fixreg | R Documentation |
Computes linear regression fixed point clusters (FPCs), i.e., subsets of the data, which consist exactly of the non-outliers w.r.t. themselves, and may be interpreted as generated from a homogeneous linear regression relation between independent and dependent variable. FPCs may overlap, are not necessarily exhausting and do not need a specification of the number of clusters.
Note that while fixreg
has lots of parameters, only one (or
few) of them have usually to be specified, cf. the examples. The
philosophy is to allow much flexibility, but to always provide
sensible defaults.
fixreg(indep=rep(1,n), dep, n=length(dep),
p=ncol(as.matrix(indep)),
ca=NA, mnc=NA, mtf=3, ir=NA, irnc=NA,
irprob=0.95, mncprob=0.5, maxir=20000, maxit=5*n,
distcut=0.85, init.group=list(),
ind.storage=FALSE, countmode=100,
plot=FALSE)
## S3 method for class 'rfpc'
summary(object, ...)
## S3 method for class 'summary.rfpc'
print(x, maxnc=30, ...)
## S3 method for class 'rfpc'
plot(x, indep=rep(1,n), dep, no, bw=TRUE,
main=c("Representative FPC No. ",no),
xlab="Linear combination of independents",
ylab=deparse(substitute(indep)),
xlim=NULL, ylim=range(dep),
pch=NULL, col=NULL,...)
## S3 method for class 'rfpc'
fpclusters(object, indep=NA, dep=NA, ca=object$ca, ...)
rfpi(indep, dep, p, gv, ca, maxit, plot)
indep |
numerical matrix or vector. Independent
variables.
Leave out for clustering one-dimensional data.
|
dep |
numerical vector. Dependent variable.
|
n |
optional positive integer. Number of cases. |
p |
optional positive integer. Number of independent variables. |
ca |
optional positive number. Tuning constant, specifying
required cluster
separation. By default determined automatically as a
function of |
mnc |
optional positive integer. Minimum size of clusters
to be reported.
By default determined automatically as a function of
|
mtf |
optional positive integer. FPCs must be found at
least |
ir |
optional positive integer. Number of algorithm runs.
By default determined
automatically as a function of |
irnc |
optional positive integer. Size of the smallest
cluster to be found with
approximated probability |
irprob |
optional value between 0 and 1. Approximated
probability for a cluster of size |
mncprob |
optional value between 0 amd 1. Approximated
probability for a cluster of size |
maxir |
optional integer. Maximum number of algorithm runs. |
maxit |
optional integer. Maximum number of iterations per algorithm run (usually an FPC is found much earlier). |
distcut |
optional value between 0 and 1. A similarity
measure between FPCs, given in Hennig (2002a), and the corresponding
Single Linkage groups of FPCs with similarity larger
than |
init.group |
optional list of logical vectors of length
|
ind.storage |
optional logical. If |
countmode |
optional positive integer. Every |
plot |
optional logical. If |
object |
object of class |
x |
object of class |
maxnc |
positive integer. Maximum number of FPCs to be reported. |
no |
positive integer. Number of the representative FPC to be plotted. |
bw |
optional logical. If |
main |
plot title. |
xlab |
label for x-axis. |
ylab |
label for y-axis. |
xlim |
plotted range of x-axis. If |
ylim |
plotted range of y-axis. |
pch |
plotting symbol, see |
col |
plotting color, see |
gv |
logical vector of length |
... |
additional parameters to be passed to |
A linear regression FPC is a data subset
that reproduces itself under the following operation:
Compute linear regression and error variance estimator for the data
subset, and compute all points of the dataset for which the squared
residual is smaller than ca
times the error variance.
Fixed points of this operation can be considered as clusters,
because they contain only
non-outliers (as defined by the above mentioned procedure) and all other
points are outliers w.r.t. the subset.
fixreg
performs ir
fixed point algorithms started from
random subsets of size p+2
to look for
FPCs. Additionally an algorithm is started from the whole dataset,
and algorithms are started from the subsets specified in
init.group
.
Usually some of the FPCs are unstable, and more than one FPC may
correspond to the same significant pattern in the data. Therefore the
number of FPCs is reduced: FPCs with less than mnc
points are
ignored. Then a similarity matrix is computed between the remaining
FPCs. Similarity between sets is defined as the ratio between
2 times size of
intersection and the sum of sizes of both sets. The Single Linkage
clusters (groups)
of level distcut
are computed, i.e. the connectivity
components of the graph where edges are drawn between FPCs with
similarity larger than distcut
. Groups of FPCs whose members
are found mtf
times or more are considered as stable enough.
A representative FPC is
chosen for every Single Linkage cluster of FPCs according to the
maximum expectation ratio ser
. ser
is the ratio between
the number of findings of an FPC and the estimated
expectation of the number of findings of an FPC of this size,
called expectation ratio and
computed by clusexpect
.
Usually only the representative FPCs of stable groups
are of interest.
The choice of the involved tuning constants such as ca
and
ir
is discussed in detail in Hennig (2002a). Statistical theory
is presented in Hennig (2003).
