#' 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)))
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)
"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
},
else if (!is.numeric(θ0$b) || length(θ0$b) != K || any(is.na(θ0$b)))
stop("θ0$b: length K, no NA")
# TODO: stopping condition? N iterations? Delta <= epsilon ?
- for (loop in 1:2)
+ loopMax <- 2
+ for (loop in 1:loopMax)
{
op_res = constrOptim( linArgs(θ0), .self$f, .self$grad_f,
ui=cbind(
rbind( rep(-1,K-1), diag(K-1) ),
matrix(0, nrow=K, ncol=(d+1)*K) ),
ci=c(-1,rep(0,K-1)) )
- W <<- computeW(expArgs(op_res$par))
- print(op_res$value) #debug
- print(expArgs(op_res$par)) #debug
+ if (loop < loopMax) #avoid computing an extra W
+ W <<- computeW(expArgs(op_res$par))
+ #print(op_res$value) #debug
+ #print(expArgs(op_res$par)) #debug
}
expArgs(op_res$par)