[Gmm-commits] r39 - in pkg/gmm: . R inst/doc man

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Mon Jun 6 19:20:57 CEST 2011 

Author: chaussep
Date: 2011-06-06 19:20:52 +0200 (Mon, 06 Jun 2011)
New Revision: 39

Modified:
   pkg/gmm/NEWS
   pkg/gmm/R/Methods.gmm.R
   pkg/gmm/R/momentEstim.R
   pkg/gmm/inst/doc/gmm_with_R.pdf
   pkg/gmm/man/gmm.Rd
Log:
gradv is now used to compute the gradiant for optim

Modified: pkg/gmm/NEWS
===================================================================
--- pkg/gmm/NEWS	2011-06-03 15:36:02 UTC (rev 38)
+++ pkg/gmm/NEWS	2011-06-06 17:20:52 UTC (rev 39)
@@ -1,10 +1,13 @@
 Changes in version 1.3-7
 
 o Until now there was no way to know whether optim or nlminb has converged in case of nonlinear GMM. The convergence code
-  is now printed by print.gmm and pring.summary.gmm  
+  is now printed by print.gmm and the message and function evaluation by print.summary.gmm  
 o For cases in which the weighting matrix is provided, you have the choice of assuming that it is just a fixed matrix
   not necessary equal to the inverse of the covariance matrix of the moment conditions (the default) or setting 
   vcov="TrueFixed" if it is the inverse of the estimated covariance matrix.
+o If gradv is provided, it is not only used to compute the covariance matrix. It is also used to compute the 
+  gradiant of the objective function when the method is set to "CG" or "BFGS" in optim. For CUE, it is only used for the 
+  first step estimate because the gradiant can hardly be obtained analytically in that case.
 
 
 Changes in version 1.3-6

Modified: pkg/gmm/R/Methods.gmm.R
===================================================================
--- pkg/gmm/R/Methods.gmm.R	2011-06-03 15:36:02 UTC (rev 38)
+++ pkg/gmm/R/Methods.gmm.R	2011-06-06 17:20:52 UTC (rev 39)
@@ -26,7 +26,7 @@
     	dimnames(ans$coefficients) <- list(names(z$coefficients), 
         c("Estimate", "Std. Error", "t value", "Pr(>|t|)"))
 	ans$stest <- specTest(z)
-        ans$convergence <- z$convergence
+        ans$algoInfo <- z$algoInfo
 	class(ans) <- "summary.gmm"
 	ans
 	}
@@ -45,8 +45,20 @@
 	print.default(format(x$stest$test, digits=digits),
                       print.gap = 2, quote = FALSE)
 	cat("\n")
-	if(!is.null(x$convergence))
-		cat("Convergence code = ", x$convergence,"\n")
+	if(!is.null(x$algoInfo))
+		{	
+		cat("#############\n")
+ 		cat("Information related to the numerical optimization\n")
+		}
+	if(!is.null(x$algoInfo$convergence))
+		cat("Convergence code = ", x$algoInfo$convergence,"\n")
+	if(!is.null(x$algoInfo$counts))
+		{	
+		cat("Function eval. = ",x$algoInfo$counts[1],"\n")
+		cat("Gradian eval. = ",x$algoInfo$counts[2],"\n")
+		}	
+	if(!is.null(x$algoInfo$message))
+		cat("Message: ",x$algoInfo$message,"\n")
 	invisible(x)
 	}
 
@@ -95,8 +107,8 @@
 	print.default(format(coef(x), digits=digits),
                       print.gap = 2, quote = FALSE)
 	cat("\n")
-	if(!is.null(x$convergence))
-		cat("Convergence code = ", x$convergence,"\n")
+	if(!is.null(x$algoInfo$convergence))
+		cat("Convergence code = ", x$algoInfo$convergence,"\n")
 	invisible(x)
 	}
 

