程序代写代做代考 matlab cache c/c++ scheme algorithm Package ‘e1071’

Package ‘e1071’
February 2, 2017

Version 1.6-8
Title Misc Functions of the Department of Statistics, Probability

Theory Group (Formerly: E1071), TU Wien

Imports graphics, grDevices, class, stats, methods, utils
Suggests cluster, mlbench, nnet, randomForest, rpart, SparseM, xtable,

Matrix, MASS

Description Functions for latent class analysis, short time Fourier
transform, fuzzy clustering, support vector machines,
shortest path computation, bagged clustering, naive Bayes
classifier, …

License GPL-2
LazyLoad yes
NeedsCompilation yes
Author David Meyer [aut, cre],

Evgenia Dimitriadou [aut, cph],
Kurt Hornik [aut],
Andreas Weingessel [aut],
Friedrich Leisch [aut],
Chih-Chung Chang [ctb, cph] (libsvm C++-code),
Chih-Chen Lin [ctb, cph] (libsvm C++-code)

Maintainer David Meyer
Repository CRAN
Date/Publication 2017-02-02 12:37:10

R topics documented:
allShortestPaths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
bclust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
bincombinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
bootstrap.lca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
boxplot.bclust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
classAgreement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1

2 R topics documented:

cmeans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
countpattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
cshell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Discrete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
e1071-deprecated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
fclustIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
hamming.distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
hamming.window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
hanning.window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
hsv_palette . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
ica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
impute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
interpolate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
kurtosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
lca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
matchClasses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
matchControls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
moment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
naiveBayes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
permutations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
plot.stft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
plot.svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
plot.tune . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
predict.svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
probplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
rbridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
read.matrix.csr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
rectangle.window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
rwiener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
sigmoid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
skewness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
stft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
tune . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
tune.control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
tune.wrapper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
write.svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Index 60

allShortestPaths 3

allShortestPaths Find Shortest Paths Between All Nodes in a Directed Graph

Description

allShortestPaths finds all shortest paths in a directed (or undirected) graph using Floyd’s algo-
rithm. extractPath can be used to actually extract the path between a given pair of nodes.

Usage

allShortestPaths(x)
extractPath(obj, start, end)

Arguments

x matrix or distance object

obj return value of allShortestPaths

start integer, starting point of path

end integer, end point of path

Details

If x is a matrix, then x[i,j] has to be the length of the direct path from point i to point j. If no
direct connection from point i to point j exist, then x[i,j] should be either NA or Inf. Note that
the graph can be directed, hence x[i,j] need not be the same as x[j,i]. The main diagonal of
x is ignored. Alternatively, x can be a distance object as returned by dist (corresponding to an
undirected graph).

Value

allShortestPaths returns a list with components

length A matrix with the total lengths of the shortest path between each pair of points.

middlePoints A matrix giving a point in the middle of each shortest path (or 0 if the direct
connection is the shortest path), this is mainly used as input for extractPath.

extractPath returns a vector of node numbers giving with the shortest path between two points.

Author(s)

Friedrich Leisch

References

Kumar, V., Grama, A., Gupta, A. and Karypis, G. Introduction to Parallel Programming – Design
and Analysis of Algorithms, Benjamin Cummings Publishing, 1994, ISBN 0-8053-3170-0

4 bclust

Examples

## build a graph with 5 nodes
x <- matrix(NA, 5, 5) diag(x) <- 0 x[1,2] <- 30; x[1,3] <- 10 x[2,4] <- 70; x[2,5] <- 40 x[3,4] <- 50; x[3,5] <- 20 x[4,5] <- 60 x[5,4] <- 10 print(x) ## compute all path lengths z <- allShortestPaths(x) print(z) ## the following should give 1 -> 3 -> 5 -> 4
extractPath(z, 1, 4)

bclust Bagged Clustering

Description

Cluster the data in x using the bagged clustering algorithm. A partitioning cluster algorithm such as
kmeans is run repeatedly on bootstrap samples from the original data. The resulting cluster centers
are then combined using the hierarchical cluster algorithm hclust.

Usage

bclust(x, centers=2, iter.base=10, minsize=0,
dist.method=”euclidian”,
hclust.method=”average”, base.method=”kmeans”,
base.centers=20, verbose=TRUE,
final.kmeans=FALSE, docmdscale=FALSE,
resample=TRUE, weights=NULL, maxcluster=base.centers, …)

hclust.bclust(object, x, centers, dist.method=object$dist.method,
hclust.method=object$hclust.method, final.kmeans=FALSE,
docmdscale = FALSE, maxcluster=object$maxcluster)

## S3 method for class ‘bclust’
plot(x, maxcluster=x$maxcluster, main, …)
centers.bclust(object, k)
clusters.bclust(object, k, x=NULL)

Arguments

x Matrix of inputs (or object of class “bclust” for plot).

centers, k Number of clusters.

bclust 5

iter.base Number of runs of the base cluster algorithm.

minsize Minimum number of points in a base cluster.

dist.method Distance method used for the hierarchical clustering, see dist for available dis-
tances.

hclust.method Linkage method used for the hierarchical clustering, see hclust for available
methods.

base.method Partitioning cluster method used as base algorithm.

base.centers Number of centers used in each repetition of the base method.

verbose Output status messages.

final.kmeans If TRUE, a final kmeans step is performed using the output of the bagged cluster-
ing as initialization.

docmdscale Logical, if TRUE a cmdscale result is included in the return value.

resample Logical, if TRUE the base method is run on bootstrap samples of x, else directly
on x.

weights Vector of length nrow(x), weights for the resampling. By default all observa-
tions have equal weight.

maxcluster Maximum number of clusters memberships are to be computed for.

object Object of class “bclust”.

main Main title of the plot.

