| 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="")) |