Modified: pkg/gmm/R/momentEstim.R
===================================================================
--- pkg/gmm/R/momentEstim.R	2011-06-03 15:36:02 UTC (rev 38)
+++ pkg/gmm/R/momentEstim.R	2011-06-06 17:20:52 UTC (rev 39)
@@ -37,7 +37,27 @@
   w=diag(q)
   if (P$optfct == "optim")
     {
-    res <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
+    if (P$gradvf)
+      {
+      gradvOptim <- P$gradv
+      gr2 <- function(thet, x,  w, gf, INV)
+		{
+		gt <- gf(thet, x)
+		Gbar <- gradvOptim(thet, x) 
+		gbar <- as.vector(colMeans(gt))
+		if (INV)		
+		  	obj <- crossprod(Gbar, solve(w, gbar))
+		else
+			obj <- crossprod(Gbar,w)%*%gbar
+		return(obj*2)
+		}
+      argDots <- list(...)
+      allArgOptim <- list(par = P$t0, fn = .obj1, gr = gr2, x = P$x, w = w, gf = P$g, INV = TRUE)
+      allArgOptim <- c(allArgOptim,argDots)
+      res <- do.call(optim,allArgOptim)
+      }
+    else
+      res <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
     }
   if (P$optfct == "nlminb")
     {
@@ -53,8 +73,10 @@
   if (q == k2 | P$wmatrix == "ident")
     {
     z = list(coefficients = res$par, objective = res$value, k=k, k2=k2, n=n, q=q, df=df)	
-    if (P$optfct != "optimize")
-	z$convergence = res$convergence
+    if (P$optfct == "optim")
+	z$algoInfo <- list(convergence = res$convergence, counts = res$counts, message = res$message)
+    else if(P$optfct == "nlminb")
+	z$algoInfo <- list(convergence = res$convergence, counts = res$evaluations, message = res$message)
     }
   else
     {
@@ -76,7 +98,30 @@
      }
 
     if (P$optfct == "optim")
-      res2 <- optim(res$par, .obj1, x = P$x, w = w, gf = P$g, ...)
+      {
+      argDots <- list(...)
+      if (P$gradvf)
+        {
+        gradvOptim <- P$gradv
+        gr2 <- function(thet, x,  w, gf, INV)
+		{
+		gt <- gf(thet, x)
+		Gbar <- gradvOptim(thet, x) 
+		gbar <- as.vector(colMeans(gt))
+		if (INV)		
+		  	obj <- crossprod(Gbar, solve(w, gbar))
+		else
+			obj <- crossprod(Gbar,w)%*%gbar
+		return(obj*2)
+		}
+	argDots <- list(...)
+        allArgOptim <- list(par = res$par, fn = .obj1, gr = gr2, x = P$x, w = w, gf = P$g, INV = TRUE)
+        allArgOptim <- c(allArgOptim,argDots)
+        res2 <- do.call(optim,allArgOptim)
+        }
+      else
+        res2 <- optim(res$par, .obj1, x = P$x, w = w, gf = P$g, ...)
+      }
 
     if (P$optfct == "nlminb")
       {
@@ -91,10 +136,11 @@
       res2$value <- res2$objective
       }	
 
-     z = list(coefficients = res2$par, objective = res2$value, k=k, k2=k2, n=n, q=q, df=df)	
-     if (P$optfct != "optimize")
-	z$convergence = res2$convergence
-
+    z = list(coefficients = res2$par, objective = res2$value, k=k, k2=k2, n=n, q=q, df=df)	
+    if (P$optfct == "optim")
+	z$algoInfo <- list(convergence = res2$convergence, counts = res2$counts, message = res2$message)
+    else if(P$optfct == "nlminb")
+	z$algoInfo <- list(convergence = res2$convergence, counts = res2$evaluations, message = res2$message)
     }
 
