Package bivariate. December 10, 2018
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1 Title Bivariate Probability Distributions Version Date License GPL (>= 2) Package bivariate December 10, 2018 Maintainer Abby Spurdle Author Abby Spurdle URL Contains convenience functions for constructing and plotting bivariate probability distributions (probability mass functions, probability density functions and cumulative distribution functions). Currently, supports uniform (discrete and continuous), binomial, Poisson, normal, bimodal and kernel distributions. Depends graphics Imports barsurf, mvtnorm, KernSmooth Suggests intoo, empirical, moments, MASS NeedsCompilation no Repository CRAN Date/Publication :20:06 UTC R topics documented: bimodal binomial kernel methods normal poisson returned_functions uniform Index 9 1
2 2 bimodal bimodal Bimodal Distributions Bimodal distributions. Refer to the vignette for better examples. bmbvpdf (mean.x1, mean.y1, variance.x1, variance.y1, mean.x2, mean.y2, variance.x2, variance.y2, w=0.5) bmbvcdf (mean.x1, mean.y1, variance.x1, variance.y1, mean.x2, mean.y2, variance.x2, variance.y2, w=0.5) mean.x1 mean.y1 variance.x1 variance.y1 mean.x2 mean.y2 variance.x2 variance.y2 w Numeric scalar, giving the mean of the first X component. Numeric scalar, giving the mean of the first Y component. Numeric scalar, giving the variance of the first X component. Numeric scalar, giving the variance of the first Y component. Numeric scalar, giving the mean of the second X component. Numeric scalar, giving the mean of the second Y component. Numeric scalar, giving the variance of the second X component. Numeric scalar, giving the variance of the second Y component. Numeric scalar in the interval (0, 1), giving the weight of the first component. bmbvpdf.f, bmbvcdf.f #construct some bimodal distributions bmbvpdf.f = bmbvpdf (3.5, 0, 1, 1, 6.5, 0, 1, 1) bmbvcdf.f = bmbvcdf (3.5, 0, 1, 1, 6.5, 0, 1, 1)
3 binomial 3 binomial Binomial Distributions Binomial distributions. Refer to the vignette for better examples. bnbvpmf (n, p.x, p.y) bnbvcdf (n, p.x, p.y) n p.x p.y Integer scalar, giving the number of trials. Numeric scalar, giving the probability of the first event. Numeric scalar, giving the probability of the second event. bnbvpmf.f, bnbvcdf.f #construct some binomial distributions bnbvpmf.f = bnbvpmf (10, 0.5, 0.5) bnbvcdf.f = bnbvcdf (10, 0.5, 0.5) kernel Kernel Distributions Probability density functions and cumulative distribution functions resulting from kernel smoothing. Refer to the vignette for better examples. kbvpdf (x, y, bw.x, bw.y) kbvcdf (x, y, bw.x, bw.y)
4 4 methods x y bw.x bw.y The x values. The y values. Numeric scalar, giving the X bandwidth. Numeric scalar, giving the Y bandwidth. However, currently they can t be evaluated. #construct some kernel distributions #(based on example in KernSmooth package) data ("geyser", package="mass") kbvpdf.f = kbvpdf (geyser$duration, geyser$waiting, 0.7, 7) kbvcdf.f = kbvcdf (geyser$duration, geyser$waiting, 0.7, 7) methods Methods Bivariate probability distribution methods. ## S3 method for class 'dubvpmf' plot(x, xlim, ylim,...) ## S3 method for class 'dubvcdf' plot(x, xlim, ylim,...) ## S3 method for class 'bnbvpmf' plot(x, xlim, ylim,...) ## S3 method for class 'bnbvcdf' plot(x, xlim, ylim,...) ## S3 method for class 'pbvpmf' plot(x, xmax, ymax,...) ## S3 method for class 'pbvcdf' plot(x, xmax, ymax,...) ## S3 method for class 'cubvpdf' plot(x, use.plot3d=false, npoints=20, xlim, ylim,..., all=false) ## S3 method for class 'cubvcdf' plot(x, use.plot3d=false, npoints=20, xlim, ylim,...) ## S3 method for class 'nbvpdf'
5 normal 5 plot(x, use.plot3d=false, npoints=20, xlim, ylim,..., all=false) ## S3 method for class 'nbvcdf' plot(x, use.plot3d=false, npoints=20, xlim, ylim,...) ## S3 method for class 'bmbvpdf' plot(x, use.plot3d=false, npoints=30, xlim, ylim,..., all=false) ## S3 method for class 'bmbvcdf' plot(x, use.plot3d=false, npoints=30, xlim, ylim,...) ## S3 method for class 'kbvpdf' plot(x, use.plot3d=false, npoints=30, xlim, ylim,..., all=false) ## S3 method for class 'kbvcdf' plot(x, use.plot3d=false, npoints=30, xlim, ylim,...) x use.plot3d npoints xlim ylim xmax ymax all A dubvpmf, dubvcdf, etc object. False by default. If true use 3d plots. The number of grid points in each direction. Increase for a smoother surface. The x range for the plot. The y range for the plot. The x range max for the plot. The y range max for the plot.... Other arguments. False by default. If true, plot a two by two grid of both the PDF and CDF using both contour and 3d plots. #plot a bivariate probability distribution dubvpmf.f = dubvpmf (0, 1, 0, 1) plot (dubvpmf.f) normal Normal Distributions Normal distributions. Refer to the vignette for better examples.
