[Robast-commits] r143 - in branches/robast-0.6/pkg/RobLox: . R
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Aug 4 12:39:21 CEST 2008
Author: stamats
Date: 2008-08-04 12:39:21 +0200 (Mon, 04 Aug 2008)
New Revision: 143
Modified:
branches/robast-0.6/pkg/RobLox/DESCRIPTION
branches/robast-0.6/pkg/RobLox/R/colRoblox.R
branches/robast-0.6/pkg/RobLox/R/roblox.R
branches/robast-0.6/pkg/RobLox/R/rowRoblox.R
Log:
adapted to new implementation of class "Estimate" ...
Modified: branches/robast-0.6/pkg/RobLox/DESCRIPTION
===================================================================
--- branches/robast-0.6/pkg/RobLox/DESCRIPTION 2008-08-04 10:36:32 UTC (rev 142)
+++ branches/robast-0.6/pkg/RobLox/DESCRIPTION 2008-08-04 10:39:21 UTC (rev 143)
@@ -1,6 +1,6 @@
Package: RobLox
Version: 0.6.0
-Date: 2008-07-28
+Date: 2008-08-04
Title: Optimally robust influence curves for location and scale
Description: functions for the determination of optimally
robust influence curves in case of normal
Modified: branches/robast-0.6/pkg/RobLox/R/colRoblox.R
===================================================================
--- branches/robast-0.6/pkg/RobLox/R/colRoblox.R 2008-08-04 10:36:32 UTC (rev 142)
+++ branches/robast-0.6/pkg/RobLox/R/colRoblox.R 2008-08-04 10:39:21 UTC (rev 143)
@@ -2,6 +2,7 @@
## Evaluate roblox on columns of a matrix
###############################################################################
colRoblox <- function(x, mean, sd, eps, eps.lower, eps.upper, initial.est, k = 1){
+ call.est <- match.call()
if(missing(x))
stop("'x' is missing with no default")
if(is.data.frame(x))
@@ -12,6 +13,8 @@
stop("'x' has to be a matrix resp. convertable to a matrix by 'as.matrix'
or 'data.matrix'")
- return(rowRoblox(x = t(x), mean = mean, sd = sd, eps = eps, eps.lower = eps.lower,
- eps.upper = eps.upper, initial.est = initial.est, k = k))
+ res <- rowRoblox(x = t(x), mean = mean, sd = sd, eps = eps, eps.lower = eps.lower,
+ eps.upper = eps.upper, initial.est = initial.est, k = k)
+ res at estimate.call <- call.est
+ return(res)
}
Modified: branches/robast-0.6/pkg/RobLox/R/roblox.R
===================================================================
--- branches/robast-0.6/pkg/RobLox/R/roblox.R 2008-08-04 10:36:32 UTC (rev 142)
+++ branches/robast-0.6/pkg/RobLox/R/roblox.R 2008-08-04 10:39:21 UTC (rev 143)
@@ -308,14 +308,14 @@
info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
w = w, biastype = symmetricBias(), normtype = NormType(),
modifyIC = modIC))
- return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst$est, samplesize = length(x), asvar = robEst$asvar,
+ return(new("kStepEstimate", name = "Optimally robust estimate",
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = length(x), asvar = robEst$asvar,
asbias = r*robEst$b, steps = k, pIC = IC1, Infos = Info.matrix))
}else
- return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst$est, samplesize = length(x), asvar = robEst$asvar,
+ return(new("kStepEstimate", name = "Optimally robust estimate",
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = length(x), asvar = robEst$asvar,
asbias = r*robEst$b, steps = k, pIC = NULL, Infos = Info.matrix))
}else{
sqrtn <- sqrt(length(x))
@@ -422,14 +422,14 @@
paste("least favorable contamination: ", round(r/sqrtn, 3), sep = ""),
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
- return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst$est, samplesize = length(x), asvar = robEst$asvar,
+ return(new("kStepEstimate", name = "Optimally robust estimate",
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = length(x), asvar = robEst$asvar,
asbias = r*robEst$b, steps = k, pIC = IC1, Infos = Info.matrix))
}else
- return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst$est, samplesize = length(x), asvar = robEst$asvar,
+ return(new("kStepEstimate", name = "Optimally robust estimate",
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = length(x), asvar = robEst$asvar,
asbias = r*robEst$b, steps = k, pIC = NULL, Infos = Info.matrix))
}
}else{
@@ -488,15 +488,13 @@
w = w, biastype = symmetricBias(), normtype = NormType(),
modifyIC = modIC))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst, samplesize = length(x),
- asvar = as.matrix(A-r^2*b^2),
+ estimate.call = es.call, estimate = robEst,
+ samplesize = length(x), asvar = as.matrix(A-r^2*b^2),
asbias = r*b, steps = k, pIC = IC1, Infos = Info.matrix))
}else
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst, samplesize = length(x),
- asvar = as.matrix(A-r^2*b^2),
+ estimate.call = es.call, estimate = robEst,
+ samplesize = length(x), asvar = as.matrix(A-r^2*b^2),
asbias = r*b, steps = k, pIC = NULL, Infos = Info.matrix))
}else{
sqrtn <- sqrt(length(x))
@@ -566,15 +564,13 @@
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst, samplesize = length(x),
- asvar = as.matrix(A-r^2*b^2),
+ estimate.call = es.call, estimate = robEst,
+ samplesize = length(x), asvar = as.matrix(A-r^2*b^2),
