Univariate distribution examples
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From Pajek to R for indegree distribution of prime buyers named in Ohta survey of 5111 firms, naming 8347 prime buyers. Use Pajek to compute degree, pass to vector, output to R, will be v2.
net=source("C:/Program Files/pajek/PAJEK/PajekR.r") # This will have zeros.
http://www.math.montana.edu/Rweb/Rhelp/00Index.html
numnodes=0
v0=0
nv=0
for (i in 1:(length(v2)-1)) {if (v2[i] >= 1)
{{nv=v2[i];
numnodes=numnodes+1};
v0[numnodes] <- nv};
}
package(stats) ecd <- ecdf(v0)
dist=c(7931,275,59,36,13,10,6,2,2,2,2,2,2)
http://tolstoy.newcastle.edu.au/R/help/06/01/18377.html
library(degreenet) #see Mark S. Handcock and http://csde.washington.edu/statnet/ help(package="degreenet") help("degreenet-package") library(help="degreenet") demo(package="degreenet")
Contents |
[edit] help(package="degreenet")
Information on package 'degreenet' Description:
Package: degreenet
Version: 1.0
Date: 2008-1-01
Title: Models for Skewed Count Distributions Relevant to
Networks
Author: Mark S. Handcock <handcock@stat.washington.edu>
Maintainer: Mark S. Handcock <handcock@stat.washington.edu>
Suggests: network
Description: Likelihood-based inference for skewed count
distributions used in network modeling. "degreenet"
is a part of the "statnet" suite of packages for
network analysis. For a list of functions type:
help(package='degreenet')
License: file LICENSE
URL: http://statnetproject.org
Packaged: Tue Jan 15 21:24:08 2008; handcock
Built: R 2.6.1; i386-pc-mingw32; 2008-01-16 12:20:31;
windows
Index:
aplnmle Poisson Lognormal Modeling of Discrete Data
awarmle Waring Modeling of Discrete Data
ayulemle Yule Distribution Modeling of Discrete Data
bsdp Calculate Bootstrap Estimates and Confidence
Intervals for the Discrete Pareto Distribution
bsnb Calculate Bootstrap Estimates and Confidence
Intervals for the Negative Binomial
Distribution
bspln Calculate Bootstrap Estimates and Confidence
Intervals for the Poisson Lognormal
Distribution
bswar Calculate Bootstrap Estimates and Confidence
Intervals for the Waring Distribution
bsyule Calculate Bootstrap Estimates and Confidence
Intervals for the Yule Distribution
degreenet-package Models for Skewed Count Distributions Relevant
to Networks
gyulemle Models for Count Distributions
llgyule Calculate the Conditional log-likelihood for
Count Distributions
llgyuleall Calculate the log-likelihood for Count
Distributions
llpln Calculate the Conditional log-likelihood for
the Poisson Lognormal Distributions
llyule Calculate the Conditional log-likelihood for
Count Distributions
llyuleall Calculate the log-likelihood for Count
Distributions
reedmolloy Generate a (non-random) network with a given
degree sequence
rplnmle Rounded Poisson Lognormal Modeling of Discrete
Data
ryule Generate a (non-random) network from a Yule
Distribution
simdp Simulate from a Discrete Pareto Distribution
simnb Simulate from a Negative Binomial Distribution
simpln Simulate from a Poisson Lognormal Distribution
simwar Simulate from a Waring Distribution
simyule Simulate from a Yule Distribution
sweden Number of sex partners in the last 12 months
for men and women in Sweden
[edit] help("degreenet-package")
degreenet-package(degreenet) R Documentation
Models for Skewed Count Distributions Relevant to Networks Description degreenet is a collection of functions to fit, diagnose, and simulate from distributions for skewed count data. The coverage of distributions is very selective, focusing on those that have been proposed to model the degree distribution on networks. For the rationale for this choice, see the papers in the references section below. For a list of functions type: help(package='degreenet')
For a complete list of the functions, use library(help="degreenet") or read the rest of the manual. For a simple demonstration, use demo(packages="degreenet").
The degreenet package is part of the statnet suite of packages. The suite was developed to facilitate the statistical analysis of network data.
When publishing results obtained using this package alone see the citation in citation(package="degreenet"). The citation for the original paper to use this package is Handcock and Jones (2003) and it should be cited for the theoretical development.
If you use other packages in the statnet suite, please cite it as:
Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris. 2003 statnet: Software tools for the Statistical Modeling of Network Data http://statnetproject.org. For complete citation information, use citation(package="statnet").
All programs derived from this or other statnet packages must cite them appropriately.
Details See the Handcock and Jones (2003) reference (and the papers it cites and is cited by) for more information on the methodology.
Recent advances in the statistical modeling of random networks have had an impact on the empirical study of social networks. Statistical exponential family models (Strauss and Ikeda 1990) are a generalization of the Markov random network models introduced by Frank and Strauss (1986). These models recognize the complex dependencies within relational data structures. To date, the use of stochastic network models for networks has been limited by three interrelated factors: the complexity of realistic models, the lack of simulation tools for inference and validation, and a poor understanding of the inferential properties of nontrivial models.
This package relies on the network package which allows networks to be represented in R. The statnet suite of packages allows maximum likelihood estimates of exponential random network models to be calculated using Markov Chain Monte Carlo, as well as a broad range of statistical analysis of networks, such as tools for plotting networks, simulating networks and assessing model goodness-of-fit.
For detailed information on how to download and install the software, go to the statnet website: http://statnetproject.org. A tutorial, support newsgroup, references and links to further resources are provided there.
Author(s) Mark S. Handcock handcock@stat.washington.edu
Maintainer: Mark S. Handcock handcock@stat.washington.edu
References Frank, O., and Strauss, D.(1986). Markov graphs. Journal of the American Statistical Association, 81, 832-842.
Jones, J. H. and Handcock, M. S. (2003). An assessment of preferential attachment as a mechanism for human sexual network formation, Proceedings of the Royal Society, B, 2003, 270, 1123-1128.
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., and Morris, M. (2003), statnet: Software tools for the Statistical Modeling of Network Data., URL http://statnetproject.org
Strauss, D., and Ikeda, M.(1990). Pseudolikelihood estimation for social networks. Journal of the American Statistical Association, 85, 204-212.
[Package degreenet version 1.0 Index]
[edit] library(help="degreenet")
same as earlier
[edit] demo(package="degreenet")
Demos in package ‘degreenet’:
ayulemle Simple MLE of Yule model
network Using "network" to create network objects
pln Simple MLE of Poisson-LogNormal model with a
Plot
(Incomplete)
