[Gmm-commits] r17 - in pkg/gmm: . R inst/doc man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Jan 8 18:53:12 CET 2010
Author: chaussep
Date: 2010-01-08 18:53:12 +0100 (Fri, 08 Jan 2010)
New Revision: 17
Modified:
pkg/gmm/DESCRIPTION
pkg/gmm/NEWS
pkg/gmm/R/gel.R
pkg/gmm/R/getModel.R
pkg/gmm/R/momentEstim.R
pkg/gmm/R/specTest.R
pkg/gmm/inst/doc/gmm_with_R.pdf
pkg/gmm/inst/doc/gmm_with_R.rnw
pkg/gmm/man/gel.Rd
pkg/gmm/man/getLamb.Rd
pkg/gmm/man/rho.Rd
Log:
GEL now uses the new structure
Modified: pkg/gmm/DESCRIPTION
===================================================================
--- pkg/gmm/DESCRIPTION 2009-12-22 15:18:34 UTC (rev 16)
+++ pkg/gmm/DESCRIPTION 2010-01-08 17:53:12 UTC (rev 17)
@@ -1,6 +1,6 @@
Package: gmm
-Version: 1.1-3
-Date: 2009-12-15
+Version: 1.3-0
+Date: 2010-01-08
Title: Generalized Method of Moments and Generalized Empirical Likelihood
Author: Pierre Chausse <pierre.chausse at uqam.ca>
Maintainer: Pierre Chausse <pierre.chausse at uqam.ca>
Modified: pkg/gmm/NEWS
===================================================================
--- pkg/gmm/NEWS 2009-12-22 15:18:34 UTC (rev 16)
+++ pkg/gmm/NEWS 2010-01-08 17:53:12 UTC (rev 17)
@@ -1,3 +1,26 @@
+Changes in version 1.3-0
+
+ o The method "getModel", "momentEstim" is now used also for the gel procedure.
+ o The GEL procedure as been modified. For the case in which the option smooth=TRUE is used,
+ the appropriate scaling parameters have been added following Smith(2004) in the estimation, the covariance matrix of the Lagrange
+ multiplier and the tests of over-identifying restrictions. In this case, only the kernels Truncated and Bartlett are available.
+ For the former, the optimal bandwidth of Andrews(91) for Bartlett is used and for the latter, it is the bandwidth proposed
+ for the Parzen. See Smith(2004) for more details. Also, the option vcov as been removed since it is irrelevant for GEL. By setting smooth to
+ FALSE, it is implied that the data are iid.
+
+Changes in version 1.2-0
+
+The following modifications were suggested by the reviewers of JSS. These changes will not be noticed by users. They are intended to make it easier for
+developpers to contribute.
+
+ o The new method specTest computes the specification tests from objects created by "gmm" or "gel"
+ o The structure of the package has been modified in order to make it more flexible. The changes include:
+ o The new method "getModel" prepares the estimation. It creates what is required by the method "momentEstim".
+ o The method "momentEstim" estimates the model defined by "getModel"
+ o The method "FinRes" finalizes the estimation when it is needed. For now, the method is used only by "gmm" to compute the final resutls.
+ o Some bugs are fixed
+ o The Vignette has been modified a little and the errors in the manual corrected.
