución que se propone aquí: http://stats.stackexchange.com/questions/24450/how-to-highlight-predefined-groups-in-pca-individual-map Y como sugerencias: - Limpiaría un poco el conjunto para quitar las filas que tienen todas sus columnas con NA. ¿Qué sentido tiene dejarlas? - Y haría el ejercicio de

I ran the following R script under both Linux and Windows, and got 2 different results. Linux R version 2.7.1 and Windows R version 2.7.2. > library(FactoMineR) >x1=read.table("freqtest.txt",header=TRUE) >xrcc2=x1[,1:8] >p1=PCA(xrcc2, graph=FALSE) >p1$var freqtest.txt file lines of text : M1 M2 M3 M4 M5 M6 M7 M8 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 -1 -1 1 1 -1 -1 -1 -1 -1 -1

Dear Forum, I have a class variable 1 (populations A-E), and two other class variables, variable 2 and variable 3. What I want is to see if the combination of var 2 and var 3, will give me a pattern that allows to distinguish populations. I found several packages like ade4, with pcaiv function and factoMineR. but there are not working. Using the ade4 package, when I try to build the pca: pca1