Nothing
R/AllClasses.R
In rrcov: Scalable Robust Estimators with High Breakdown Point
Defines functions
.vrobGK
.mrobTau
setClass("PsiFun", representation(n = "numeric",
p = "numeric",
r = "numeric",
alpha = "numeric",
c1 = "numeric"))
setClass("PsiBwt", representation(M = "numeric"),
contains="PsiFun")
## Define class unions for optional slots, e.g. for definition
## of slots which will be computed on demand, like the
## mahalanobis/robust distances
setClassUnion("Uvector", c("vector", "NULL"))
setClassUnion("Unumeric", c("numeric", "NULL"))
setClassUnion("Cnumeric", c("numeric", "character"))
setClassUnion("Umatrix", c("matrix", "NULL"))
setClassUnion("Ulist", c("list", "NULL"))
setClassUnion("Ufunction", c("function", "character", "NULL"))
setClassUnion("Utable", c("table", "NULL"))
## This is a virtual base class for control objects. Each robust
## method like CovMest, CovOgk, etc. will derive a subclass with
## the necessary control parameters, e.g. CovControlMest will
## contain the control parameters for CovMest.
setClass("CovControl", representation(trace="logical",
tolSolve="numeric",
"VIRTUAL"))
setClassUnion("UCovControl", c("CovControl", "NULL"))
setClass("Cov", representation(call = "language",
cov = "matrix",
center = "vector",
det = "numeric",
n.obs = "numeric",
mah = "Uvector",
flag = "Uvector",
method = "character",
singularity = "Ulist",
X = "Umatrix",
"VIRTUAL"),
prototype=list(det=-1))
setClassUnion("UCov", c("Cov", "NULL"))
setClass("SummaryCov", representation(covobj = "Cov",
evals = "vector"))
setClass("CovClassic", contains="Cov")
setClass("CovRobust", representation(iter="numeric",
crit="numeric",
wt="Uvector",
"VIRTUAL"),
contains="Cov")
setClass("SummaryCovRobust", representation(),
contains="SummaryCov")
setClass("CovMest", representation(vt="vector"),
contains="CovRobust")
setClass("CovMcd", representation(alpha = "numeric",
quan = "numeric",
best = "Uvector",
raw.cov = "matrix",
raw.center = "vector",
raw.mah = "Uvector",
raw.wt = "Uvector",
raw.cnp2 = "numeric",
cnp2 = "numeric"),
contains="CovRobust")
setClass("CovMrcd", representation(alpha = "numeric",
quan = "numeric",
best = "Uvector",
cnp2 = "numeric",
icov = "matrix",
rho = "numeric",
target="matrix"),
contains="CovRobust")
setClass("CovOgk", representation(raw.cov = "matrix",
raw.center = "vector",
raw.mah = "Uvector",
raw.wt = "Uvector"),
contains="CovRobust")
setClass("CovMve", representation(alpha = "numeric",
quan = "numeric",
best = "Uvector",
raw.cov = "matrix",
raw.center = "vector",
raw.mah = "Uvector",
raw.wt = "Uvector",
raw.cnp2 = "numeric",
cnp2 = "numeric"),
contains="CovRobust")
setClass("CovSest", representation(iBest = "numeric",
nsteps = "Uvector",
initHsets = "Umatrix",
cc = "numeric",
kp = "numeric"),
contains="CovRobust")
setClass("CovSde", representation(),
contains="CovRobust")
setClass("CovMMest", representation(c1 ="numeric",
sest = "CovSest"),
contains="CovRobust")
## Control parameters for CovMcd
setClass("CovControlMcd", representation(alpha="numeric",
nsamp="Cnumeric",
scalefn="Ufunction",
maxcsteps="numeric",
seed="Uvector",
use.correction="logical"),
prototype = list(alpha=0.5,
nsamp=500,
scalefn=NULL,
maxcsteps=200,
seed=NULL,
trace=FALSE,
tolSolve=1e-14,
use.correction=TRUE),
contains="CovControl")
## Control parameters for CovMrcd
setClass("CovControlMrcd", representation(alpha="numeric",
h="Unumeric",
maxcsteps="numeric",
rho="Unumeric",
target = "character",
maxcond = "numeric"),
prototype = list(alpha=0.5,
h=NULL,
maxcsteps=200,
rho=NULL,
target="identity",
maxcond=50,
trace=FALSE,
tolSolve=1e-14),
contains="CovControl")
## Control parameters for CovMest
setClass("CovControlMest", representation(r="numeric",
arp="numeric",
eps="numeric",
maxiter="numeric"),
prototype = list(r=0.45,
arp=0.05,
eps=1e-3,
maxiter=120,
trace=FALSE,
tolSolve=1e-14),
contains="CovControl"
)
## Control parameters for CovOgk
##
## Computes robust univariate mu and sigmma of the vector x
## - sigma: tau scale Yohai and Zamar (1988) - a truncated
## standard deviation
## - mu: weighted mean
##
## Returns a vector of length two with the calculated mu and sigma
##
.mrobTau <- function(x, c1 = 4.5, c2 = 3.0, ...) #c2=2.36075
{
return(scaleTau2(x, mu.too=TRUE)) # use scaleTau2 from package robustbase
if(FALSE) {
m0 <- median(x) # MED
s0 <- median(abs(x - m0)) # MAD
r <- abs(x-m0)/s0
wt <- (1 - (r/c1)^2)^2
wt <- ifelse(r <= c1, wt, 0) # wt = weigths w(x,c1)
m <- sum(x*wt)/sum(wt) # mu = weighted mean
r <- (x-m)/s0
r <- r^2
r[r > c2^2] <- c2^2 # rho(x,c2)
s2 <- s0^2 / length(x) * sum(r) # sigma = tau scale (Yohai&Zamar 1988)
# truncated standard deviation
c(m, sqrt(s2))