+
Changes in version 1.1-0
Modified: pkg/gmm/R/gel.R
===================================================================
--- pkg/gmm/R/gel.R 2009-12-22 15:18:34 UTC (rev 16)
+++ pkg/gmm/R/gel.R 2010-01-08 17:53:12 UTC (rev 17)
@@ -11,12 +11,12 @@
# A copy of the GNU General Public License is available at
# http://www.r-project.org/Licenses/
-rho <- function(x, lamb, derive = 0, type = c("EL", "ET", "CUE"), drop = TRUE)
+rho <- function(x, lamb, derive = 0, type = c("EL", "ET", "CUE"), drop = TRUE, k = 1)
{
type <- match.arg(type)
lamb <- matrix(lamb, ncol = 1)
- gml <- x%*%lamb
+ gml <- x%*%lamb*k
ch <- 0
if (derive == 0)
{
@@ -68,17 +68,18 @@
return(rhom)
}
-getLamb <- function(g, tet, x, type = c('EL', 'ET', 'CUE'), tol_lam = 1e-12, maxiterlam = 1000, tol_obj = 1e-7)
+getLamb <- function(g, tet, x, type = c('EL', 'ET', 'CUE'), tol_lam = 1e-12, maxiterlam = 1000, tol_obj = 1e-7, k = 1)
{
type <- match.arg(type)
gt <- g(tet, x)
+
n <- nrow(gt)
tol_cond=1e-12
gb <- colMeans(gt)
khat <- crossprod(gt)/n
lamb0 <- -solve(khat,gb)
+
conv_mes <- "Normal convergence"
-
singular <-0
crit <-1e30
crit0 <- crit
@@ -89,17 +90,18 @@
j <- 1
while ((crit > tol_lam*( 1 + sqrt( crossprod(lamb0) ) ) ) & (j <= maxiterlam))
{
- rho2 <- as.numeric(rho(gt, lamb0, derive = 2, type = type)$rhomat)
- rho1 <- as.numeric(rho(gt, lamb0, derive = 1, type = type)$rhomat)
+ rho2 <- as.numeric(rho(gt, lamb0, derive = 2, type = type, k = k)$rhomat)
+ rho1 <- as.numeric(rho(gt, lamb0, derive = 1, type = type, k = k)$rhomat)
gblam <- colMeans(rho1*gt)
klam <- crossprod(rho2*gt, gt)/n
chklam <- sum(abs(klam))
if (!is.null(gblam0))
dgblam <- crossprod(gblam) - crossprod(gblam0)
- if (is.na(chklam) | chklam == 0 | chklam == Inf | dgblam > 0 | dgblam == Inf | is.na(dgblam) | dcrit < 0)
+ #if (is.na(chklam) | chklam == 0 | chklam == Inf | dgblam > 0 | dgblam == Inf | is.na(dgblam) | dcrit < 0)
+ if (is.na(chklam) | chklam == 0 | chklam == Inf | dgblam == Inf | is.na(dgblam))
{
- lamb1 <- rep(sqrt(1/n), length(lamb0))
+ lamb1 <- rep(0, length(lamb0))
crit <- 0
singular=2
conv_mes <- "The algorithm produced singular system, NaN or Inf"
@@ -109,12 +111,12 @@
if (rcond(klam) > tol_cond)
{
lamb1 <- lamb0 - solve(klam, gblam)
- crit <- sqrt(crossprod(lamb0 - lamb1))
+ crit <- sqrt(crossprod(lamb0 - lamb1))
lamb0 <- lamb1
}
else
{
- lamb1 <- rep(sqrt(1/n) , length(lamb0))
+ lamb1 <- rep(0 , length(lamb0))
crit <- 0
singular <- 2
conv_mes <- "The algorithm produced singular system"
@@ -126,11 +128,12 @@
crit0 <- crit
}
z <- list("lambda" = lamb1, singular = singular, conv_mes = conv_mes)
- if (j > maxiterlam | max(abs(gblam)) > tol_obj)
+ if (j > maxiterlam)
{
singular <- 1
conv_mes <- "No convergence after 'maxiterlam' iterations"
- z$singular <- singular
+ z$singular <- singular
+ stop("Maxiterlam reached.\n Increase it, try other starting values \n or use the option optlam=\"numeric\".")
}
z$obj <- crossprod(gblam)
return(z)
@@ -171,14 +174,13 @@
}
-gel <- function(g, x, tet0, gradv = NULL, smooth = FALSE, type = c("EL", "ET", "CUE", "ETEL"), vcov = c("HAC", "iid"),
- kernel = c("Bartlett", "Parzen", "Truncated", "Tukey-Hanning"), bw = bwAndrews2, approx = c("AR(1)",
+gel <- function(g, x, tet0, gradv = NULL, smooth = FALSE, type = c("EL", "ET", "CUE", "ETEL"),
+ kernel = c("Truncated", "Bartlett"), bw = bwAndrews2, approx = c("AR(1)",
"ARMA(1,1)"), prewhite = 1, ar.method = "ols", tol_weights = 1e-7, tol_lam = 1e-9, tol_obj = 1e-9,
- tol_mom = 1e-9, maxiterlam = 1000, constraint = FALSE, optfct = c("optim", "optimize", "nlminb"),
+ tol_mom = 1e-9, maxiterlam = 100, constraint = FALSE, optfct = c("optim", "optimize", "nlminb"),
optlam = c("iter", "numeric"), model = TRUE, X = FALSE, Y = FALSE, TypeGel = "baseGel", ...)
