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> > Estimados muy buenas quería hacerles unas consulta: Estoy trabajando en mi tesis sobre mejoramiento animal y mi objetivo es evaluar la habilidad predictiva de modelos estadísticos mediante validación cruzada. Pero antes la intención es dividir mi base de datos en 3 partes y quisiera que todos los efectos incluidos en el estudio y cada uno de sus niveles, estén lo más equitativamente

Forgive the newbie question, I want to select random rows from my data.frame to create a test set (which I can do) but then I want to create a training set using whats left over. Example code: acc <- read.table("accOUT.txt", header=T, sep = ",", row.names=1) #select 400 random rows in data training <- acc[sample(1:nrow(acc), 400, replace=TRUE),] #try to get whats left

Hi. I started with a file which was a sparse 982x923 matrix and where the last column was a variable to be predicted. I did principle component analysis on it and arrived at a new 982x923 matrix. Then I ran the code below to get a neural network using nnet and then wanted to get a confusion matrix or at least know how accurate the neural net was. I used the first 22 principle components only for