reports/accuracy.R

   1 optimBeta <- function(N, n, p, beta, b, link, ncores)
   2 {
   3   library(morpheus)
   4   res <- multiRun(
   5     list(n=n, p=p, beta=beta, b=b, link=link),
   6     list(
   7       # morpheus
   8       function(fargs) {
   9         library(morpheus)
  10         K <- ncol(fargs$beta)
  11         M <- computeMoments(fargs$X, fargs$Y)
  12         mu <- computeMu(fargs$X, fargs$Y, list(K=K, M=M))
  13         op <- optimParams(fargs$X, fargs$Y, K, fargs$link, M)
  14         x_init <- list(p=rep(1/K,K-1), beta=mu, b=rep(0,K))
  15         res2 <- NULL
  16         tryCatch({
  17           res2 <- do.call(rbind, op$run(x_init))
  18         }, error = function(e) {})
  19         res2
  20       }
  21 #      ,
  22 #      # flexmix
  23 #      function(fargs) {
  24 #        library(flexmix)
  25 #        source("../patch_Bettina/FLXMRglm.R")
  26 #        K <- ncol(fargs$beta)
  27 #        dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
  28 #        res2 <- NULL
  29 #        tryCatch({
  30 #          fm <- flexmix( cbind(V1, 1-V1) ~ .-V1, data=dat, k=K,
  31 #            model = FLXMRglm(family = binomial(link = link)) )
  32 #          p <- mean(fm@posterior[["scaled"]][,1])
  33 #          out <- refit(fm)
  34 #          beta_b <- sapply( seq_len(K), function(i) {
  35 #            as.double( out@components[[1]][[i]][,1] )
  36 #          } )
  37 #          res2 <- rbind(p, beta_b[2:nrow(beta_b),], beta_b[1,])
  38 #        }, error = function(e) {
  39 #          res2 <- NA
  40 #        })
  41 #        res2
  42 #      }
  43     ),
  44     prepareArgs = function(fargs, index) {
  45       library(morpheus)
  46       io = generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$link)
  47       fargs$X = io$X
  48       fargs$Y = io$Y
  49       fargs
  50     }, N=N, ncores=ncores, verbose=TRUE)
  51   p <- c(p, 1-sum(p))
  52   for (i in 1:length(res)) {
  53     for (j in N:1) {
  54       if (is.null(res[[i]][[j]]) || is.na(res[[i]][[j]]))
  55         res[[i]][[j]] <- NULL
  56     }
  57     print(paste("Count valid runs for ",i," = ",length(res[[i]]),sep=""))
  58     res[[i]] <- alignMatrices(res[[i]], ref=rbind(p,beta,b), ls_mode="exact")
  59   }
  60   res
  61 }
  62
  63 # Default values:
  64 link = "logit"
  65 N <- 10
  66 d <- 2
  67 n <- 1e4
  68 ncores <- 1
  69
  70 cmd_args <- commandArgs()
  71 for (arg in cmd_args)
  72 {
  73   if (substr(arg,1,1)!='-') {
  74     spl <- strsplit(arg,'=')[[1]]
  75     if (spl[1] == "nc") {
  76       ncores <- as.integer(spl[2])
  77     } else if (spl[1] == "N") {
  78       N <- as.integer(spl[2])
  79     } else if (spl[1] == "n") {
  80       n <- as.integer(spl[2])
  81     } else if (spl[1] == "d") {
  82       d <- as.integer(spl[2])
  83     } else if (spl[1] == "link") {
  84       link <- spl[2]
  85     }
  86   }
  87 }
  88
  89 if (d == 2) {
  90   p <- .5
  91   b <- c(-.2, .5)
  92   beta <- matrix( c(1,-2, 3,1), ncol=2 )
  93 } else if (d == 5) {
  94   p <- .5
  95   b <- c(-.2, .5)
  96   beta <- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=2 )
  97 } else if (d == 10) {
  98   p <- c(.3, .3)
  99   b <- c(-.2, 0, .5)
 100   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=3 )
 101 }
 102
 103 mr <- optimBeta(N, n, p, beta, b, link, ncores)
 104 mr_params <- list("N"=N, "nc"=ncores, "n"=n, "K"=K, "d"=d, "link"=link,
 105   "p"=c(p,1-sum(p)), "beta"=beta, "b"=b)
 106
 107 save("mr", "mr_params", file=paste("res_",n,"_",d,"_",link,".RData",sep=""))