{
- vcov <- match.arg(vcov)
type <- match.arg(type)
optfct <- match.arg(optfct)
optlam <- match.arg(optlam)
@@ -186,10 +188,10 @@
approx <- match.arg(approx)
kernel <- match.arg(kernel)
- all_args <- list(g = g, x = x, tet0 = tet0, gradv = gradv, smooth = smooth, type = type, vcov = vcov,
+ all_args <- list(g = g, x = x, tet0 = tet0, gradv = gradv, smooth = smooth, type = type,
kernel = kernel, bw = bw, approx = approx, prewhite = prewhite, ar.method = ar.method,
tol_weights = tol_weights, tol_lam = tol_lam, tol_obj = tol_obj, tol_mom = tol_mom,
- maxiterlam = maxiterlam, constraint = constraint, optfct = optfct,
+ maxiterlam = maxiterlam, constraint = constraint, optfct = optfct, weights = weightsAndrews2,
optlam = optlam, model = model, X = X, Y = Y, TypeGel = TypeGel, call = match.call())
class(all_args)<-TypeGel
@@ -210,17 +212,18 @@
}
else
x <- P$x
+
if (P$optlam == "iter")
{
- lamblist <- getLamb(P$g, tet, x, type = P$typel, tol_lam = P$tol_lam, maxiterlam = P$maxiterlam, tol_obj = P$tol_obj)
+ lamblist <- getLamb(P$g, tet, x, type = P$typel, tol_lam = P$tol_lam, maxiterlam = P$maxiterlam, tol_obj = P$tol_obj, k = P$k1/P$k2)
lamb <- lamblist$lambda
gt <- P$g(tet, x)
- pt <- -rho(gt, lamb, type = P$typet, derive = 1)$rhomat/nrow(gt)
+ pt <- -rho(gt, lamb, type = P$typet, derive = 1, k = P$k1/P$k2)$rhomat/nrow(gt)
checkmom <- sum(as.numeric(pt)*gt)
if (lamblist$singular == 0)
- p <- sum(rho(gt, lamb, type = P$typet)$rhomat) + abs(checkmom)/P$tol_mom
+ p <- sum(rho(gt, lamb, type = P$typet, k = P$k1/P$k2)$rhomat) + abs(checkmom)/P$tol_mom
if (lamblist$singular == 1)
- p <- sum(rho(gt, lamb, type = P$typet)$rhomat) + abs(checkmom)/P$tol_mom + lamblist$obj/P$tol_mom
+ p <- sum(rho(gt, lamb, type = P$typet, k = P$k1/P$k2)$rhomat) + abs(checkmom)/P$tol_mom + lamblist$obj/P$tol_mom
if (lamblist$singular == 2)
p <- 1e50*proc.time()[3]
}
@@ -229,7 +232,7 @@
gt <- P$g(tet, x)
rhofct <- function(lamb)
{
- rhof <- -sum(rho(gt, lamb, type = P$typel)$rhomat)
+ rhof <- -sum(rho(gt, lamb, type = P$typel, k = P$k1/P$k2)$rhomat)
return(rhof)
}
if (ncol(gt) > 1)
@@ -241,7 +244,7 @@
rlamb$value <- rlamb$objective
}
lamb <- rlamb$par
- pt <- -rho(gt, lamb, type = P$typet, derive = 1)$rhomat/nrow(gt)
+ pt <- -rho(gt, lamb, type = P$typet, derive = 1, k = P$k1/P$k2)$rhomat/nrow(gt)
checkmom <- sum(as.numeric(pt)*gt)
p <- -rlamb$value + (checkmom)^2/P$tol_mom + (sum(as.numeric(pt)) - 1)^2/P$tol_mom
}
Modified: pkg/gmm/R/getModel.R
===================================================================
--- pkg/gmm/R/getModel.R 2009-12-22 15:18:34 UTC (rev 16)
+++ pkg/gmm/R/getModel.R 2010-01-08 17:53:12 UTC (rev 17)
@@ -127,26 +127,55 @@
{
clname <- paste(class(P), ".mod", sep = "")
P$gform <- NULL
+ if (!is.function(object$gradv))
+ {
+ P$gradv <- .Gf
+ P$gradvf <- FALSE
+ }
+ else
+ {
+ P$gradvf <- TRUE
+ }
+
}
if (P$smooth)
{
+ if(P$kernel == "Truncated")
