[R] logical to numeric matrix changing dimensions

example: > y=(rbind(c(TRUE,TRUE,TRUE),c(FALSE,FALSE,FALSE))) > y [,1] [,2] [,3] [1,] TRUE TRUE TRUE [2,] FALSE FALSE FALSE > as.numeric(y) [1] 1 0 1 0 1 0 I am trying to make some important matrixes become nuemric (1 or 0) but they change their dimensions.. anyone know how to easily fix

[R] logical to numeric matrix changing dimensions

example: > y=(rbind(c(TRUE,TRUE,TRUE),c( FALSE,FALSE,FALSE))) > y [,1] [,2] [,3] [1,] TRUE TRUE TRUE [2,] FALSE FALSE FALSE > as.numeric(y) [1] 1 0 1 0 1 0 I am trying to make some important matrixes become nuemric (1 or 0) but they change their dimensions.. anyone know how to easily fi

[R] sem problem - did not converge

Someone can help me? I tried several things and always don't converge # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> Item54, lam54, NA F1 -> Item63, lam63, NA F1

[R] sem problem - did not converge

Someone can help me? I tried several things and always don't converge I am making a confirmatory factor analysis. # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> I

[R] sem problem - did not converge

Someone can help me? I tried several things and always don't converge I am making a confirmatory factor analysis model. # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1

[R] Bayesian Neural Networks

I wanted to do a modelling like this, many variables, will simplificate for understanding y1 in [a1,b1] => (weight 1) => number 1.1 y2 in [a1,b1] => (weight 1) => number 1.2 y3 in [a1,b1] => (weight 1) => number 1.3 y4 in [a1,b1] => (weight 1) => number 1.4 y5 in [a1,b1] => (weight 2) => number 2