-optimBeta <- function(N, n, K, p, beta, b, link, ncores)
+optimBeta <- function(N, n, p, beta, b, link, ncores)
{
library(morpheus)
res <- multiRun(
- list(n=n, p=p, beta=beta, b=b, K=K, link=link),
+ list(n=n, p=p, beta=beta, b=b, link=link),
list(
# morpheus
function(fargs) {
library(morpheus)
- K <- fargs$K
+ K <- ncol(fargs$beta)
M <- computeMoments(fargs$X, fargs$Y)
mu <- computeMu(fargs$X, fargs$Y, list(K=K, M=M))
op <- optimParams(fargs$X, fargs$Y, K, fargs$link, M)
}, error = function(e) {})
res2
}
-# ,
-# # flexmix
-# function(fargs) {
-# library(flexmix)
-# source("../patch_Bettina/FLXMRglm.R")
-# K <- fargs$K
-# dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
-# res2 <- NULL
-# tryCatch({
-# fm <- flexmix( cbind(V1, 1-V1) ~ .-V1, data=dat, k=K,
-# model = FLXMRglm(family = binomial(link = link)) )
-# p <- mean(fm@posterior[["scaled"]][,1])
-# out <- refit(fm)
-# beta_b <- sapply( seq_len(K), function(i) {
-# as.double( out@components[[1]][[i]][,1] )
-# } )
-# res2 <- rbind(p, beta_b[2:nrow(beta_b),], beta_b[1,])
-# }, error = function(e) {
-# res2 <- NA
-# })
-# res2
-# }
+ ,
+ # flexmix
+ function(fargs) {
+ library(flexmix)
+ source("../patch_Bettina/FLXMRglm.R")
+ K <- ncol(fargs$beta)
+ dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
+ res2 <- NULL
+ tryCatch({
+ fm <- flexmix( cbind(V1, 1-V1) ~ .-V1, data=dat, k=K,
+ model = FLXMRglm(family = binomial(link = link)) )
+ p <- mean(fm@posterior[["scaled"]][,1])
+ out <- refit(fm)
+ beta_b <- sapply( seq_len(K), function(i) {
+ as.double( out@components[[1]][[i]][,1] )
+ } )
+ res2 <- rbind(p, beta_b[2:nrow(beta_b),], beta_b[1,])
+ }, error = function(e) {
+ res2 <- NA
+ })
+ res2
+ }
),
prepareArgs = function(fargs, index) {
library(morpheus)
res
}
-#model = binomial; default values:
+# Default values:
link = "logit"
N <- 10
d <- 2
}
if (d == 2) {
- K <- 2
p <- .5
b <- c(-.2, .5)
- beta <- matrix( c(1,-2, 3,1), ncol=K )
+ beta <- matrix( c(1,-2, 3,1), ncol=2 )
} else if (d == 5) {
- K <- 2
p <- .5
b <- c(-.2, .5)
- beta <- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=K )
+ beta <- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=2 )
} else if (d == 10) {
- K <- 3
- p <- c(.3, .3)
- b <- c(-.2, 0, .5)
- 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 )
-} else if (d == 20) {
- K <- 3
p <- c(.3, .3)
b <- c(-.2, 0, .5)
- 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 )
+ 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 )
}
-mr <- optimBeta(N, n, K, p, beta, b, link, ncores)
-mr_params <- list("N"=N, "nc"=ncores, "n"=n, "K"=K, "d"=d, "link"=link,
+mr <- optimBeta(N, n, p, beta, b, link, ncores)
+mr_params <- list("N"=N, "nc"=ncores, "n"=n, "link"=link,
"p"=c(p,1-sum(p)), "beta"=beta, "b"=b)
save("mr", "mr_params", file=paste("res_",n,"_",d,"_",link,".RData",sep=""))