[Gmm-commits] r49 - in pkg/gmm: . R inst/doc
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Apr 12 18:58:13 CEST 2012
Author: chaussep
Date: 2012-04-12 18:58:12 +0200 (Thu, 12 Apr 2012)
New Revision: 49
Modified:
pkg/gmm/DESCRIPTION
pkg/gmm/NEWS
pkg/gmm/R/FinRes.R
pkg/gmm/R/Methods.gmm.R
pkg/gmm/R/getModel.R
pkg/gmm/R/gmm.R
pkg/gmm/R/momentEstim.R
pkg/gmm/inst/doc/gmm_with_R.pdf
Log:
Cleaned the codes
Modified: pkg/gmm/DESCRIPTION
===================================================================
--- pkg/gmm/DESCRIPTION 2012-04-10 16:22:36 UTC (rev 48)
+++ pkg/gmm/DESCRIPTION 2012-04-12 16:58:12 UTC (rev 49)
@@ -1,6 +1,6 @@
Package: gmm
Version: 1.4-0
-Date: 2011-11-30
+Date: 2012-04-12
Title: Generalized Method of Moments and Generalized Empirical
Likelihood
Author: Pierre Chausse <pchausse at uwaterloo.ca>
Modified: pkg/gmm/NEWS
===================================================================
--- pkg/gmm/NEWS 2012-04-10 16:22:36 UTC (rev 48)
+++ pkg/gmm/NEWS 2012-04-12 16:58:12 UTC (rev 49)
@@ -25,6 +25,8 @@
and avoid NA's when computing log(1-lambda'gt).
o Sometimes, problems happen in GMM estimation because of the bad first step estimates used to compute the weighting matrix.
The first step estimates are usually computed using the identity matrix. The vector is now printed for better control.
+o Cleaned the codes. The data are in object$dat and we can get the moment matrix by calling gt <- object$g(object$coef,object$dat) for linear and
+ non-linear models, where object is of class gmm.
Changes in version 1.3-8
Modified: pkg/gmm/R/FinRes.R
===================================================================
--- pkg/gmm/R/FinRes.R 2012-04-10 16:22:36 UTC (rev 48)
+++ pkg/gmm/R/FinRes.R 2012-04-12 16:58:12 UTC (rev 49)
@@ -21,23 +21,12 @@
FinRes.baseGmm.res <- function(z, object, ...)
{
P <- object
- if(!is.null(object$gform))
- {
- dat <- z$dat
- x <- dat$x
- }
- else
- x <- z$x
+ x <- z$dat
+ n <- ifelse(is.null(nrow(z$gt)),length(z$gt),nrow(z$gt))
- n <- z$n
- gradv <- z$gradv
+ G <- z$G
iid <- z$iid
- if(P$gradvf)
- G <- gradv(z$coefficients, x)
- else
- G <- gradv(z$coefficients, x, g = object$g)
-
if (P$vcov == "iid")
{
v <- iid(z$coefficients, x, z$g, P$centeredVcov)
@@ -94,7 +83,6 @@
dimnames(z$vcov) <- list(names(z$coefficients), names(z$coefficients))
z$call <- P$call
-
if(is.null(P$weightsMatrix))
{
if(P$wmatrix == "ident")
@@ -112,7 +100,6 @@
z$weightsMatrix <- P$weightsMatrix
z$infVcov <- P$vcov
z$infWmatrix <- P$wmatrix
- z$G <- G
z$met <- P$type
z$kernel <- P$kernel
z$coefficients <- c(z$coefficients)
Modified: pkg/gmm/R/Methods.gmm.R
===================================================================
--- pkg/gmm/R/Methods.gmm.R 2012-04-10 16:22:36 UTC (rev 48)
+++ pkg/gmm/R/Methods.gmm.R 2012-04-12 16:58:12 UTC (rev 49)
