similar to: [dist]how to analise a large matrix?

Thanks a lot for all of you that have reply me about opening and ending R workspaces in BATCH mode. However replies were a king general and I?m afraid I could not take the entire message from them. Therefore I chose to expose here a representative fraction of my work. I have 50 Rdata files (F1,F2,F3,F4, ,F50) with objects inside. I need to: open F1: - perform some simple operations

I already searched for information regarding the batch file operations within R. But I could not locate the information I need. Basically I have a doubt regarding the procedures on the batch use of several script files (*.R). How can I do this? How can I define the order of files? My intention is to programme openings, math operations and savings of *.Rdata sessions, but in a sequential

Dear all, I have 3 matrices with the same dimension, A,B,C and I would like to produce a matrix D where in each position would retrieve the max(or min) value along A,B,C taken from the same position. I guess that apply functions should fit, but for matrices objects I am not getting it. thanks in advance, Diogo André Alagador [[alternative HTML version deleted]]

To all, I am undergoing an analysis involving big matrices of about 30000x200 which I have to handle in a more efficient way. So I would like some advice to build such efficient function to deliver the following result: - starting with 2 matrices of the same dimension (eg. A and B) 0 0 3 5 6 0 0 5 A= 0 0 6 4 B= 0 4 3 5

Dear all, I have a matrix with positive and negative values. >From this I would like to produce 2 matrices: 1st - retaining positives and putting NA in other positions 2nd - retaining negatives and putting NA in other positions and then apply rowMeans for both. I am trying to use the function ifelse in the exemplified form: ifelse(A>0,A,NA) but by putting A as a 2nd parameter it

Hi all, I am not a skillful R programmer and has I am handling with large dataframes (about 30000 x 300) I am in need of an efficient function. I have 4 dataframes with the same dimension. I need to generate other dataframe with the some dimension than the others where in each position it has the median value of the 4 values in the same position coming from the 4 dataframes. Grateful by your

Hi all, I would like to know if it is posible by, someway, to get colMeans from a data.frame with numeric as well as character data, dispersed all over the object. Note that I would like to get colMeans neglecting character data. I am really in need of some function proceeding in that way… All the best Diogo André Alagador [[alternative HTML version deleted]]

Hi all, Basically I have 2 data frames with equal dimension and I want to apply the wilcox.test to compare columns in the same position (i.e. 1st of df.A with 1st of df.B, 2nd of df.A with 2nd of df.B,...). Anyone give me an hint on this, as I think it is nicer to avoid loops, specially for huge dataframes (30000 x 200) Thanks in advance, Diogo André Alagador Portugal [[alternative HTML

Hy all, I apologize for my ingenuity in regard to interfaces in R, but I do need it for my work. In that respect I took a simple and small example from the net (the "hello world", one) to interface R with C. I have a Windows XP OS using R.2.5.0 and in that regard I have installed the Perl and RTools files to my PC. I also wrote a proper path file, as suggested. The C program is:

Hi all, I'm handling massive data.frames and matrices in R (30000 x 400). In the 1st column, say, I have 0s and 1s indicating rows that matter; other columns have probability values. One simple task I would like to do would be to get the column mean values for signaled rows (the ones with 1) As a very fresh "programmer" I have build a simple function in R which should not be very

Hello, I have little doubt, and I do not think that the way I solve the problem is the best way to do it. The following is a small dataset x<-data.frame(city="Barcelona",sales=253639) x<-rbind(x,data.frame(city="Madrid",sales=223455)) x<-rbind(x,data.frame(city="Lisbon",sales=273633)) x<-rbind(x,data.frame(city="Madrid",sales=266535))

Hello, I have two different dataset, and wanted to join the two. For example I have a table of codes of cities, and other with with the codes of travels, [source for the destine]. what he wanted was to have a new data.frame with all the information city<-data.frame(city="Barcelona",cod=1) city<-rbind(city,data.frame(city="Madrid",cod=2))

Bueno, creo que no contesté tu pregunta. Con training <- data[-i, ] crea una df llamada training, sin la muestra i, que después utiliza para entrenar el algoritmo. Quoting Javier Marcuzzi <javier.ruben.marcuzzi en gmail.com>: > Estimado Manuel Mendoza > > Con sus datos y a modo de curiosidad, ¿que pasa en training <- data[-i, ]? > > Javier Rubén Marcuzzi >

Gracias Carlos. No uso caret, pero lo miraré. Quoting Carlos Ortega <cof en qualityexcellence.es>: > Hola, > > Creo que si utilizas "caret" y en la función "trainControl()" defines "oob" > como criterio de randomización, puedes luego recuperar del objeto del > modelo, las predicciones individuales... > > Saludos, > Carlos Ortega >

Gracias Javier, pero creo que si no consigo que me lo haga todo de una vez con un loop, me merece más la pena hacerlo como hasta ahora, una a una. Manuel Quoting Javier Marcuzzi <javier.ruben.marcuzzi en gmail.com>: > Estimado Manuel Mendoza > > No sería lo ideal, pero de pronto podría ir guardando en json, que es una > forma no estructurada, luego toma los datos

Gracias Carlos, mi idea es construir un cono, un cilindro u otros cuerpos geométrico y luego graficarlos. Alguna idea de como empezar? Muchas gracias como siempre El lun., 19 de feb. de 2018 15:06, <r-help-es-request en r-project.org> escribió: > Envíe los mensajes para la lista R-help-es a > r-help-es en r-project.org > > Para subscribirse o anular su subscripción a

Gracias Carlos J., sale bien, pero me transforma las 6 categorías en números del 1 al 6 ¿sabes cómo evitarlo? Quoting "Carlos J. Gil Bellosta" <cgb en datanalytics.com>: > apply(data, 1, function(x) which.max(table(x))) > > El sáb., 14 abr. 2018 a las 19:54, Manuel Mendoza (<mmendoza en mncn.csic.es>) > escribió: > >> >> Buenas tardes de

Pues no lo sé. Es algo del código de RandomForest, supongo. Quoting Jesús Para Fernández <j.para.fernandez en hotmail.com>: > U es un dataframe? > > Obtener Outlook para Android<https://aka.ms/ghei36> > > ________________________________ > From: R-help-es <r-help-es-bounces en r-project.org> on behalf of > Manuel Mendoza <mmendoza en mncn.csic.es>

Bien, Carlos, lo de ir metiendo las dfs en una lista parece buena idea, y después puedo fusionarlas con un cbind, tal y como hago ahora mismo, después de crear cada una de ellas independientemente. Son 9 dfs, y obtener cada una de ellas toma bastante tiempo de computación. Lo que quiero es que me haga las 9 en un loop. El problema es que si no les pone nombres distintos a las variables,

Hola de nuevo. Se me olvidaba la principal razón para utilizar gbm.step del paquete dismo. Como sabéis, los boosted si sobreajustan (a diferencia de los random forest o cualquier otro bootstrap) pero gbm.step hace validación cruzada para determinar el nº óptimo de árboles y evitarlo. Es fundamental. La opción que me queda, Carlos, es hacerlo con gbm, pero muchas veces, y usar el