}
}
##
## Compute a robust estimate of the covariance of two random
## variables x1 and x2.
## Use the estimate defined by Gnanadesikan and Kettenring (1972):
## cov(X,Y)=1/4 * (sigma(X+Y)^2 - sigma(X-Y)^2)
## where sigma is a robust univariate scale.
## As sigma is used the tau scale estimate defined above - mrobTau()
##
.vrobGK <- function(x1, x2, ...)
{
(.mrobTau(x1+x2, ...)[2]^2 - .mrobTau(x1-x2, ...)[2]^2)/4.0
}
setClass("CovControlOgk", representation(niter="numeric",
beta="numeric",
mrob="Ufunction", # mrob=.mrobTau
vrob="Ufunction", # vrob=.vrobGK
smrob="character",
svrob="character"),
prototype = list(niter=2,
beta=0.90,
mrob=NULL,
vrob=.vrobGK,
smrob="scaleTau2",
svrob="gk",
trace=FALSE,
tolSolve=1e-14),
contains="CovControl"
)
## Control parameters for CovMve
setClass("CovControlMve", representation(alpha="numeric",
nsamp="numeric",
seed="Uvector"),
prototype = list(alpha=0.5,
nsamp=500,
seed=NULL,
trace=FALSE,
tolSolve=1e-14),
contains="CovControl")
## Control parameters for CovSest
setClass("CovControlSest", representation(bdp="numeric",
arp="numeric",
eps="numeric",
maxiter="numeric",
nsamp="numeric",
seed="Uvector",
method="character"),
prototype = list(bdp=0.5,
arp=0.1,
eps=1e-5,
maxiter=120,
nsamp=500,
seed=NULL,
trace=FALSE,
tolSolve=1e-14,
method="sfast"),
contains="CovControl")
CovControlSest <- function (bdp=0.5,
arp=0.1,
eps=1e-5,
maxiter=120,
nsamp=500,
seed=NULL,
trace=FALSE,
tolSolve=1e-14,
method="sfast")
{
new("CovControlSest", bdp=bdp, arp=arp, eps=eps, maxiter=maxiter,
nsamp=nsamp, seed=seed, trace=trace, tolSolve=tolSolve, method=method)
}
## Control parameters for CovSde
setClass("CovControlSde", representation(nsamp="numeric",
maxres="numeric",
tune="numeric",
eps="numeric",
prob="numeric",
seed="Uvector"),
prototype = list(tune=0.95,
eps=0.5,
prob=0.99,
seed=NULL,
trace=FALSE,
tolSolve=1e-14),
contains="CovControl")
## Control parameters for CovMMest
setClass("CovControlMMest", representation(bdp="numeric",
eff="numeric",
maxiter="numeric",
sest="CovControlSest"),
prototype = list(bdp=0.5,
eff=0.95,
maxiter=50,
sest=CovControlSest(),
trace=FALSE,
tolSolve=10e-14),
contains="CovControl"
)
###################### PCA ####################################
setClass("Pca", representation(call = "language",
center = "vector",
scale = "Uvector",
rank = "numeric",
loadings = "matrix",
eigenvalues = "vector",
scores = "matrix",
k = "numeric",
sd = "Uvector",
od = "Uvector",
cutoff.sd = "numeric",
cutoff.od = "numeric",
crit.pca.distances = "numeric",
flag = "Uvector",
n.obs = "numeric",
eig0 = "vector",
totvar0 = "numeric",
"VIRTUAL"))
setClass("SummaryPca", representation(pcaobj = "Pca",
importance ="matrix"))
setClass("PcaClassic", contains="Pca")