… Optional arguments top be passed to the base method in bclust, ignored in
plot.

Details

First, iter.base bootstrap samples of the original data in x are created by drawing with replace-
ment. The base cluster method is run on each of these samples with base.centers centers. The
base.method must be the name of a partitioning cluster function returning a list with the same
components as the return value of kmeans.

This results in a collection of iter.base * base.centers centers, which are subsequently clus-
tered using the hierarchical method hclust. Base centers with less than minsize points in there
respective partitions are removed before the hierarchical clustering.

The resulting dendrogram is then cut to produce centers clusters. Hence, the name of the argument
centers is a little bit misleading as the resulting clusters need not be convex, e.g., when single
linkage is used. The name was chosen for compatibility with standard partitioning cluster methods
such as kmeans.

A new hierarchical clustering (e.g., using another hclust.method) re-using previous base runs can
be performed by running hclust.bclust on the return value of bclust.

Value

bclust and hclust.bclust return objects of class “bclust” including the components

hclust Return value of the hierarchical clustering of the collection of base centers (Ob-
ject of class “hclust”).

6 bincombinations

cluster Vector with indices of the clusters the inputs are assigned to.

centers Matrix of centers of the final clusters. Only useful, if the hierarchical clustering
method produces convex clusters.

allcenters Matrix of all iter.base * base.centers centers found in the base runs.

Author(s)

Friedrich Leisch

References

Friedrich Leisch. Bagged clustering. Working Paper 51, SFB “Adaptive Information Systems and
Modeling in Economics and Management Science”, August 1999. http://epub.wu.ac.at/1272/
1/document.pdf

See Also

hclust, kmeans, boxplot.bclust

Examples

data(iris)
bc1 <- bclust(iris[,1:4], 3, base.centers=5) plot(bc1) table(clusters.bclust(bc1, 3)) centers.bclust(bc1, 3) bincombinations Binary Combinations Description Returns a matrix containing the 2p vectors of length p. Usage bincombinations(p) Arguments p Length of binary vectors Author(s) Friedrich Leisch http://epub.wu.ac.at/1272/1/document.pdf http://epub.wu.ac.at/1272/1/document.pdf bootstrap.lca 7 Examples bincombinations(2) bincombinations(3) bootstrap.lca Bootstrap Samples of LCA Results Description This function draws bootstrap samples from a given LCA model and refits a new LCA model for each sample. The quality of fit of these models is compared to the original model. Usage bootstrap.lca(l, nsamples=10, lcaiter=30, verbose=FALSE) Arguments l An LCA model as created by lca nsamples Number of bootstrap samples lcaiter Number of LCA iterations verbose If TRUE some output is printed during the computations. Details From a given LCA model l, nsamples bootstrap samples are drawn. For each sample a new LCA model is fitted. The goodness of fit for each model is computed via Likelihood Ratio and Pearson’s Chisquare. The values for the fitted models are compared with the values of the original model l. By this method it can be tested whether the data to which l was originally fitted come from an LCA model. Value An object of class bootstrap.lca is returned, containing logl, loglsat The LogLikelihood of the models and of the corresponding saturated models lratio Likelihood quotient of the models and the corresponding saturated models lratiomean, lratiosd Mean and Standard deviation of lratio lratioorg Likelihood quotient of the original model and the corresponding saturated model zratio Z-Statistics of lratioorg pvalzratio, pvalratio P-Values for zratio, computed via normal distribution and empirical distribu- tion chisq Pearson’s Chisq of the models 8 boxplot.bclust chisqmean, chisqsd Mean and Standard deviation of chisq chisqorg Pearson’s Chisq of the original model zchisq Z-Statistics of chisqorg pvalzchisq, pvalchisq P-Values for zchisq, computed via normal distribution and empirical distribu- tion nsamples Number of bootstrap samples lcaiter Number of LCA Iterations Author(s) Andreas Weingessel References Anton K. Formann: “Die Latent-Class-Analysis”, Beltz Verlag 1984 See Also lca Examples ## Generate a 4-dim. sample with 2 latent classes of 500 data points each. ## The probabilities for the 2 classes are given by type1 and type2. type1 <- c(0.8,0.8,0.2,0.2) type2 <- c(0.2,0.2,0.8,0.8) x <- matrix(runif(4000),nr=1000) x[1:500,] <- t(t(x[1:500,])