@@ -170,7 +170,7 @@
{
if (is(x, "function"))
{
- gmat <- x(y, theta)
+ gmat <- x(theta, y)
return(gmat)
}
else
Modified: pkg/gmm/R/getModel.R
===================================================================
--- pkg/gmm/R/getModel.R 2012-04-10 16:22:36 UTC (rev 48)
+++ pkg/gmm/R/getModel.R 2012-04-12 16:58:12 UTC (rev 49)
@@ -42,8 +42,12 @@
object$type <- "One step GMM with fixed W"
}
object$gform<-object$g
- g <- function(tet, x, ny = dat$ny, nh = dat$nh, k = dat$k)
+ g <- function(tet, dat)
{
+ x <- dat$x
+ ny <- dat$ny
+ nh <- dat$nh
+ k <- dat$k
tet <- matrix(tet, ncol = k)
e <- x[,1:ny] - x[,(ny+1):(ny+k)] %*% t(tet)
gt <- e * x[, ny+k+1]
@@ -51,14 +55,17 @@
for (i in 2:nh) gt <- cbind(gt, e*x[, (ny+k+i)])
return(gt)
}
- gradv <- function(tet, x, ny = dat$ny, nh = dat$nh, k = dat$k, g = NULL)
+ gradv <- function(dat)
{
- a <- g
- tet <- NULL
+ x <- dat$x
+ ny <- dat$ny
+ nh <- dat$nh
+ k <- dat$k
dgb <- -(t(x[,(ny+k+1):(ny+k+nh)]) %*% x[,(ny+1):(ny+k)]) %x% diag(rep(1,ny))/nrow(x)
return(dgb)
}
object$g <- g
+ object$x <- dat
}
else
{
@@ -85,7 +92,7 @@
{
gt <- g(thet,x)
if(centeredVcov) gt <- residuals(lm(gt~1))
- n <- ifelse(is.null(nrow(x)), length(x), nrow(x))
+ n <- ifelse(is.null(nrow(gt)), length(gt), nrow(gt))
v <- crossprod(gt,gt)/n
return(v)
}
Modified: pkg/gmm/R/gmm.R
===================================================================
--- pkg/gmm/R/gmm.R 2012-04-10 16:22:36 UTC (rev 48)
+++ pkg/gmm/R/gmm.R 2012-04-12 16:58:12 UTC (rev 49)
@@ -125,8 +125,12 @@
}
-.tetlin <- function(x, w, ny, nh, k, gradv, g, type=NULL, inv=TRUE)
+.tetlin <- function(dat, w, gradv, g, type=NULL, inv=TRUE)
{
+ x <- dat$x
+ ny <- dat$ny
+ nh <- dat$nh
+ k <- dat$k
n <- nrow(x)
ym <- as.matrix(x[,1:ny])
xm <- as.matrix(x[,(ny+1):(ny+k)])
@@ -147,7 +151,7 @@
else
{
par <- c(t(par))
- g2sls <- g(par, x, ny, nh, k)
+ g2sls <- g(par, dat)
w <- crossprod(g2sls)/n
gb <- matrix(colMeans(g2sls), ncol = 1)
value <- crossprod(gb, solve(w, gb))
@@ -168,7 +172,7 @@
whx <- w%*% (crossprod(hm, xm) %x% diag(ny))
wvecyh <- w%*%matrix(crossprod(ym, hm), ncol = 1)
}
- dg <- gradv(NULL,x, ny, nh, k)
+ dg <- gradv(dat)
xx <- crossprod(dg, whx)
par <- solve(xx, crossprod(dg, wvecyh))
}
@@ -185,7 +189,7 @@
else
par <- solve(crossprod(hm,xm),crossprod(hm,ym)) }
}
- gb <- matrix(colSums(g(par, x, ny, nh, k))/n, ncol = 1)
+ gb <- matrix(colSums(g(par, dat))/n, ncol = 1)
if(inv)
value <- crossprod(gb, solve(w, gb))
else
Modified: pkg/gmm/R/momentEstim.R
===================================================================
--- pkg/gmm/R/momentEstim.R 2012-04-10 16:22:36 UTC (rev 48)
+++ pkg/gmm/R/momentEstim.R 2012-04-12 16:58:12 UTC (rev 49)
@@ -148,12 +148,17 @@
else
names(z$coefficients) <- names(P$t0)
- z$x <- P$x
+ if(P$gradvf)
+ z$G <- P$gradv(z$coefficients, P$x)
