Replace tabs by 2spaces

[morpheus.git] / pkg / R / sampleIO.R

diff --git a/pkg/R/sampleIO.R b/pkg/R/sampleIO.R
index 6fa38ae..3d46092 100644 (file)
--- a/pkg/R/sampleIO.R
+++ b/pkg/R/sampleIO.R
@@ -20,54 +20,54 @@
 #' @export
 generateSampleIO = function(n, p, β, b, link)
 {
-       # Check arguments
-       tryCatch({n = as.integer(n)}, error=function(e) stop("Cannot convert n to integer"))
-       if (length(n) > 1)
-               warning("n is a vector but should be scalar: only first element used")
-       if (n <= 0)
-               stop("n: positive integer")
-       if (!is.matrix(β) || !is.numeric(β) || any(is.na(β)))
-               stop("β: real matrix, no NAs")
-       K = ncol(β)
-       if (!is.numeric(p) || length(p)!=K-1 || any(is.na(p)) || any(p<0) || sum(p) > 1)
-               stop("p: positive vector of size K-1, no NA, sum<=1")
-       p <- c(p, 1-sum(p))
-       if (!is.numeric(b) || length(b)!=K || any(is.na(b)))
-               stop("b: real vector of size K, no NA")
+  # Check arguments
+  tryCatch({n = as.integer(n)}, error=function(e) stop("Cannot convert n to integer"))
+  if (length(n) > 1)
+    warning("n is a vector but should be scalar: only first element used")
+  if (n <= 0)
+    stop("n: positive integer")
+  if (!is.matrix(β) || !is.numeric(β) || any(is.na(β)))
+    stop("β: real matrix, no NAs")
+  K = ncol(β)
+  if (!is.numeric(p) || length(p)!=K-1 || any(is.na(p)) || any(p<0) || sum(p) > 1)
+    stop("p: positive vector of size K-1, no NA, sum<=1")
+  p <- c(p, 1-sum(p))
+  if (!is.numeric(b) || length(b)!=K || any(is.na(b)))
+    stop("b: real vector of size K, no NA")
 
-       #random generation of the size of each population in X~Y (unordered)
-       classes = rmultinom(1, n, p)
+  #random generation of the size of each population in X~Y (unordered)
+  classes = rmultinom(1, n, p)
 
-       d = nrow(β)
-       zero_mean = rep(0,d)
-       id_sigma = diag(rep(1,d))
-       # Always consider an intercept (use b=0 for none)
-       d = d + 1
-       β = rbind(β, b)
-       X = matrix(nrow=0, ncol=d)
-       Y = c()
-       index = c()
-       for (i in 1:ncol(β))
-       {
-               index = c(index, rep(i, classes[i]))
-               newXblock = cbind( MASS::mvrnorm(classes[i], zero_mean, id_sigma), 1 )
-               arg_link = newXblock %*% β[,i] #β
-               probas =
-                       if (link == "logit")
-                       {
-                               e_arg_link = exp(arg_link)
-                               e_arg_link / (1 + e_arg_link)
-                       }
-                       else #"probit"
-                               pnorm(arg_link)
-               probas[is.nan(probas)] = 1 #overflow of exp(x)
-               #probas = rowSums(p * probas)
-               X = rbind(X, newXblock)
-               #Y = c( Y, vapply(probas, function(p) (ifelse(p >= .5, 1, 0)), 1) )
-               Y = c( Y, vapply(probas, function(p) (rbinom(1,1,p)), 1) )
-       }
-       shuffle = sample(n)
-       # Returned X should not contain an intercept column (it's an argument of estimation
-       # methods)
-       list("X"=X[shuffle,-d], "Y"=Y[shuffle], "index"=index[shuffle])
+  d = nrow(β)
+  zero_mean = rep(0,d)
+  id_sigma = diag(rep(1,d))
+  # Always consider an intercept (use b=0 for none)
+  d = d + 1
+  β = rbind(β, b)
+  X = matrix(nrow=0, ncol=d)
+  Y = c()
+  index = c()
+  for (i in 1:ncol(β))
+  {
+    index = c(index, rep(i, classes[i]))
+    newXblock = cbind( MASS::mvrnorm(classes[i], zero_mean, id_sigma), 1 )
+    arg_link = newXblock %*% β[,i] #β
+    probas =
+      if (link == "logit")
+      {
+        e_arg_link = exp(arg_link)
+        e_arg_link / (1 + e_arg_link)
+      }
+      else #"probit"
+        pnorm(arg_link)
+    probas[is.nan(probas)] = 1 #overflow of exp(x)
+    #probas = rowSums(p * probas)
+    X = rbind(X, newXblock)
+    #Y = c( Y, vapply(probas, function(p) (ifelse(p >= .5, 1, 0)), 1) )
+    Y = c( Y, vapply(probas, function(p) (rbinom(1,1,p)), 1) )
+  }
+  shuffle = sample(n)
+  # Returned X should not contain an intercept column (it's an argument of estimation
+  # methods)
+  list("X"=X[shuffle,-d], "Y"=Y[shuffle], "index"=index[shuffle])
 }