asbias = r*b, steps = k, pIC = IC1, Infos = Info.matrix))
}else
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst, samplesize = length(x),
- asvar = as.matrix(A-r^2*b^2),
+ estimate.call = es.call, estimate = robEst,
+ samplesize = length(x), asvar = as.matrix(A-r^2*b^2),
asbias = r*b, steps = k, pIC = NULL, Infos = Info.matrix))
}
}
@@ -658,15 +654,13 @@
w = w, biastype = symmetricBias(), normtype = NormType(),
modifyIC = modIC))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst$est, samplesize = length(x),
- asvar = as.matrix(robEst$A-r^2*robEst$b^2),
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = length(x), asvar = as.matrix(robEst$A-r^2*robEst$b^2),
asbias = r*robEst$b, steps = k, pIC = IC1, Infos = Info.matrix))
}else
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst$est, samplesize = length(x),
- asvar = as.matrix(robEst$A-r^2*robEst$b^2),
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = length(x), asvar = as.matrix(robEst$A-r^2*robEst$b^2),
asbias = r*robEst$b, steps = k, pIC = NULL, Infos = Info.matrix))
}else{
sqrtn <- sqrt(length(x))
@@ -758,15 +752,13 @@
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst$est, samplesize = length(x),
- asvar = as.matrix(robEst$A-r^2*robEst$b^2),
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = length(x), asvar = as.matrix(robEst$A-r^2*robEst$b^2),
asbias = r*robEst$b, steps = k, pIC = IC1, Infos = Info.matrix))
}else
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate.call = es.call,
- estimate = robEst$est, samplesize = length(x),
- asvar = as.matrix(robEst$A-r^2*robEst$b^2),
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = length(x), asvar = as.matrix(robEst$A-r^2*robEst$b^2),
asbias = r*robEst$b, steps = k, pIC = NULL, Infos = Info.matrix))
}
}
Modified: branches/robast-0.6/pkg/RobLox/R/rowRoblox.R
===================================================================
--- branches/robast-0.6/pkg/RobLox/R/rowRoblox.R 2008-08-04 10:36:32 UTC (rev 142)
+++ branches/robast-0.6/pkg/RobLox/R/rowRoblox.R 2008-08-04 10:39:21 UTC (rev 143)
@@ -72,6 +72,7 @@
## Evaluate roblox on rows of a matrix
###############################################################################
rowRoblox <- function(x, mean, sd, eps, eps.lower, eps.upper, initial.est, k = 1){
+ es.call <- match.call()
if(missing(x))
stop("'x' is missing with no default")
if(is.data.frame(x))
@@ -158,7 +159,8 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate = robEst$est, samplesize = ncol(x), steps = k,
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = ncol(x), steps = k,
pIC = NULL, Infos = Info.matrix))
## we need a class like "list of estimates" to set asvar and asbias consistently ...
# return(new("kStepEstimate", name = "Optimally robust estimate",
@@ -209,7 +211,8 @@
paste("maximum MSE-inefficiency: ", round(ineff[1], 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate = robEst$est, samplesize = ncol(x), steps = k,
+ estimate.call = es.call, estimate = robEst$est, #
+ samplesize = ncol(x), steps = k,
pIC = NULL, Infos = Info.matrix))
## we need a class like "list of estimates" to set asvar and asbias consistently ...
# return(new("kStepEstimate", name = "Optimally robust estimate",
@@ -252,7 +255,8 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate = robEst, samplesize = ncol(x), steps = k,
+ estimate.call = es.call, estimate = robEst,
+ samplesize = ncol(x), steps = k,
pIC = NULL, Infos = Info.matrix))
## we need a class like "list of estimates" to set asvar and asbias consistently ...
# return(new("kStepEstimate", name = "Optimally robust estimate",
@@ -294,7 +298,8 @@
paste("maximum MSE-inefficiency: ", round(ineff[1], 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate = robEst, samplesize = ncol(x), steps = k,
+ estimate.call = es.call, estimate = robEst,
+ samplesize = ncol(x), steps = k,
pIC = NULL, Infos = Info.matrix))
## we need a class like "list of estimates" to set asvar and asbias consistently ...
# return(new("kStepEstimate", name = "Optimally robust estimate",
@@ -342,7 +347,8 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate = robEst$est, samplesize = ncol(x), steps = k,
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = ncol(x), steps = k,
pIC = NULL, Infos = Info.matrix))
## we need a class like "list of estimates" to set asvar and asbias consistently ...
# return(new("kStepEstimate", name = "Optimally robust estimate",
@@ -387,7 +393,8 @@
paste("maximum MSE-inefficiency: ", round(ineff[1], 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(new("kStepEstimate", name = "Optimally robust estimate",
- estimate = robEst$est, samplesize = ncol(x), steps = k,
+ estimate.call = es.call, estimate = robEst$est,
+ samplesize = ncol(x), steps = k,
pIC = NULL, Infos = Info.matrix))
## we need a class like "list of estimates" to set asvar and asbias consistently ...
# return(new("kStepEstimate", name = "Optimally robust estimate",
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