setClass("PcaRobust", representation("VIRTUAL"),
contains="Pca")
setClass("PcaHubert", representation(alpha = "numeric",
quan = "numeric",
skew = "logical",
ao = "Uvector"),
contains="PcaRobust")
setClass("PcaLocantore", representation(),
contains="PcaRobust")
setClass("PcaCov", representation(quan = "numeric"),
contains="PcaRobust")
setClass("PcaProj", representation(),
contains="PcaRobust")
setClass("PcaGrid", representation(),
contains="PcaRobust")
###################### LDA ####################################
setClass("Lda", representation(call = "language",
prior = "vector",
counts = "vector",
center = "matrix",
cov = "matrix",
ldf = "matrix",
ldfconst = "vector",
method = "character",
X = "Umatrix",
grp = "factor",
covobj = "UCov",
control = "UCovControl",
"VIRTUAL"))
setClass("SummaryLda", representation(ldaobj = "Lda"))
setClass("LdaClassic", contains="Lda")
setClass("LdaRobust", representation("VIRTUAL"),
contains="Lda")
setClass("PredictLda", representation(classification = "factor",
posterior = "matrix",
x = "matrix",
ct="Utable"))
setClass("Linda", representation(
l1med = "logical"),
contains="LdaRobust")
setClass("LdaPP", representation(
raw.ldf = "matrix",
raw.ldfconst = "vector"),
contains="LdaRobust")
###################### QDA ####################################
setClass("Qda", representation(call = "language",
prior = "vector",
counts = "vector",
center = "matrix",
cov = "array",
covinv = "array",
covdet = "vector",
method = "character",
X = "Umatrix",
grp = "factor",
control = "UCovControl",
"VIRTUAL"))
setClass("SummaryQda", representation(qdaobj = "Qda"))
setClass("QdaClassic", contains="Qda")
setClass("QdaRobust", representation("VIRTUAL"),
contains="Qda")
setClass("PredictQda", representation(classification = "factor",
posterior = "matrix",
x = "matrix",
ct="Utable"))
setClass("QdaCov", contains="QdaRobust")
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rrcov documentation built on May 29, 2024, 1:13 a.m.
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Defines functions .vrobGK .mrobTau
setClass("PsiFun", representation(n = "numeric",
p = "numeric",
r = "numeric",
alpha = "numeric",
c1 = "numeric"))
setClass("PsiBwt", representation(M = "numeric"),
contains="PsiFun")
## Define class unions for optional slots, e.g. for definition
## of slots which will be computed on demand, like the
## mahalanobis/robust distances
setClassUnion("Uvector", c("vector", "NULL"))
setClassUnion("Unumeric", c("numeric", "NULL"))
setClassUnion("Cnumeric", c("numeric", "character"))
setClassUnion("Umatrix", c("matrix", "NULL"))
setClassUnion("Ulist", c("list", "NULL"))
setClassUnion("Ufunction", c("function", "character", "NULL"))
setClassUnion("Utable", c("table", "NULL"))
