[Robast-commits] r109 - in branches/robast-0.6/pkg/RobLox: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Jul 21 14:39:08 CEST 2008
Author: stamats
Date: 2008-07-21 14:39:08 +0200 (Mon, 21 Jul 2008)
New Revision: 109
Modified:
branches/robast-0.6/pkg/RobLox/R/rlOptIC.R
branches/robast-0.6/pkg/RobLox/R/rlsOptIC_AL.R
branches/robast-0.6/pkg/RobLox/R/roblox.R
branches/robast-0.6/pkg/RobLox/R/rsOptIC.R
branches/robast-0.6/pkg/RobLox/man/roblox.Rd
Log:
adapted RobLox to the changes made in ROptEst
Modified: branches/robast-0.6/pkg/RobLox/R/rlOptIC.R
===================================================================
--- branches/robast-0.6/pkg/RobLox/R/rlOptIC.R 2008-07-21 12:01:18 UTC (rev 108)
+++ branches/robast-0.6/pkg/RobLox/R/rlOptIC.R 2008-07-21 12:39:08 UTC (rev 109)
@@ -10,11 +10,20 @@
A <- sd^2*A1
if(computeIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- 0
+ stand(w) <- as.matrix(A)
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
+
return(generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = 0, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = A - r^2*b^2),
- info = c("rlOptIC", "optimally robust IC for AL estimators and 'asMSE'"))))
+ info = c("rlOptIC", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType())))
}else{
return(list(A = A, a = 0, b = b))
}
Modified: branches/robast-0.6/pkg/RobLox/R/rlsOptIC_AL.R
===================================================================
--- branches/robast-0.6/pkg/RobLox/R/rlsOptIC_AL.R 2008-07-21 12:01:18 UTC (rev 108)
+++ branches/robast-0.6/pkg/RobLox/R/rlsOptIC_AL.R 2008-07-21 12:39:08 UTC (rev 109)
@@ -127,11 +127,20 @@
if(computeIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- c(0, a2)
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
+
return(generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationScaleFamily(mean = mean, sd = sd),
- res = list(A = A, a = a, b = b, d = NULL,
- risk = list(asMSE = mse, asBias = b, asCov = mse - r^2*b^2),
- info = c("rlsOptIC.AL", "optimally robust IC for AL estimators and 'asMSE'"))))
+ res = list(A = as.matrix(A), a = a, b = b, d = NULL,
+ risk = list(asMSE = mse, asBias = b, trAsCov = mse - r^2*b^2),
+ info = c("rlOptIC", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType())))
}else{
return(list(A = A, a = a, b = b))
}
Modified: branches/robast-0.6/pkg/RobLox/R/roblox.R
===================================================================
--- branches/robast-0.6/pkg/RobLox/R/roblox.R 2008-07-21 12:01:18 UTC (rev 108)
+++ branches/robast-0.6/pkg/RobLox/R/roblox.R 2008-07-21 12:39:08 UTC (rev 109)
@@ -242,11 +242,19 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- a/A2
+ stand(w) <- diag(c(A1, A2))
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationScaleFamily(mean = mean, sd = sd),
res = list(A = diag(c(A1, A2)), a = a, b = b, d = NULL,
risk = list(asMSE = mse, asBias = b, asCov = mse - r^2*b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix, "optIC" = IC1),
class = c("ALEstimate", "Estimate")))
}else
@@ -296,11 +304,19 @@
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- a/A2
+ stand(w) <- diag(c(A1, A2))
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationScaleFamily(mean = mean, sd = sd),
res = list(A = diag(c(A1, A2)), a = a, b = b, d = NULL,
risk = list(asMSE = mse, asBias = b, asCov = mse - r^2*b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
Infos(IC1) <- matrix(c(rep("roblox", 3),
paste("radius-minimax IC for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
@@ -341,11 +357,19 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- 0
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = 0, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix, "optIC" = IC1),
class = c("ALEstimate", "Estimate")))
}else
@@ -387,11 +411,19 @@
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- 0
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = 0, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
Infos(IC1) <- matrix(c(rep("roblox", 3),
paste("radius-minimax IC for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
@@ -437,11 +469,19 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- a/A
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormScaleFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = a, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix, "optIC" = IC1),
class = c("ALEstimate", "Estimate")))
}else
@@ -485,11 +525,19 @@
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- a/A
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormScaleFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = a, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
Infos(IC1) <- matrix(c(rep("roblox", 3),
paste("radius-minimax IC for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
Modified: branches/robast-0.6/pkg/RobLox/R/rsOptIC.R
===================================================================
--- branches/robast-0.6/pkg/RobLox/R/rsOptIC.R 2008-07-21 12:01:18 UTC (rev 108)
+++ branches/robast-0.6/pkg/RobLox/R/rsOptIC.R 2008-07-21 12:39:08 UTC (rev 109)
@@ -62,11 +62,20 @@
A <- sd^2*A1
if(computeIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- z
+ stand(w) <- as.matrix(A)
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
+
return(generateIC(neighbor = ContNeighborhood(radius = r),
- L2Fam = NormScaleFamily(mean = mean, sd = sd),
+ L2Fam = NormScaleFamily(sd = sd, mean = mean),
res = list(A = as.matrix(A), a = a, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = A - r^2*b^2),
- info = c("rsOptIC", "optimally robust IC for AL estimators and 'asMSE'"))))
+ info = c("rlOptIC", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType())))
}else{
return(list(A = A, a = a, b = b))
}
Modified: branches/robast-0.6/pkg/RobLox/man/roblox.Rd
===================================================================
--- branches/robast-0.6/pkg/RobLox/man/roblox.Rd 2008-07-21 12:01:18 UTC (rev 108)
+++ branches/robast-0.6/pkg/RobLox/man/roblox.Rd 2008-07-21 12:39:08 UTC (rev 109)
@@ -90,8 +90,7 @@
## amount of gross errors known
res1 <- roblox(x, eps = 0.05, returnIC = TRUE)
-res1$mean
-res1$sd
+res1$estimate
res1$optIC
checkIC(res1$optIC)
Risks(res1$optIC)
@@ -101,8 +100,7 @@
## amount of gross errors unknown
res2 <- roblox(x, eps.lower = 0.01, eps.upper = 0.1, returnIC = TRUE)
-res2$mean
-res2$sd
+res2$estimate
res2$optIC
checkIC(res2$optIC)
Risks(res2$optIC)
@@ -118,10 +116,10 @@
c(median(x), mad(x))
# optimally robust (amount of gross errors known)
-c(res1$mean, res1$sd)
+res1$estimate
# optimally robust (amount of gross errors unknown)
-c(res2$mean, res2$sd)
+res2$estimate
# Kolmogorov(-Smirnov) minimum distance estimator (robust)
(ks.est <- MDEstimator(x, ParamFamily = NormLocationScaleFamily(), distance = KolmogorovDist))
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