Reintroduce optional arg Mhat

diff --git a/pkg/R/optimParams.R b/pkg/R/optimParams.R
index c1d7fe8..d8e2cf9 100644 (file)
--- a/pkg/R/optimParams.R
+++ b/pkg/R/optimParams.R
@@ -31,7 +31,7 @@
 #' o$f( o$linArgs(par0) )
 #' o$f( o$linArgs(par1) )
 #' @export
-optimParams <- function(X, Y, K, link=c("logit","probit"))
+optimParams <- function(X, Y, K, link=c("logit","probit"), M=NULL)
 {
   # Check arguments
   if (!is.matrix(X) || any(is.na(X)))
@@ -42,9 +42,19 @@ optimParams <- function(X, Y, K, link=c("logit","probit"))
   if (!is.numeric(K) || K!=floor(K) || K < 2)
     stop("K: integer >= 2")
 
+  if (is.null(M))
+  {
+    # Precompute empirical moments
+    Mtmp <- computeMoments(X, Y)
+    M1 <- as.double(Mtmp[[1]])
+    M2 <- as.double(Mtmp[[2]])
+    M3 <- as.double(Mtmp[[3]])
+    M <- c(M1, M2, M3)
+  }
+
   # Build and return optimization algorithm object
   methods::new("OptimParams", "li"=link, "X"=X,
-    "Y"=as.integer(Y), "K"=as.integer(K))
+    "Y"=as.integer(Y), "K"=as.integer(K), "Mhat"=as.double(M))
 }
 
 #' Encapsulated optimization for p (proportions), β and b (regression parameters)
@@ -82,18 +92,14 @@ setRefClass(
       "Check args and initialize K, d, W"
 
       callSuper(...)
-      if (!hasArg("X") || !hasArg("Y") || !hasArg("K") || !hasArg("li"))
+      if (!hasArg("X") || !hasArg("Y") || !hasArg("K")
+        || !hasArg("li") || !hasArg("Mhat"))
+      {
         stop("Missing arguments")
-
-      # Precompute empirical moments
-      M <- computeMoments(X, Y)
-      M1 <- as.double(M[[1]])
-      M2 <- as.double(M[[2]])
-      M3 <- as.double(M[[3]])
-      Mhat <<- c(M1, M2, M3)
+      }
 
       n <<- nrow(X)
-      d <<- length(M1)
+      d <<- ncol(X)
       W <<- diag(d+d^2+d^3) #initialize at W = Identity
     },
 

diff --git a/reports/accuracy.R b/reports/accuracy.R
index ee08078..fd22a31 100644 (file)
--- a/reports/accuracy.R
+++ b/reports/accuracy.R
@@ -1,62 +1,63 @@
 optimBeta <- function(N, n, K, p, beta, b, link, ncores)
 {
-       library(morpheus)
-       res <- multiRun(
-               list(n=n, p=p, beta=beta, b=b, K=K, link=link),
-               list(
-                       # morpheus
-                       function(fargs) {
-                               library(morpheus)
-                               K <- fargs$K
-                               mu <- computeMu(fargs$X, fargs$Y, list(K=K))
-        op <- optimParams(fargs$X, fargs$Y, K, fargs$link)
+  library(morpheus)
+  res <- multiRun(
+    list(n=n, p=p, beta=beta, b=b, K=K, link=link),
+    list(
+      # morpheus
+      function(fargs) {
+        library(morpheus)
+        K <- fargs$K
+        M <- computeMoments(fargs$X, fargs$Y)
+        mu <- computeMu(fargs$X, fargs$Y, list(K=K, M=M))
+        op <- optimParams(fargs$X, fargs$Y, K, fargs$link, M)
         x_init <- list(p=rep(1/K,K-1), beta=mu, b=rep(0,K))
-                               res2 <- NULL
-                               tryCatch({
+        res2 <- NULL
+        tryCatch({
           res2 <- do.call(rbind, op$run(x_init))
-                               }, error = function(e) {})
-                               res2
-                       }
-#                      ,
-#                      # flexmix
-#                      function(fargs) {
-#                              library(flexmix)
-#                              source("../patch_Bettina/FLXMRglm.R")
-#                              K <- fargs$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
-#                      }
-               ),
-               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$Y
-                       fargs
-               }, N=N, ncores=ncores, verbose=TRUE)
-       p <- c(p, 1-sum(p))
-       for (i in 1:length(res)) {
-               for (j in N:1) {
-                       if (is.null(res[[i]][[j]]) || is.na(res[[i]][[j]]))
-                               res[[i]][[j]] <- NULL
-               }
-               print(paste("Count valid runs for ",i," = ",length(res[[i]]),sep=""))
-               res[[i]] <- alignMatrices(res[[i]], ref=rbind(p,beta,b), ls_mode="exact")
-       }
-       res
+        }, error = function(e) {})
+        res2
+      }
+#      ,
+#      # flexmix
+#      function(fargs) {
+#        library(flexmix)
+#        source("../patch_Bettina/FLXMRglm.R")
+#        K <- fargs$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
+#      }
+    ),
+    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
+    }, N=N, ncores=ncores, verbose=TRUE)
+  p <- c(p, 1-sum(p))
+  for (i in 1:length(res)) {
+    for (j in N:1) {
+      if (is.null(res[[i]][[j]]) || is.na(res[[i]][[j]]))
+        res[[i]][[j]] <- NULL
+    }
+    print(paste("Count valid runs for ",i," = ",length(res[[i]]),sep=""))
+    res[[i]] <- alignMatrices(res[[i]], ref=rbind(p,beta,b), ls_mode="exact")
+  }
+  res
 }
 
 #model = binomial; default values:
@@ -69,46 +70,46 @@ ncores <- 1
 cmd_args <- commandArgs()
 for (arg in cmd_args)
 {
-       if (substr(arg,1,1)!='-') {
-               spl <- strsplit(arg,'=')[[1]]
-               if (spl[1] == "nc") {
-                       ncores <- as.integer(spl[2])
-               } else if (spl[1] == "N") {
-                       N <- as.integer(spl[2])
-               } else if (spl[1] == "n") {
-                       n <- as.integer(spl[2])
-               } else if (spl[1] == "d") {
-                       d <- as.integer(spl[2])
-               } else if (spl[1] == "link") {
-                       link <- spl[2]
-               }
-       }
+  if (substr(arg,1,1)!='-') {
+    spl <- strsplit(arg,'=')[[1]]
+    if (spl[1] == "nc") {
+      ncores <- as.integer(spl[2])
+    } else if (spl[1] == "N") {
+      N <- as.integer(spl[2])
+    } else if (spl[1] == "n") {
+      n <- as.integer(spl[2])
+    } else if (spl[1] == "d") {
+      d <- as.integer(spl[2])
+    } else if (spl[1] == "link") {
+      link <- spl[2]
+    }
+  }
 }
 
 if (d == 2) {
-       K <- 2
-       p <- .5
-       b <- c(-.2, .5)
-       beta <- matrix( c(1,-2, 3,1), ncol=K )
+  K <- 2
+  p <- .5
+  b <- c(-.2, .5)
+  beta <- matrix( c(1,-2, 3,1), ncol=K )
 } 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 )
+  K <- 2
+  p <- .5
+  b <- c(-.2, .5)
+  beta <- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=K )
 } 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 )
+  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 )
+  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 )
 }
 
 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,
-       "p"=c(p,1-sum(p)), "beta"=beta, "b"=b)
+  "p"=c(p,1-sum(p)), "beta"=beta, "b"=b)
 
 save("mr", "mr_params", file=paste("res_",n,"_",d,"_",link,".RData",sep=""))