## This is a virtual base class for control objects. Each robust
## method like CovMest, CovOgk, etc. will derive a subclass with
## the necessary control parameters, e.g. CovControlMest will
## contain the control parameters for CovMest.
setClass("CovControl", representation(trace="logical",
tolSolve="numeric",
"VIRTUAL"))
setClassUnion("UCovControl", c("CovControl", "NULL"))
setClass("Cov", representation(call = "language",
cov = "matrix",
center = "vector",
det = "numeric",
n.obs = "numeric",
mah = "Uvector",
flag = "Uvector",
method = "character",
singularity = "Ulist",
X = "Umatrix",
"VIRTUAL"),
prototype=list(det=-1))
setClassUnion("UCov", c("Cov", "NULL"))
setClass("SummaryCov", representation(covobj = "Cov",
evals = "vector"))
setClass("CovClassic", contains="Cov")
setClass("CovRobust", representation(iter="numeric",
crit="numeric",
wt="Uvector",
"VIRTUAL"),
contains="Cov")
setClass("SummaryCovRobust", representation(),
contains="SummaryCov")
setClass("CovMest", representation(vt="vector"),
contains="CovRobust")
setClass("CovMcd", representation(alpha = "numeric",
quan = "numeric",
best = "Uvector",
raw.cov = "matrix",
raw.center = "vector",
raw.mah = "Uvector",
raw.wt = "Uvector",
raw.cnp2 = "numeric",
cnp2 = "numeric"),
contains="CovRobust")
setClass("CovMrcd", representation(alpha = "numeric",
quan = "numeric",
best = "Uvector",
cnp2 = "numeric",
icov = "matrix",
rho = "numeric",
target="matrix"),
contains="CovRobust")
setClass("CovOgk", representation(raw.cov = "matrix",
raw.center = "vector",
raw.mah = "Uvector",
raw.wt = "Uvector"),
contains="CovRobust")
setClass("CovMve", representation(alpha = "numeric",
quan = "numeric",
best = "Uvector",
raw.cov = "matrix",
raw.center = "vector",
raw.mah = "Uvector",
raw.wt = "Uvector",
raw.cnp2 = "numeric",
cnp2 = "numeric"),
contains="CovRobust")
setClass("CovSest", representation(iBest = "numeric",
nsteps = "Uvector",
initHsets = "Umatrix",
cc = "numeric",
kp = "numeric"),
contains="CovRobust")
setClass("CovSde", representation(),
contains="CovRobust")
setClass("CovMMest", representation(c1 ="numeric",
sest = "CovSest"),
contains="CovRobust")
## Control parameters for CovMcd
setClass("CovControlMcd", representation(alpha="numeric",
nsamp="Cnumeric",
scalefn="Ufunction",
maxcsteps="numeric",
seed="Uvector",
use.correction="logical"),
prototype = list(alpha=0.5,
nsamp=500,
scalefn=NULL,
maxcsteps=200,
seed=NULL,
trace=FALSE,
tolSolve=1e-14,
use.correction=TRUE),
contains="CovControl")
## Control parameters for CovMrcd
setClass("CovControlMrcd", representation(alpha="numeric",
h="Unumeric",
maxcsteps="numeric",
rho="Unumeric",
target = "character",
maxcond = "numeric"),
prototype = list(alpha=0.5,
h=NULL,
maxcsteps=200,
rho=NULL,
target="identity",
maxcond=50,
trace=FALSE,
tolSolve=1e-14),
contains="CovControl")
## Control parameters for CovMest
setClass("CovControlMest", representation(r="numeric",
arp="numeric",
eps="numeric",
maxiter="numeric"),
prototype = list(r=0.45,
arp=0.05,
eps=1e-3,
maxiter=120,
trace=FALSE,
tolSolve=1e-14),
contains="CovControl"
)
## Control parameters for CovOgk
##
## Computes robust univariate mu and sigmma of the vector x
## - sigma: tau scale Yohai and Zamar (1988) - a truncated
## standard deviation
## - mu: weighted mean
##
## Returns a vector of length two with the calculated mu and sigma
##
.mrobTau <- function(x, c1 = 4.5, c2 = 3.0, ...) #c2=2.36075
{
return(scaleTau2(x, mu.too=TRUE)) # use scaleTau2 from package robustbase
if(FALSE) {
m0 <- median(x) # MED
s0 <- median(abs(x - m0)) # MAD
r <- abs(x-m0)/s0
wt <- (1 - (r/c1)^2)^2
wt <- ifelse(r <= c1, wt, 0) # wt = weigths w(x,c1)
m <- sum(x*wt)/sum(wt) # mu = weighted mean
r <- (x-m)/s0
r <- r^2
r[r > c2^2] <- c2^2 # rho(x,c2)
s2 <- s0^2 / length(x) * sum(r) # sigma = tau scale (Yohai&Zamar 1988)
# truncated standard deviation
c(m, sqrt(s2))
}
}
##
## Compute a robust estimate of the covariance of two random
## variables x1 and x2.
## Use the estimate defined by Gnanadesikan and Kettenring (1972):
## cov(X,Y)=1/4 * (sigma(X+Y)^2 - sigma(X-Y)^2)
## where sigma is a robust univariate scale.
## As sigma is used the tau scale estimate defined above - mrobTau()
##
.vrobGK <- function(x1, x2, ...)
