multiRun: multiRun

In morpheus: Estimate Parameters of Mixtures of Logistic Regressions

multiRun

Description

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 mu or beta. 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.

Usage

multiRun(
  fargs,
  estimParams,
  prepareArgs = function(x, i) x,
  N = 10,
  ncores = 3,
  agg = lapply,
  verbose = FALSE
)

Arguments

fargs	List of arguments for the estimation functions
estimParams	List of nf function(s) to apply on fargs
prepareArgs	Prepare arguments for the functions inside estimParams
N	Number of runs
ncores	Number of cores for parallel runs (<=1: sequential)
agg	Aggregation method (default: lapply)
verbose	TRUE to indicate runs + methods numbers

Value

A list of nf aggregates of N results (matrices).

Examples

## Not run: 
beta <- matrix(c(1,-2,3,1),ncol=2)

# Bootstrap + computeMu, morpheus VS flexmix
io <- generateSampleIO(n=1000, p=1/2, beta=beta, b=c(0,0), "logit")
mu <- normalize(beta)
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$Y[ind], list(K=fargs$K))
  },
  # flexmix
  function(fargs) {
    library(flexmix)
    ind <- fargs$ind
    K <- fargs$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$X)*K)], ncol=K) )
  } ),
  prepareArgs = function(fargs,index) {
    if (index == 1)
      fargs$ind <- 1:nrow(fargs$X)
    else
      fargs$ind <- sample(1:nrow(fargs$X),replace=TRUE)
    fargs
  }, N=10, ncores=3)
for (i in 1:2)
  res[[i]] <- alignMatrices(res[[i]], ref=mu, ls_mode="exact")

# Monte-Carlo + optimParams from X,Y, morpheus VS flexmix
res <- multiRun(list(n=1000,p=1/2,beta=beta,b=c(0,0),link="logit"), list(
  # morpheus
  function(fargs) {
    library(morpheus)
    K <- fargs$K
    mu <- computeMu(fargs$X, fargs$Y, list(K=fargs$K))
    o <- optimParams(fargs$X, fargs$Y, fargs$K, fargs$link, fargs$M)
    o$run(list(beta=mu))$beta
  },
  # flexmix
  function(fargs) {
    library(flexmix)
    K <- fargs$K
    dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
    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]][2:(1+ncol(fargs$X)),1] ) )
  } ),
  prepareArgs = function(fargs,index) {
    library(morpheus)
    io <- generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$link)
    fargs$X <- io$X
    fargs$Y <- io$Y
    fargs$K <- ncol(fargs$beta)
    fargs$link <- fargs$link
    fargs$M <- computeMoments(io$X,io$Y)
    fargs
  }, N=10, ncores=3)
for (i in 1:2)
  res[[i]] <- alignMatrices(res[[i]], ref=beta, ls_mode="exact")
## End(Not run)

morpheus documentation built on Feb. 16, 2023, 10:01 p.m.