   if(is.null(names(P$t0)))
@@ -308,7 +354,30 @@
   df <- q - k
   w=diag(q)
   if (P$optfct == "optim")
-    res <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
+    {
+    if (P$gradvf)
+        {
+        gradvOptim <- P$gradv
+        gr2 <- function(thet, x,  w, gf, INV)
+		{
+		gt <- gf(thet, x)
+		Gbar <- gradvOptim(thet, x) 
+		gbar <- as.vector(colMeans(gt))
+		if (INV)		
+		  	obj <- crossprod(Gbar, solve(w, gbar))
+		else
+			obj <- crossprod(Gbar,w)%*%gbar
+		return(obj*2)
+		}
+      argDots <- list(...)
+      allArgOptim <- list(par = P$t0, fn = .obj1, gr = gr2, x = P$x, w = w, gf = P$g, INV = TRUE)
+      allArgOptim <- c(allArgOptim,argDots)
+      res <- do.call(optim,allArgOptim)
+      }
+    else
+      res <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
+    }
+    
   if (P$optfct == "nlminb")
     {
     res <- nlminb(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
@@ -324,8 +393,10 @@
   if (q == k2 | P$wmatrix == "ident")
     {
     z <- list(coefficients = res$par, objective = res$value, k=k, k2=k2, n=n, q=q, df=df)
-    if (P$optfct != "optimize")
-	z$convergence = res$convergence
+    if (P$optfct == "optim")
+	z$algoInfo <- list(convergence = res$convergence, counts = res$counts, message = res$message)
+    else if(P$optfct == "nlminb")
+	z$algoInfo <- list(convergence = res$convergence, counts = res$evaluations, message = res$message)
     }	
   else
     {
@@ -350,7 +421,31 @@
         }
 
       if (P$optfct == "optim")
-        res <- optim(tet, .obj1, x = P$x, w = w, gf = P$g, ...)
+        {
+        if (P$gradvf)
+          {
+          gradvOptim <- P$gradv
+          gr2 <- function(thet, x,  w, gf, INV)
+		{
+		gt <- gf(thet, x)
+		Gbar <- gradvOptim(thet, x) 
+		gbar <- as.vector(colMeans(gt))
+		if (INV)		
+		  	obj <- crossprod(Gbar, solve(w, gbar))
+		else
+			obj <- crossprod(Gbar,w)%*%gbar
+		return(obj*2)
+		}
+   	  argDots <- list(...)
+          allArgOptim <- list(par = tet, fn = .obj1, gr = gr2, x = P$x, w = w, gf = P$g, INV = TRUE)
+          argDots$gr <- NULL
+          allArgOptim <- c(allArgOptim,argDots)
+          res <- do.call(optim,allArgOptim)
+          }
+        else
+          res <- optim(tet, .obj1, x = P$x, w = w, gf = P$g, ...)
+        }
+
       if (P$optfct == "nlminb")
         {
         res <- nlminb(tet, .obj1, x = P$x, w = w, gf = P$g, ...)
@@ -371,8 +466,11 @@
         j <- j+1	
       }
     z = list(coefficients = res$par, objective = res$value,k=k, k2=k2, n=n, q=q, df=df)	
-    if (P$optfct != "optimize")
-	z$convergence = res$convergence
+    if (P$optfct == "optim")
+	z$algoInfo <- list(convergence = res$convergence, counts = res$counts, message = res$message)
+    else if(P$optfct == "nlminb")
+	z$algoInfo <- list(convergence = res$convergence, counts = res$evaluations, message = res$message)
+
     }
 