{
(.mrobTau(x1+x2, ...)[2]^2 - .mrobTau(x1-x2, ...)[2]^2)/4.0
}
setClass("CovControlOgk", representation(niter="numeric",
beta="numeric",
mrob="Ufunction", # mrob=.mrobTau
vrob="Ufunction", # vrob=.vrobGK
smrob="character",
svrob="character"),
prototype = list(niter=2,
beta=0.90,
mrob=NULL,
vrob=.vrobGK,
smrob="scaleTau2",
svrob="gk",
trace=FALSE,
tolSolve=1e-14),
contains="CovControl"
)
## Control parameters for CovMve
setClass("CovControlMve", representation(alpha="numeric",
nsamp="numeric",
seed="Uvector"),
prototype = list(alpha=0.5,
nsamp=500,
seed=NULL,
trace=FALSE,
tolSolve=1e-14),
contains="CovControl")
## Control parameters for CovSest
setClass("CovControlSest", representation(bdp="numeric",
arp="numeric",
eps="numeric",
maxiter="numeric",
nsamp="numeric",
seed="Uvector",
method="character"),
prototype = list(bdp=0.5,
arp=0.1,
eps=1e-5,
maxiter=120,
nsamp=500,
seed=NULL,
trace=FALSE,
tolSolve=1e-14,
method="sfast"),
contains="CovControl")
CovControlSest <- function (bdp=0.5,
arp=0.1,
eps=1e-5,
maxiter=120,
nsamp=500,
seed=NULL,
trace=FALSE,
tolSolve=1e-14,
method="sfast")
{
new("CovControlSest", bdp=bdp, arp=arp, eps=eps, maxiter=maxiter,
nsamp=nsamp, seed=seed, trace=trace, tolSolve=tolSolve, method=method)
}
## Control parameters for CovSde
setClass("CovControlSde", representation(nsamp="numeric",
maxres="numeric",
tune="numeric",
eps="numeric",
prob="numeric",
seed="Uvector"),
prototype = list(tune=0.95,
eps=0.5,
prob=0.99,
seed=NULL,
trace=FALSE,
tolSolve=1e-14),
contains="CovControl")
## Control parameters for CovMMest
setClass("CovControlMMest", representation(bdp="numeric",
eff="numeric",
maxiter="numeric",
sest="CovControlSest"),
prototype = list(bdp=0.5,
eff=0.95,
maxiter=50,
sest=CovControlSest(),
trace=FALSE,
tolSolve=10e-14),
contains="CovControl"
)
###################### PCA ####################################
setClass("Pca", representation(call = "language",
center = "vector",
scale = "Uvector",
rank = "numeric",
loadings = "matrix",
eigenvalues = "vector",
scores = "matrix",
k = "numeric",
sd = "Uvector",
od = "Uvector",
cutoff.sd = "numeric",
cutoff.od = "numeric",
crit.pca.distances = "numeric",
flag = "Uvector",
n.obs = "numeric",
eig0 = "vector",
totvar0 = "numeric",
"VIRTUAL"))
setClass("SummaryPca", representation(pcaobj = "Pca",
importance ="matrix"))
setClass("PcaClassic", contains="Pca")
setClass("PcaRobust", representation("VIRTUAL"),
contains="Pca")
setClass("PcaHubert", representation(alpha = "numeric",
quan = "numeric",
skew = "logical",
ao = "Uvector"),
contains="PcaRobust")
setClass("PcaLocantore", representation(),
contains="PcaRobust")
setClass("PcaCov", representation(quan = "numeric"),
contains="PcaRobust")
setClass("PcaProj", representation(),
contains="PcaRobust")
setClass("PcaGrid", representation(),
contains="PcaRobust")
###################### LDA ####################################
setClass("Lda", representation(call = "language",
prior = "vector",
counts = "vector",
center = "matrix",
cov = "matrix",
ldf = "matrix",
ldfconst = "vector",
method = "character",
X = "Umatrix",
grp = "factor",
covobj = "UCov",
control = "UCovControl",
"VIRTUAL"))
setClass("SummaryLda", representation(ldaobj = "Lda"))
setClass("LdaClassic", contains="Lda")
setClass("LdaRobust", representation("VIRTUAL"),
contains="Lda")
setClass("PredictLda", representation(classification = "factor",
posterior = "matrix",
x = "matrix",
ct="Utable"))
setClass("Linda", representation(
l1med = "logical"),
contains="LdaRobust")
setClass("LdaPP", representation(
raw.ldf = "matrix",
raw.ldfconst = "vector"),
contains="LdaRobust")
###################### QDA ####################################
setClass("Qda", representation(call = "language",
prior = "vector",
counts = "vector",
center = "matrix",
cov = "array",
covinv = "array",
covdet = "vector",
method = "character",
X = "Umatrix",
grp = "factor",
control = "UCovControl",
"VIRTUAL"))
setClass("SummaryQda", representation(qdaobj = "Qda"))
setClass("QdaClassic", contains="Qda")
setClass("QdaRobust", representation("VIRTUAL"),
contains="Qda")
setClass("PredictQda", representation(classification = "factor",
posterior = "matrix",
x = "matrix",
ct="Utable"))
setClass("QdaCov", contains="QdaRobust")
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