+ else
+ z$G <- P$gradv(z$coefficients, P$x, g = P$g)
+
+ z$dat <- P$x
z$gt <- P$g(z$coefficients, P$x)
z$gradv <- P$gradv
z$iid <- P$iid
z$g <- P$g
-
+
class(z) <- paste(P$TypeGmm,".res",sep="")
return(z)
}
@@ -162,10 +167,7 @@
{
P <- object
g <- P$g
- if (is.null(P$data))
- dat <- getDat(P$gform, P$x)
- else
- dat <- getDat(P$gform, P$x, P$data)
+ dat <- P$x
x <- dat$x
k <- dat$k
@@ -177,42 +179,40 @@
if (q == k2 | P$wmatrix == "ident")
{
w <- diag(q)
- res <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, P$g)
+ res <- .tetlin(dat, w, P$gradv, P$g)
z = list(coefficients = res$par, objective = res$value, dat = dat, k = k, k2 = k2, n = n, q = q, df = df)
}
else
{
if (P$vcov == "iid")
{
- res2 <- .tetlin(x, diag(q), dat$ny, dat$nh, dat$k, P$gradv, P$g, type="2sls")
+ res2 <- .tetlin(dat, diag(q), P$gradv, P$g, type="2sls")
initTheta <- NULL
}
if (P$vcov == "HAC")
{
- res1 <- .tetlin(x, diag(q), dat$ny, dat$nh, dat$k, P$gradv, P$g, type="2sls")
+ res1 <- .tetlin(dat, diag(q), P$gradv, P$g, type="2sls")
initTheta <- res1$par
if(P$centeredVcov)
- gmat <- lm(g(res1$par, x)~1)
+ gmat <- lm(g(res1$par, dat)~1)
else
{
- gmat <- g(res1$par, x)
+ gmat <- g(res1$par, dat)
class(gmat) <- "gmmFct"
}
w <- kernHAC(gmat, kernel = P$kernel, bw = P$bw, prewhite = P$prewhite,
ar.method = P$ar.method, approx = P$approx, tol = P$tol, sandwich = FALSE)
- res2 <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g)
+ res2 <- .tetlin(dat, w, P$gradv, g)
}
-
z = list(coefficients = res2$par, objective = res2$value, dat=dat, k=k, k2=k2, n=n, q=q, df=df, initTheta = initTheta)
}
- z$gt <- g(z$coefficients, x)
+ z$gt <- g(z$coefficients, dat)
b <- z$coefficients
y <- as.matrix(model.response(dat$mf, "numeric"))
ny <- dat$ny
b <- t(matrix(b, nrow = dat$ny))
x <- as.matrix(model.matrix(dat$mt, dat$mf, NULL))
yhat <- x %*% b
- z$dat <- dat
z$fitted.values <- yhat
z$residuals <- y - yhat
z$terms <- dat$mt
@@ -222,6 +222,8 @@
z$gradv <- P$gradv
z$iid <- P$iid
z$g <- P$g
+ z$G <- P$gradv(dat)
+
namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k)])
nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh)])
@@ -249,11 +251,8 @@
{
P <- object
g <- P$g
- if (is.null(P$data))
- dat <- getDat(P$gform, P$x)
- else
- dat <- getDat(P$gform, P$x, P$data)
+ dat <- P$x
x <- dat$x
k <- dat$k
k2 <- k*dat$ny
@@ -264,13 +263,13 @@
if (q == k2 | P$wmatrix == "ident")
{
w <- diag(q)
- res <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g)
+ res <- .tetlin(dat, w, P$gradv, g)
z = list(coefficients = res$par, objective = res$value, dat = dat, k = k, k2 = k2, n = n, q = q, df = df)
}
else
{
w <- diag(q)
- res <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g, type="2sls")
+ res <- .tetlin(dat, w, P$gradv, g, type="2sls")
initTheta <- res$par
ch <- 100000