+ {
+ P$wkernel <- "Bartlett"
+ P$k1 <- 2
+ P$k2 <- 2
+ }
+ if(P$kernel == "Bartlett")
+ {
+ P$wkernel <- "Parzen"
+ P$k1 <- 1
+ P$k2 <- 2/3
+ }
P$g1 <- P$g
rgmm <- gmm(P$g, x, P$tet0, wmatrix = "ident")
- if (is.function(P$weights))
- P$w <- P$weights(P$g(rgmm$coefficients, x), kernel = P$kernel, bw = P$bw, prewhite = P$prewhite,
+
+ P$bwVal <- P$bw(P$g(rgmm$coefficients, x), kernel = P$wkernel, prewhite = P$prewhite,
+ ar.method = P$ar.method, approx = P$approx)
+ P$w <- P$weights(P$g(rgmm$coefficients, x), kernel = P$kernel, bw = P$bwVal, prewhite = P$prewhite,
ar.method = P$ar.method, approx = P$approx, tol = P$tol_weights)
- else
- P$w <- P$weights
- P$sg <- function(thet, x, g1 = P$g1, w = P$w)
+ P$g <- function(thet, x, g1 = P$g1, w = P$w)
{
gf <- g1(thet, x)
- gt <- smoothG(gf, weights = w)$smoothx
+ gt <- smoothG(gf, weights = w)$smoothx
return(gt)
}
- }
+ }
+ else
+ {
+ P$k1 <- 1
+ P$k2 <- 1
+ P$w <- 1
+ P$bwVal <- 1
+ }
class(P) <- clname
return(P)
}
Modified: pkg/gmm/R/momentEstim.R
===================================================================
--- pkg/gmm/R/momentEstim.R 2009-12-22 15:18:34 UTC (rev 16)
+++ pkg/gmm/R/momentEstim.R 2010-01-08 17:53:12 UTC (rev 17)
@@ -454,8 +454,8 @@
names(z$coefficients) <- paste("Theta[" ,1:k, "]", sep = "")
z$x <- P$x
+ z$gradv <- P$gradv
z$gt <- P$g(z$coefficients, P$x)
- z$gradv <- P$gradv
z$iid <- P$iid
z$g <- P$g
@@ -463,7 +463,6 @@
return(z)
}
-
momentEstim.baseGel.modFormula <- function(object, ...)
{
P <- object
@@ -493,7 +492,7 @@
if (P$optlam == "iter")
{
- rlamb <- getLamb(P$g, res$par, x, type = P$typel, tol_lam = P$tol_lam, maxiterlam = P$maxiterlam, tol_obj = P$tol_obj)
+ rlamb <- getLamb(P$g, res$par, x, type = P$typel, tol_lam = P$tol_lam, maxiterlam = P$maxiterlam, tol_obj = P$tol_obj, k = P$k1/P$k2)
z <- list(coefficients = res$par, lambda = rlamb$lam, conv_lambda = rlamb$conv_mes, conv_par = res$convergence)
z$foc_lambda <- rlamb$obj
}
@@ -503,19 +502,19 @@
gt <- P$g(res$par, x)
rhofct <- function(lamb)
{
- rhof <- -sum(rho(gt, lamb, type = P$typel)$rhomat)
+ rhof <- -sum(rho(gt, lamb, type = P$typel, k = P$k1/P$k2)$rhomat)
return(rhof)
}
rlamb <- optim(rep(0, ncol(gt)), rhofct, control = list(maxit = 1000))
z <- list(coefficients = res$par, conv_par = res$convergence, lambda = rlamb$par)
z$conv_lambda = paste("Lambda by optim. Conv. code = ", rlamb$convergence, sep = "")
- rho1 <- as.numeric(rho(gt, z$lambda, derive = 1, type = P$typel)$rhomat)
+ rho1 <- as.numeric(rho(gt, z$lambda, derive = 1, type = P$typel, k = P$k1/P$k2)$rhomat)
z$foc_lambda <- crossprod(colMeans(rho1*gt))
}
z$type <- P$type
z$gt <- P$g(z$coefficients, x)
- rhom <- rho(z$gt, z$lambda, type = P$typet)
- z$pt <- -rho(z$gt, z$lambda, type = P$typet, derive = 1)$rhomat/n
+ rhom <- rho(z$gt, z$lambda, type = P$typet, k = P$k1/P$k2)
+ z$pt <- -rho(z$gt, z$lambda, type = P$typet, derive = 1, k = P$k1/P$k2)$rhomat/n