Generally, the default settings are recommended for
fixreg
. In cases where they lead to a too large number of
algorithm runs (e.g., always for p>4
), the use of
init.group
together with mtf=1
and ir=0
is useful. Occasionally, irnc
may be chosen
smaller than the default,
if smaller clusters are of interest, but this may lead to too many
clusters and too many algorithm runs. Decrease of
ca
will often lead to too many clusters, even for homogeneous
data. Increase of ca
will produce only very strongly
separated clusters. Both may be of interest occasionally.
rfpi
is called by fixreg
for a single fixed point
algorithm and will usually not be executed alone.
summary.rfpc
gives a summary about the representative FPCs of
stable groups.
plot.rfpc
is a plot method for the representative FPC of stable
group
no. no
. It produces a scatterplot of the linear combination of
independent variables determined by the regression coefficients of the
FPC vs. the dependent variable. The regression line and the region
of non-outliers determined by ca
are plotted as well.
fpclusters.rfpc
produces a list of indicator vectors for the
representative FPCs of stable groups.
fixreg
returns an object of class rfpc
. This is a list
containing the components nc, g, coefs, vars, nfound, er, tsc,
ncoll, grto, imatrix, smatrix, stn, stfound, sfpc, ssig, sto, struc,
n, p, ca, ir, mnc, mtf, distcut
.
summary.rfpc
returns an object of class summary.rfpc
.
This is a list containing the components coefs, vars, stfound,
stn, sn, ser, tsc, sim, ca, ir, mnc, mtf
.
fpclusters.rfpc
returns a list of indicator vectors for the
representative FPCs of stable groups.
rfpi
returns a list with the components coef, var, g,
coll, ca
.
nc |
integer. Number of FPCs. |
g |
list of logical vectors. Indicator vectors of FPCs. |
coefs |
list of numerical vectors. Regression coefficients of
FPCs. In |
vars |
list of numbers. Error variances of FPCs. In
|
nfound |
vector of integers. Number of findings for the FPCs. |
er |
numerical vector. Expectation ratios of FPCs. Can be taken as a stability measure. |
tsc |
integer. Number of algorithm runs leading to too small or too seldom found FPCs. |
ncoll |
integer. Number of algorithm runs where collinear regressor matrices occurred. |
grto |
vector of integers. Numbers of FPCs to which algorithm
runs led, which were started by |
imatrix |
vector of integers. Size of intersection between
FPCs. See |
smatrix |
numerical vector. Similarities between
FPCs. See |
stn |
integer. Number of representative FPCs of stable groups. In
|
stfound |
vector of integers. Number of findings of members of
all groups of FPCs. In
|
sfpc |
vector of integers. Numbers of representative FPCs. |
ssig |
vector of integers. As |
sto |
vector of integers. Number of representative FPC of most, 2nd most, ..., often found group of FPCs. |
struc |
vector of integers. Number of group an FPC belongs to. |
n |
see arguments. |
p |
see arguments. |
ca |
see arguments. |
ir |
see arguments. |
mnc |
see arguments. |
mtf |
see arguments. |
distcut |
see arguments. |
sn |
vector of integers. Number of points of representative FPCs. |
ser |
numerical vector. Expectation ratio for stable groups. |
sim |
vector of integers. Size of intersections between
representative FPCs of stable groups. See |
coef |
vector of regression coefficients. |
var |
error variance. |
g |
logical indicator vector of iterated FPC. |
coll |
logical. |
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
Hennig, C. (2002) Fixed point clusters for linear regression: computation and comparison, Journal of Classification 19, 249-276.
Hennig, C. (2003) Clusters, outliers and regression: fixed point clusters, Journal of Multivariate Analysis 86, 183-212.
fixmahal
for fixed point clusters in the usual setup
(non-regression).
regmix
for clusterwiese linear regression by mixture
modeling ML.
can
, itnumber
for computation of the default
settings.
clusexpect
for estimation of the expected number of
findings of an FPC of given size.
itnumber
for the generation of the number of fixed point
algorithms.
minsize
for the smallest FPC size to be found with a given
probability..
sseg
for indexing the similarity/intersection vectors
computed by fixreg
.
set.seed(190000)
options(digits=3)
data(tonedata)
attach(tonedata)
tonefix <- fixreg(stretchratio,tuned,mtf=1,ir=20)
summary(tonefix)
# This is designed to have a fast example; default setting would be better.
# If you want to see more (and you have a bit more time),
# try out the following:
## Not run:
set.seed(1000)
tonefix <- fixreg(stretchratio,tuned)
# Default - good for these data
summary(tonefix)
plot(tonefix,stretchratio,tuned,1)
plot(tonefix,stretchratio,tuned,2)
plot(tonefix,stretchratio,tuned,3,bw=FALSE,pch=5)
toneclus <- fpclusters(tonefix,stretchratio,tuned)
plot(stretchratio,tuned,col=1+toneclus[[2]])
tonefix2 <- fixreg(stretchratio,tuned,distcut=1,mtf=1,countmode=50)
# Every found fixed point cluster is reported,
# no matter how instable it may be.
summary(tonefix2)
tonefix3 <- fixreg(stretchratio,tuned,ca=7)
# ca defaults to 10.07 for these data.
summary(tonefix3)
subset <- c(rep(FALSE,5),rep(TRUE,24),rep(FALSE,121))
tonefix4 <- fixreg(stretchratio,tuned,
mtf=1,ir=0,init.group=list(subset))
summary(tonefix4)
## End(Not run)
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