j <- 1
@@ -280,10 +279,10 @@
if (P$vcov == "HAC")
{
if (P$centeredVcov)
- gmat <- lm(g(tet, x)~1)
+ gmat <- lm(g(tet, dat)~1)
else
{
- gmat <- g(tet, x)
+ gmat <- g(tet, dat)
class(gmat) <- "gmmFct"
}
if (j==1)
@@ -292,7 +291,7 @@
ar.method = P$ar.method, tol = P$tol)
w <- vcovHAC(gmat, weights = fixedKernW, sandwich = FALSE)
}
- res <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g)
+ res <- .tetlin(dat, w, P$gradv, g)
ch <- crossprod(abs(tet- res$par)/tet)^.5
if (j>P$itermax)
{
@@ -305,14 +304,13 @@
}
z = list(coefficients = res$par, objective = res$value, dat=dat, k=k, k2=k2, n=n, q=q, df=df, initTheta=initTheta)
}
- z$gt <- g(z$coefficients, x)
+ z$gt <- g(z$coefficients, dat)
b <- z$coefficients
y <- as.matrix(model.response(dat$mf, "numeric"))
ny <- dat$ny
b <- t(matrix(b, nrow = dat$ny))
x <- as.matrix(model.matrix(dat$mt, dat$mf, NULL))
yhat <- x %*% b
- z$dat <- dat
z$fitted.values <- yhat
z$residuals <- y - yhat
z$terms <- dat$mt
@@ -322,7 +320,8 @@
z$gradv <- P$gradv
z$iid <- P$iid
z$g <- P$g
-
+ z$G <- P$gradv(dat)
+
namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k)])
nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh)])
@@ -493,7 +492,12 @@
else
names(z$coefficients) <- names(P$t0)
- z$x <- P$x
+ if(P$gradvf)
+ z$G <- P$gradv(z$coefficients, P$x)
+ else
+ z$G <- P$gradv(z$coefficients, P$x, g = P$g)
+
+ z$dat <- P$x
z$gt <- P$g(z$coefficients, P$x)
z$gradv <- P$gradv
z$iid <- P$iid
@@ -509,11 +513,8 @@
fixedKernWeights <- TRUE # to be changed or included as an option in gmm() in future version
P <- object
g <- P$g
- if (is.null(P$data))
- dat <- getDat(P$gform, P$x)
- else
- dat <- getDat(P$gform, P$x, P$data)
+ dat <- P$x
x <- dat$x
k <- dat$k
k2 <- k*dat$ny
@@ -524,15 +525,15 @@
if (q == k2 | P$wmatrix == "ident")
{
w <- diag(q)
- res <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g)
+ res <- .tetlin(dat, w, P$gradv, g)
z = list(coefficients = res$par, objective = res$value, dat = dat, k = k, k2 = k2, n = n, q = q, df = df)
- P$weightMessage <- "No CUE needed because the model if just identified"
+ P$weightMessage <- "No CUE needed because the model is just identified"
}
else
{
if (is.null(P$t0))
{
- P$t0 <- .tetlin(x,diag(q), dat$ny, dat$nh, dat$k, P$gradv, g, type="2sls")$par
+ P$t0 <- .tetlin(dat,diag(q), P$gradv, g, type="2sls")$par
initTheta <- P$t0
if (fixedKernWeights)
P$weightMessage <- "Weights for kernel estimate of the covariance are fixed and based on the first step estimate of Theta"
@@ -550,7 +551,7 @@
if (fixedKernWeights)
{
- gt0 <- g(P$t0,x)
+ gt0 <- g(P$t0,dat)
gt0 <- lm(gt0~1)
P$fixedKernW <- weightsAndrews(gt0, prewhite=P$prewhite,
bw = P$bw, kernel = P$kernel, approx = P$approx,
@@ -559,15 +560,15 @@
if (P$optfct == "optim")
- res2 <- optim(P$t0,.objCue, x = x, P = P, ...)
