#' multiRun
#'
-#' Estimate N times some parameters, outputs of some list of functions. This method is
-#' thus very generic, allowing typically bootstrap or Monte-Carlo estimations of matrices
-#' μ or β. Passing a list of functions opens the possibility to compare them on a fair
-#' basis (exact same inputs). It's even possible to compare methods on some deterministic
-#' design of experiments.
+#' Estimate N times some parameters, outputs of some list of functions.
+#' This method is thus very generic, allowing typically bootstrap or
+#' Monte-Carlo estimations of matrices μ or β.
+#' Passing a list of functions opens the possibility to compare them on a fair
+#' basis (exact same inputs). It's even possible to compare methods on some
+#' deterministic design of experiments.
+#'
+#' @name multiRun
#'
#' @param fargs List of arguments for the estimation functions
-#' @param estimParams List of nf function(s) to apply on fargs - shared signature
+#' @param estimParams List of nf function(s) to apply on fargs
#' @param prepareArgs Prepare arguments for the functions inside estimParams
#' @param N Number of runs
#' @param ncores Number of cores for parallel runs (<=1: sequential)
#' \donttest{
#' β <- matrix(c(1,-2,3,1),ncol=2)
#'
-#' # Bootstrap + computeMu, morpheus VS flexmix ; assumes fargs first 3 elts X,Y,K
+#' # Bootstrap + computeMu, morpheus VS flexmix
#' io <- generateSampleIO(n=1000, p=1/2, β=β, b=c(0,0), "logit")
#' μ <- normalize(β)
-#' res <- multiRun(list(X=io$X,Y=io$Y,optargs=list(K=2)), list(
+#' res <- multiRun(list(X=io$X,Y=io$Y,K=2), list(
#' # morpheus
#' function(fargs) {
#' library(morpheus)
#' ind <- fargs$ind
-#' computeMu(fargs$X[ind,],fargs$Y[ind],fargs$optargs)
+#' computeMu(fargs$X[ind,], fargs$Y[ind], list(K=fargs$K))
#' },
#' # flexmix
#' function(fargs) {
#' library(flexmix)
#' ind <- fargs$ind
-#' K <- fargs$optargs$K
-#' dat = as.data.frame( cbind(fargs$Y[ind],fargs$X[ind,]) )
-#' out = refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=K,
+#' K <- fargs$K
+#' dat <- as.data.frame( cbind(fargs$Y[ind],fargs$X[ind,]) )
+#' out <- refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=K,
#' model=FLXMRglm(family="binomial") ) )
#' normalize( matrix(out@@coef[1:(ncol(fargs$X)*K)], ncol=K) )
#' } ),
#' for (i in 1:2)
#' res[[i]] <- alignMatrices(res[[i]], ref=μ, ls_mode="exact")
#'
-#' # Monte-Carlo + optimParams from X,Y, morpheus VS flexmix ; first args n,p,β,b
-#' res <- multiRun(list(n=1000,p=1/2,β=β,b=c(0,0),optargs=list(link="logit")),list(
+#' # Monte-Carlo + optimParams from X,Y, morpheus VS flexmix
+#' res <- multiRun(list(n=1000,p=1/2,β=β,b=c(0,0),link="logit"), list(
#' # morpheus
#' function(fargs) {
#' library(morpheus)
-#' K <- fargs$optargs$K
-#' μ <- computeMu(fargs$X, fargs$Y, fargs$optargs)
-#' optimParams(fargs$K,fargs$link,fargs$optargs)$run(list(β=μ))$β
+#' K <- fargs$K
+#' μ <- computeMu(fargs$X, fargs$Y, list(K=fargs$K))
+#' o <- optimParams(fargs$X, fargs$Y, fargs$K, fargs$link, fargs$M)
+#' o$run(list(β=μ))$β
#' },
#' # flexmix
#' function(fargs) {
#' library(flexmix)
-#' K <- fargs$optargs$K
+#' K <- fargs$K
#' dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
-#' out <- refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=K,
+#' out <- refit( flexmix( cbind(V1, 1 - V1) ~ ., data=dat, k=K,
#' model=FLXMRglm(family="binomial") ) )
-#' sapply( seq_len(K), function(i) as.double( out@@components[[1]][[i]][,1] ) )
+#' sapply( seq_len(K), function(i)
+#' as.double( out@@components[[1]][[i]][2:(1+ncol(fargs$X)),1] ) )
#' } ),
#' prepareArgs = function(fargs,index) {
#' library(morpheus)
-#' io = generateSampleIO(fargs$n, fargs$p, fargs$β, fargs$b, fargs$optargs$link)
-#' fargs$X = io$X
-#' fargs$Y = io$Y
-#' fargs$K = ncol(fargs$β)
-#' fargs$link = fargs$optargs$link
-#' fargs$optargs$M = computeMoments(io$X,io$Y)
+#' io <- generateSampleIO(fargs$n, fargs$p, fargs$β, fargs$b, fargs$link)
+#' fargs$X <- io$X
+#' fargs$Y <- io$Y
+#' fargs$K <- ncol(fargs$β)
+#' fargs$link <- fargs$link
+#' fargs$M <- computeMoments(io$X,io$Y)
#' fargs
#' }, N=10, ncores=3)
#' for (i in 1:2)
if (ncores > 1)
{
- cl = parallel::makeCluster(ncores, outfile="")
+ cl <- parallel::makeCluster(ncores, outfile="")
parallel::clusterExport(cl, c("fargs","verbose"), environment())
- list_res = parallel::clusterApplyLB(cl, 1:N, estimParamAtIndex)
+ list_res <- parallel::clusterApplyLB(cl, 1:N, estimParamAtIndex)
parallel::stopCluster(cl)
}
else
- list_res = lapply(1:N, estimParamAtIndex)
+ list_res <- lapply(1:N, estimParamAtIndex)
# De-interlace results: output one list per function
nf <- length(estimParams)