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fix test; EMGLLF.c != EMGLLF.R now...
[valse.git]
/
test
/
generate_test_data
/
helper.R
diff --git
a/test/generate_test_data/helper.R
b/test/generate_test_data/helper.R
index
49cd1b5
..
8ec122b
100644
(file)
--- a/
test/generate_test_data/helper.R
+++ b/
test/generate_test_data/helper.R
@@
-1,10
+1,12
@@
#' Generate a sample of (X,Y) of size n with default values
#' Generate a sample of (X,Y) of size n with default values
+#'
#' @param n sample size
#' @param p number of covariates
#' @param m size of the response
#' @param k number of clusters
#' @param n sample size
#' @param p number of covariates
#' @param m size of the response
#' @param k number of clusters
+#'
#' @return list with X and Y
#' @return list with X and Y
-#'
@export
+#'
generateXYdefault = function(n, p, m, k)
{
meanX = rep(0, p)
generateXYdefault = function(n, p, m, k)
{
meanX = rep(0, p)
@@
-12,27
+14,30
@@
generateXYdefault = function(n, p, m, k)
covY = array(dim=c(m,m,k))
for(r in 1:k)
covY[,,r] = diag(m)
covY = array(dim=c(m,m,k))
for(r in 1:k)
covY[,,r] = diag(m)
-
pi
= rep(1./k,k)
+
π
= rep(1./k,k)
#initialize beta to a random number of non-zero random value
#initialize beta to a random number of non-zero random value
-
beta
= array(0, dim=c(p,m,k))
+
β
= array(0, dim=c(p,m,k))
for (j in 1:p)
{
nonZeroCount = sample(1:m, 1)
for (j in 1:p)
{
nonZeroCount = sample(1:m, 1)
-
beta
[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
+
β
[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k)
}
}
- sample_IO = generateXY(
meanX, covX, covY, pi, beta, n
)
+ sample_IO = generateXY(
n, π, meanX, β, covX, covY
)
return (list(X=sample_IO$X,Y=sample_IO$Y))
}
return (list(X=sample_IO$X,Y=sample_IO$Y))
}
-#' Initialize the parameters in a basic way (zero for the conditional mean, uniform for weights,
-#' identity for covariance matrices, and uniformly distributed for the clustering)
+#' Initialize the parameters in a basic way (zero for the conditional mean, uniform for
+#' weights, identity for covariance matrices, and uniformly distributed for the
+#' clustering)
+#'
#' @param n sample size
#' @param p number of covariates
#' @param m size of the response
#' @param k number of clusters
#' @param n sample size
#' @param p number of covariates
#' @param m size of the response
#' @param k number of clusters
+#'
#' @return list with phiInit, rhoInit,piInit,gamInit
#' @return list with phiInit, rhoInit,piInit,gamInit
-#'
@export
+#'
basicInitParameters = function(n,p,m,k)
{
phiInit = array(0, dim=c(p,m,k))
basicInitParameters = function(n,p,m,k)
{
phiInit = array(0, dim=c(p,m,k))
@@
-49,5
+54,5
@@
basicInitParameters = function(n,p,m,k)
gamInit[i,R[i]] = 0.9
gamInit = gamInit/sum(gamInit[1,])
gamInit[i,R[i]] = 0.9
gamInit = gamInit/sum(gamInit[1,])
- return (list("phiInit"
= phiInit, "rhoInit" = rhoInit, "piInit" = piInit, "gamInit" =
gamInit))
+ return (list("phiInit"
=phiInit, "rhoInit"=rhoInit, "piInit"=piInit, "gamInit"=
gamInit))
}
}