+ res2 <- optim(P$t0,.objCue, x = dat, P = P, ...)
if (P$optfct == "nlminb")
{
- res2 <- nlminb(P$t0,.objCue, x = x, P = P, ...)
+ res2 <- nlminb(P$t0,.objCue, x = dat, P = P, ...)
res2$value <- res2$objective
}
if (P$optfct == "optimize")
{
- res2 <- optimize(.objCue,P$t0, x = x, P = P, ...)
+ res2 <- optimize(.objCue,P$t0, x = dat, P = P, ...)
res2$par <- res2$minimum
res2$value <- res2$objective
}
@@ -578,7 +579,7 @@
z$algoInfo <- list(convergence = res2$convergence, counts = res2$evaluations, message = res2$message)
}
- z$gt <- g(z$coefficients, x)
+ z$gt <- g(z$coefficients, dat)
b <- z$coefficients
y <- as.matrix(model.response(dat$mf, "numeric"))
ny <- dat$ny
@@ -595,6 +596,8 @@
z$gradv <- P$gradv
z$iid <- P$iid
z$g <- P$g
+ z$G <- P$gradv(dat)
+
z$cue <- list(weights=P$fixedKernW,message=P$weightMessage)
namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k)])
@@ -679,7 +682,12 @@
else
names(z$coefficients) <- names(P$t0)
- z$x <- P$x
+ if(P$gradvf)
+ z$G <- P$gradv(z$coefficients, P$x)
+ else
+ z$G <- P$gradv(z$coefficients, P$x, g = P$g)
+
+ z$dat <- P$x
z$gradv <- P$gradv
z$gt <- P$g(z$coefficients, P$x)
z$iid <- P$iid
@@ -878,11 +886,8 @@
{
P <- object
g <- P$g
- if (is.null(P$data))
- dat <- getDat(P$gform, P$x)
- else
- dat <- getDat(P$gform, P$x, P$data)
+ dat <- P$x
x <- dat$x
k <- dat$k
k2 <- k*dat$ny
@@ -901,10 +906,10 @@
warning("The matrix of weights is not strictly positive definite")
}
- res2 <- .tetlin(x, w, dat$ny, dat$nh, dat$k, P$gradv, g, inv=FALSE)
+ res2 <- .tetlin(dat, w, P$gradv, g, inv=FALSE)
z = list(coefficients = res2$par, objective = res2$value, dat=dat, k=k, k2=k2, n=n, q=q, df=df)
- z$gt <- g(z$coefficients, x)
+ z$gt <- g(z$coefficients, dat)
b <- z$coefficients
y <- as.matrix(model.response(dat$mf, "numeric"))
ny <- dat$ny
@@ -921,7 +926,8 @@
z$gradv <- P$gradv
z$iid <- P$iid
z$g <- P$g
-
+ z$G <- P$gradv(dat)
+
namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k)])
nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh)])
@@ -1020,7 +1026,12 @@
else
names(z$coefficients) <- names(P$t0)
- z$x <- P$x
+ if(P$gradvf)
+ z$G <- P$gradv(z$coefficients, P$x)
+ else
+ z$G <- P$gradv(z$coefficients, P$x, g = P$g)
+
+ z$dat <- P$x
z$gt <- P$g(z$coefficients, P$x)
z$gradv <- P$gradv
z$iid <- P$iid
Modified: pkg/gmm/inst/doc/gmm_with_R.pdf
===================================================================
--- pkg/gmm/inst/doc/gmm_with_R.pdf 2012-04-10 16:22:36 UTC (rev 48)
+++ pkg/gmm/inst/doc/gmm_with_R.pdf 2012-04-12 16:58:12 UTC (rev 49)
@@ -117,634 +117,320 @@
77 0 obj
<< /S /GoTo /D [78 0 R /Fit ] >>
endobj
-93 0 obj <<
-/Length 3298
+81 0 obj <<
+/Length 3317
/Filter /FlateDecode
>>
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[TRUNCATED]
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