similar to: Different PCA results under Windows and Linux

Hello all, Probably my concepts about the data.frame and matrix and array in R are not clear, I need some clarification to help me understand them better. >M <- read.table("test1.csv",sep=",",row.names=NULL,header=T) gives me: M as M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 1 9 11 14 15 18 20 20 20 20 20 2 3 4 8 9 11 12 14 15 15 15 3 4 5 8 8 9 9 9 9 9 9 4 4

Hi to all the people again, I was writting a simply function in R, and wish to collect the results in a excel file. The work goes as follows, Ciervos<-function(K1, K0, A, R,M,Pi,Hembras) {B<-(K1-K0)/A T1<-(R*Pi*Hembras-M*Pi+B)/(Pi-M*Pi+R*Pi*Hembras) P1<-Pi-B R1<-P1*Hembras*R M1<-P1*M T2<-(R1-M1+B)/(P1-M1+R1) P2<-P1-B R2<-P2*Hembras*R M2<-P2*M

Cordial saludo, soy principiante en R y estoy haciendo un análisis multivariado de datos, y necesito hacer las elipses de confianza donde la variable 19 es cualitativa suplementaria y el resto son variables activas; tengo el siguiente código: eu60 <- read.csv(file.choose(), header=T, sep=";", dec=",", row.names=1) ### capturar datos eu60.pca <- PCA(eu60, quali.sup=19) ##

Cordial saludo adjunto la base de datos y el script: 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 <- coord.ellipse(eu60.data, bary=TRUE) plot.PCA(ellipse=eu60.ellipse, cex=0.8) El 4 de diciembre de 2013 17:35, Camilo Calle

Hola, ¿qué tal? Tu problema es que tienes agrupaciones de países (p.e., ASIA_MERIDIOL) con un solo respresentante. Las matrices con las que construyes las elipses son degeneradas (más bien, no existen: sospecho que tienen que ver con la covarianza... ¿de un único caso?). Elimina las agrupaciones triviales. Un saludo, Carlos J. Gil Bellosta http://www.datanalytics.com El día 4 de diciembre de

Hi, I am trying to find an efficient way of applying a linear regression model to different factor combinations in a data frame. I want to obtain the output with minimal or no use of loops if possible. Please let me know if this query is unclear. Thanks, Murtaza

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?

Hi All, How do i calculate KMO for a dataset? *Dataset:---------------------* m1 m2 m3 m4 m5 m6 m7 m8 1 2 20 20 2 1 4 14 12 2 9 16 3 5 2 5 5 15 3 18 18 18 13 17 9 2 4 4 7 7 2 12 2 11 11 11 5 7 8 5 19 5 2 20 18 6 7 4 7 4 7 9 3 3 7 5 5 5 12 5 13 13 12 8 6 6 4 3 5 17 17 16 9 12 12 4 2 4 4 14 14 10 5 14

Hi, I created a principal component plot using the first two principal components. I used the function princomp() to calculate the scores. now, I would like to superimpose an ellipse representing the center and the 95% confidence interval of a series of points in my plot (as to illustrate the grouping of my samples). I looked at the ellipse() function in the ellipse package but can't get it

Hi all! I?ve got some problems with the function gam (library mgcv). For some models I get the error message : Error: no valid set of coefficients has been found:please supply starting values In addition: Warning message: NaNs produced in: log(x) This is a shortened code I used: gam(y ~ M1 + M3 + M4 + M5 + M6 + sex + M1*M3 + s(age), family=Gamma(link ="identity"), weights=days) If

Hi! I am new to looping and R in general; and I have sent waaaay to much time on this one problem and am about a hair away from doing it manually for the next two days. So, there is a package that while calculating the statistic creates lists (that look like matrices) in the background. Each item (there are 10 items) has one of these ‘matrix looking list’ that I need to extract data from. The

Hello everyone. In my last post I did not explained my problem quite well. I made a principal component analysis and took the 2 first principal components. I made ​​a chart of my points based on the score of the 2 PC. I would like to add on this graph a 95% confidence region. To do this I used the ellipse function as follows: pcsref=PC$score[data[,1]==ref,1:2] #matrix containing the scores

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 <-

Dear R helpers,   Following is a R script I am using to run the Fast Fourier Transform. The csv files has 10 columns with titles m1, m2, m3 .....m10.     When I use the following commands, I am getting the required results. The probelm is if there are 100 columns, it is not wise to define 100 commands as fk <- ONS$mk and so on. Thus, I need some guidance to write the loop for the STEP A and

I am used to java (well, i dont remember it really well, but anyway) I have having a really difficult time making simple loops to work. I got the following to work: ## ##Creates objects Ux1, Ux2, Ux2 etc. that all contain n numbers in a random distribution ## m<-c(m1,m2,m3,m4,m5,m6,m7,m8,m9,m10)#these are defined as numbers (means) v<-c(v1,v2,v3,v4,v5,v6,v7,v8,v9,v10)#these

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

Dear All I am reposting because I my problem is real issue and I have been working on this. I know this might be simple to those who know it ! Anyway I need help ! Let me clear my point. I have huge number of datapoints plotted using either base plot function or xyplot in lattice (I have preference to use lattice). name xvar p 1 M1 1 0.107983837 2 M2 11

libswfdec/Makefile.am | 2 libswfdec/swfdec_movie.c | 24 +++++- libswfdec/swfdec_shape_parser.c | 4 - test/dump.c | 3 test/trace/Makefile.am | 9 ++ test/trace/movieclip-version-5.swf |binary test/trace/movieclip-version-5.swf.trace | 111 +++++++++++++++++++++++++++++++

Muchas gracias por la rapidez y por la ayuda tan efectiva, me quedó el gráfico de maravilla, al parecer el problema era que habían individuos en una sola categoría, así quedó la gráfica: [image: Imágenes integradas 1] El 4 de diciembre de 2013 11:04, Carlos J. Gil Bellosta < cgb en datanalytics.com> escribió: > Hola, ¿qué tal? > > Tu problema es que tienes agrupaciones de

Dear All: I am interested in multinomial logit models (function multinon, library nnet) but I'm having troubles in choose whether to define the predictors as factors or not. I had posted earlier this example (thanks for the reply ronggui): worms<- data.frame(year= rep(2000:2004, c(3,3,3,3,3)),age=rep(1:3,5),