z$conv_moment <- colSums(as.numeric(z$pt)*z$gt)
z$conv_pt <- sum(as.numeric(z$pt))
z$objective <- sum(as.numeric(rhom$rhomat) - rho(1, 0, type = P$typet)$rhomat)/n
@@ -543,23 +542,20 @@
names(z$badrho) <- "Number_of_bad_rho"
}
- if (!is.function(P$gradv))
- G <- .Gf(z$coefficients, x, P$g)
- else
- G <- P$gradv(z$coefficients, x)
+ G <- P$gradv(z$coefficients, x)
- if (P$vcov == "iid")
- khat <- crossprod(z$gt)
- else
- khat <- HAC(P$g(z$coefficients, x), kernel = P$kernel, bw = P$bw, prewhite = P$prewhite,
- ar.method = P$ar.method, approx = P$approx, tol = P$tol_weights)
+ khat <- crossprod(z$gt)/(n*P$k2)*P$bwVal
+ G <- G/P$k1
kg <- solve(khat, G)
z$vcov_par <- solve(crossprod(G, kg))/n
- z$vcov_lambda <- ((solve(khat) - kg%*%z$vcov_par%*%t(kg)))/n
-
- if (P$smooth) z$weights <- P$w
+ p_temp <- solve(khat,G)
+ z$vcov_lambda <- solve(khat, ( diag(ncol(khat)) - G %*% (z$vcov_par*n) %*% t(p_temp) ))/n*P$bwVal^2
+ z$weights <- P$w
+ z$bwVal <- P$bwVal
+ names(z$bwVal) <- "Bandwidth"
+
dimnames(z$vcov_par) <- list(names(z$coefficients), names(z$coefficients))
dimnames(z$vcov_lambda) <- list(names(z$lambda), names(z$lambda))
b <- z$coefficients
@@ -575,6 +571,9 @@
if(P$X) z$x <- x
if(P$Y) z$y <- y
z$call <- P$call
+ z$k1 <- P$k1
+ z$k2 <- P$k2
+ z$khat <- khat
class(z) <- paste(P$TypeGel, ".res", sep = "")
return(z)
@@ -606,7 +605,7 @@
if (P$optlam == "iter")
{
- rlamb <- getLamb(P$g, res$par, x, type = P$typel, tol_lam = P$tol_lam, maxiterlam = P$maxiterlam, tol_obj = P$tol_obj)
+ rlamb <- getLamb(P$g, res$par, x, type = P$typel, tol_lam = P$tol_lam, maxiterlam = P$maxiterlam, tol_obj = P$tol_obj, k = P$k1/P$k2)
z <- list(coefficients = res$par, lambda = rlamb$lam, conv_lambda = rlamb$conv_mes, conv_par = res$convergence)
z$foc_lambda <- rlamb$obj
}
@@ -616,22 +615,22 @@
gt <- P$g(res$par, x)
rhofct <- function(lamb)
{
- rhof <- -sum(rho(gt, lamb, type = P$typel)$rhomat)
+ rhof <- -sum(rho(gt, lamb, type = P$typel, k = P$k1/P$k2)$rhomat)
return(rhof)
}
rlamb <- optim(rep(0, ncol(gt)), rhofct, control = list(maxit = 1000))
z <- list(coefficients = res$par, conv_par = res$convergence, lambda = rlamb$par)
z$conv_lambda = paste("Lambda by optim. Conv. code = ", rlamb$convergence, sep = "")
- rho1 <- as.numeric(rho(gt, z$lambda, derive = 1, type = P$typel)$rhomat)
+ rho1 <- as.numeric(rho(gt, z$lambda, derive = 1, type = P$typel, k = P$k1/P$k2)$rhomat)
z$foc_lambda <- crossprod(colMeans(rho1*gt))
}
z$type <- P$type
z$gt <- P$g(z$coefficients, x)
- rhom <- rho(z$gt, z$lambda, type = P$typet)
- z$pt <- -rho(z$gt, z$lambda, type = P$typet, derive = 1)$rhomat/n
+ rhom <- rho(z$gt, z$lambda, type = P$typet, k = P$k1/P$k2)
+ z$pt <- -rho(z$gt, z$lambda, type = P$typet, derive = 1, k = P$k1/P$k2)$rhomat/n
z$conv_moment <- colSums(as.numeric(z$pt)*z$gt)
z$conv_pt <- sum(as.numeric(z$pt))
