reports/accuracy.R

   1 optimBeta <- function(N, n, K, p, beta, b, link, ncores)
   2 {
   3     library(morpheus)
   4     res <- multiRun(
   5         list(n=n,p=p,beta=beta,b=b,optargs=list(K=K,link=link)),
   6         list(
   7             # morpheus
   8             function(fargs) {
   9                 library(morpheus)
  10                 K <- fargs$optargs$K
  11                 M <- computeMoments(fargs$X, fargs$Y)
  12                 fargs$optargs$M <- M
  13                 mu <- computeMu(fargs$X, fargs$Y, fargs$optargs)
  14                 op <- optimParams(K,link,fargs$optargs)
  15                 x_init <- list(p=rep(1/K,K-1), beta=mu, b=rep(0,K))
  16                 do.call(rbind, op$run(x_init))
  17             },
  18             # flexmix
  19             function(fargs) {
  20                 library(flexmix)
  21                 source("../patch_Bettina/FLXMRglm.R")
  22                 K <- fargs$optargs$K
  23                 dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
  24                 fm <- flexmix( cbind(V1, 1-V1) ~ .-V1, data=dat, k=K,
  25                     model = FLXMRglm(family = binomial(link = link)) )
  26                 p <- mean(fm@posterior[["scaled"]][,1])
  27                 out <- refit(fm)
  28                 beta_b <- sapply( seq_len(K), function(i) {
  29                     as.double( out@components[[1]][[i]][,1] )
  30                 } )
  31                 rbind(p, beta_b[2:nrow(beta_b),], beta_b[1,])
  32             } ),
  33         prepareArgs = function(fargs, index) {
  34             library(morpheus)
  35             io = generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$optargs$link)
  36             fargs$X = io$X
  37             fargs$Y = io$Y
  38             fargs$optargs$K = ncol(fargs$beta)
  39             fargs$optargs$M = computeMoments(io$X,io$Y)
  40             fargs
  41         }, N=N, ncores=ncores, verbose=TRUE)
  42     p <- c(p, 1-sum(p))
  43     for (i in 1:2)
  44         res[[i]] <- alignMatrices(res[[i]], ref=rbind(p,beta,b), ls_mode="exact")
  45     res
  46 }
  47
  48 #model = binomial; default values:
  49 link = "logit"
  50 N <- 10
  51 d <- 2
  52 n <- 1e4
  53 ncores <- 1
  54
  55 cmd_args <- commandArgs()
  56 for (arg in cmd_args)
  57 {
  58     if (substr(arg,1,1)!='-') {
  59         spl <- strsplit(arg,'=')[[1]]
  60         if (spl[1] == "nc") {
  61             ncores <- as.integer(spl[2])
  62         } else if (spl[1] == "N") {
  63             N <- as.integer(spl[2])
  64         } else if (spl[1] == "n") {
  65             n <- as.integer(spl[2])
  66         } else if (spl[1] == "d") {
  67             d <- as.integer(spl[2])
  68         } else if (spl[1] == "link") {
  69             link <- spl[2]
  70         }
  71     }
  72 }
  73
  74 if (d == 2) {
  75     K <- 2
  76     p <- .5
  77     b <- c(-.2, .5)
  78     beta <- matrix( c(1,-2, 3,1), ncol=K )
  79 } else if (d == 5) {
  80     K <- 2
  81     p <- .5
  82     b <- c(-.2, .5)
  83     beta <- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=K )
  84 } else if (d == 10) {
  85     K <- 3
  86     p <- c(.3, .3)
  87     b <- c(-.2, 0, .5)
  88     beta <- matrix( c(1,2,-1,0,3,4,-1,-3,0,2, 2,-3,0,1,0,-1,-4,3,2,0, -1,1,3,-1,0,0,2,0,1,-2), ncol=K )
  89 } else if (d == 20) {
  90     K <- 3
  91     p <- c(.3, .3)
  92     b <- c(-.2, 0, .5)
  93     beta <- matrix( c(1,2,-1,0,3,4,-1,-3,0,2,2,-3,0,1,0,-1,-4,3,2,0, -1,1,3,-1,0,0,2,0,1,-2,1,2,-1,0,3,4,-1,-3,0,2, 2,-3,0,1,0,-1,-4,3,2,0,1,1,2,2,-2,-2,3,1,0,0), ncol=K )
  94 }
  95
  96 mr <- optimBeta(N, n, K, p, beta, b, link, ncores)
  97 mr_params <- list("N"=N, "n"=n, "K"=K, "d"=d, "link"=link,
  98     "p"=c(p,1-sum(p)), "beta"=beta, "b"=b)
  99
 100 save("mr", "mr_params", file=paste("multirun_",d,"_",link,".RData",sep=""))