   if(is.null(names(P$t0)))
@@ -491,7 +589,30 @@
   df <- q - k
   w=diag(q)
   if (P$optfct == "optim")
-    res <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
+   {
+   if (P$gradvf)
+      {
+      gradvOptim <- P$gradv
+      gr2 <- function(thet, x,  w, gf, INV)
+		{
+		gt <- gf(thet, x)
+		Gbar <- gradvOptim(thet, x) 
+		gbar <- as.vector(colMeans(gt))
+		if (INV)		
+		  	obj <- crossprod(Gbar, solve(w, gbar))
+		else
+			obj <- crossprod(Gbar,w)%*%gbar
+		return(obj*2)
+		}
+      argDots <- list(...)
+      allArgOptim <- list(par = P$t0, fn = .obj1, gr = gr2, x = P$x, w = w, gf = P$g, INV = TRUE)
+      argDots$gr <- NULL
+      allArgOptim <- c(allArgOptim,argDots)
+      res <- do.call(optim,allArgOptim)
+      }
+    else
+      res <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
+    }
   if (P$optfct == "nlminb")
     {
     res <- nlminb(P$t0, .obj1, x = P$x, w = w, gf = P$g, ...)
@@ -507,16 +628,20 @@
   if (q == k2 | P$wmatrix == "ident")
     {
     z <- list(coefficients = res$par, objective = res$value, k=k, k2=k2, n=n, q=q, df=df)
-    if (P$optfct != "optimize")
-	z$convergence = res$convergence
+    if (P$optfct == "optim")
+	z$algoInfo <- list(convergence = res$convergence, counts = res$counts, message = res$message)
+    else if(P$optfct == "nlminb")
+	z$algoInfo <- list(convergence = res$convergence, counts = res$evaluations, message = res$message)
     }	
   else
     {
     if (P$optfct == "optim")
-      res2 <- optim(P$t0, .objCue, x = x, P = P, ...)
+      {
+      res2 <- optim(res$par, .objCue, x = x, P = P, ...)
+      }
     if (P$optfct == "nlminb")
       {
-      res2 <- nlminb(P$t0, .objCue, x = x, P = P, ...)
+      res2 <- nlminb(res$par, .objCue, x = x, P = P, ...)
       res2$value <- res2$objective
       }
     if (P$optfct == "optimize")
@@ -526,8 +651,11 @@
       res2$value <- res2$objective
       }
     z = list(coefficients=res2$par,objective=res2$value, k=k, k2=k2, n=n, q=q, df=df)	
-    if (P$optfct != "optimize")
-	z$convergence = res2$convergence
+    if (P$optfct == "optim")
+	z$algoInfo <- list(convergence = res2$convergence, counts = res2$counts, message = res2$message)
+    else if(P$optfct == "nlminb")
+	z$algoInfo <- list(convergence = res2$convergence, counts = res2$evaluations, message = res2$message)
+
     }
 
   if(is.null(names(P$t0)))
@@ -854,7 +982,30 @@
     }
 
   if (P$optfct == "optim")
-    res2 <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, INV = FALSE,  ...)
+    {
+    if (P$gradvf)
+        {
+        gradvOptim <- P$gradv
+        gr2 <- function(thet, x,  w, gf, INV)
+		{
+		gt <- gf(thet, x)
+		Gbar <- gradvOptim(thet, x) 
+		gbar <- as.vector(colMeans(gt))
+		if (INV)		
+		  	obj <- crossprod(Gbar, solve(w, gbar))
+		else
+			obj <- crossprod(Gbar,w)%*%gbar
+		return(obj*2)
+		}
+      argDots <- list(...)
+      allArgOptim <- list(par = P$t0, fn = .obj1, gr = gr2, x = P$x, w = w, gf = P$g, INV = FALSE)
+      argDots$gr <- NULL
+      allArgOptim <- c(allArgOptim,argDots)
+      res2 <- do.call(optim,allArgOptim)
+      }
+    else
+      res2 <- optim(P$t0, .obj1, x = P$x, w = w, gf = P$g, INV = FALSE,  ...)
+    }
 
   if (P$optfct == "nlminb")
     {
@@ -868,9 +1019,12 @@
     res2$value <- res2$objective
     }	
   z = list(coefficients = res2$par, objective = res2$value, k=k, k2=k2, n=n, q=q, df=df)	
-  if (P$optfct != "optimize")
-    z$convergence = res2$convergence
+  if (P$optfct == "optim")
+     z$algoInfo <- list(convergence = res2$convergence, counts = res2$counts, message = res2$message)
+  else if(P$optfct == "nlminb")
+     z$algoInfo <- list(convergence = res2$convergence, counts = res2$evaluations, message = res2$message)
 
+
   if(is.null(names(P$t0)))
     names(z$coefficients) <- paste("Theta[" ,1:k, "]", sep = "")
   else

Modified: pkg/gmm/inst/doc/gmm_with_R.pdf
===================================================================
--- pkg/gmm/inst/doc/gmm_with_R.pdf	2011-06-03 15:36:02 UTC (rev 38)
+++ pkg/gmm/inst/doc/gmm_with_R.pdf	2011-06-06 17:20:52 UTC (rev 39)
@@ -829,22 +829,21 @@
 /ProcSet [ /PDF /Text ]
 >> endobj
 200 0 obj <<
-/Length 1346      
+/Length 1284      
 /Filter /FlateDecode
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To get the complete diff run:
    svnlook diff /svnroot/gmm -r 39

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