- z$objective <- sum(as.numeric(rhom$rhomat) - rho(1, 0, type = P$typet)$rhomat)/n
+ z$objective <- sum(as.numeric(rhom$rhomat) - rho(1, 0, type = P$typet, k = P$k1/P$k2)$rhomat)/n
if (P$type == "EL")
{
@@ -639,22 +638,23 @@
names(z$badrho) <- "Number_of_bad_rho"
}
- if (!is.function(P$gradv))
- G <- .Gf(z$coefficients, x, P$g)
- else
+ if(P$gradvf)
G <- P$gradv(z$coefficients, x)
- if (P$vcov == "iid")
- khat <- crossprod(z$gt)
else
- khat <- HAC(P$g(z$coefficients, x), kernel = P$kernel, bw = P$bw, prewhite = P$prewhite,
- ar.method = P$ar.method, approx = P$approx, tol = P$tol_weights)
+ G <- P$gradv(z$coefficients, x, g = P$g)
+
+ khat <- crossprod(z$gt)/(n*P$k2)*P$bwVal
+ G <- G/P$k1
kg <- solve(khat, G)
z$vcov_par <- solve(crossprod(G, kg))/n
- z$vcov_lambda <- ((solve(khat) - kg%*%z$vcov_par%*%t(kg)))/n
+ p_temp <- solve(khat,G)
+ z$vcov_lambda <- solve(khat, ( diag(ncol(khat)) - G %*% (z$vcov_par*n) %*% t(p_temp) ))/n*P$bwVal^2
- if (P$smooth) z$weights <- P$w
-
+ z$weights <- P$w
+ z$bwVal <- P$bwVal
+ names(z$bwVal) <- "Bandwidth"
+
names(z$coefficients) <- paste("Theta[",1:P$k,"]", sep = "")
colnames(z$gt) <- paste("gt[",1:ncol(z$gt),"]", sep = "")
names(z$lambda) <- paste("Lambda[",1:ncol(z$gt),"]", sep = "")
@@ -662,6 +662,9 @@
dimnames(z$vcov_lambda) <- list(names(z$lambda), names(z$lambda))
if(P$X) z$x <- x
z$call <- P$call
+ z$k1 <- P$k1
+ z$k2 <- P$k2
+ z$khat <- khat
class(z) <- paste(P$TypeGel, ".res", sep = "")
return(z)
Modified: pkg/gmm/R/specTest.R
===================================================================
--- pkg/gmm/R/specTest.R 2009-12-22 15:18:34 UTC (rev 16)
+++ pkg/gmm/R/specTest.R 2010-01-08 17:53:12 UTC (rev 17)
@@ -26,11 +26,11 @@
specTest.gel <- function(x, ...)
{
n <- nrow(x$gt)
- khat <- crossprod(x$gt)/n
+ khat <- x$khat
gbar <- colMeans(x$gt)
- LR_test <- 2*x$objective*n
- LM_test <- n*crossprod(x$lambda, crossprod(khat, x$lambda))
- J_test <- n*crossprod(gbar, solve(khat, gbar))
+ LR_test <- 2*x$objective*n*x$k2/(x$bwVal*x$k1^2)
+ LM_test <- n*crossprod(x$lambda, crossprod(khat, x$lambda))/(x$bwVal^2)
+ J_test <- n*crossprod(gbar, solve(khat, gbar))/(x$k1^2)
test <- c(LR_test, LM_test, J_test)
df <- (ncol(x$gt) - length(x$par))
ntest <- noquote(paste("Over-identifying restrictions tests: degrees of freedom is ", df, sep = ""))
Modified: pkg/gmm/inst/doc/gmm_with_R.pdf
===================================================================
--- pkg/gmm/inst/doc/gmm_with_R.pdf 2009-12-22 15:18:34 UTC (rev 16)
+++ pkg/gmm/inst/doc/gmm_with_R.pdf 2010-01-08 17:53:12 UTC (rev 17)
@@ -112,34 +112,37 @@
<< /S /GoTo /D [78 0 R /Fit ] >>
endobj
91 0 obj <<
-/Length 3267
+/Length 3260
/Filter /FlateDecode
>>
stream
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+s{
[TRUNCATED]
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svnlook diff /svnroot/gmm -r 17
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