6 6 poisson nbvpdf (mean.x, mean.y, variance.x, variance.y, covariance) nbvcdf (mean.x, mean.y, variance.x, variance.y, covariance) mean.x Numeric scalar, giving the mean of X. mean.y Numeric scalar, giving the mean of Y. variance.x Numeric scalar, giving the variance of X. variance.y Numeric scalar, giving the variance of Y. covariance Numeric scalar, giving the covariance of X and Y. nbvpdf.f, nbvcdf.f #construct some normal distributions nbvpdf.f = nbvpdf (0, 0, 1, 1, 0) nbvcdf.f = nbvcdf (0, 0, 1, 1, 0) poisson Poisson Distributions Poisson distributions. Refer to the vignette for better examples and references. pbvpmf (mean.x, mean.y, covariance) pbvcdf (mean.x, mean.y, covariance) mean.x Numeric scalar, giving the mean (and variance) of X. mean.y Numeric scalar, giving the mean (and variance) of Y. covariance Numeric scalar, giving the covariance of X and Y.
7 returned_functions 7 pbvpmf.f, pbvcdf.f #construct some Poisson distributions pbvpmf.f = pbvpmf (10, 10, 0) pbvcdf.f = pbvcdf (10, 10, 0) returned_functions Returned Functions Returned functions. Note that you can name your functions whatever you like. (In the vignette I ve named them f and F). Also note that you don t call these functions, rather call dubvpmf(), dubvcdf(), etc and then call the functions that are returned. dubvpmf.f (x, y) dubvcdf.f (x, y) bnbvpmf.f (x, y) bnbvcdf.f (x, y) pbvpmf.f (x, y) pbvcdf.f (x, y) cubvpdf.f (x, y) cubvcdf.f (x, y) nbvpdf.f (x, y) nbvcdf.f (x, y) bmbvpdf.f (x, y) bmbvcdf.f (x, y) x y A vector of x values. A vector of y values. #construct and evaluate a discrete uniform distribution dubvpmf.f = dubvpmf (0, 1, 0, 1) dubvpmf.f (0.5, 0.5)
8 8 uniform uniform Discrete and Continuous Uniform Distributions Discrete and continuous uniform distributions. Refer to the vignette for better examples. dubvpmf (a.x, b.x, a.y, b.y) dubvcdf (a.x, b.x, a.y, b.y) cubvpdf (a.x, b.x, a.y, b.y) cubvcdf (a.x, b.x, a.y, b.y) a.x Numeric scalar, giving the a value of X. b.x Numeric scalar, giving the b value of X. a.y Numeric scalar, giving the a value of Y. b.y Numeric scalar, giving the b value of Y. dubvpmf.f, dubvcdf.f, cubvpdf.f, cubvcdf.f #construct some discrete uniform distributions dubvpmf.f = dubvpmf (0, 1, 0, 1) dubvcdf.f = dubvcdf (0, 1, 0, 1)
9 Index bimodal, 2 binomial, 3 bmbvcdf (bimodal), 2 bmbvcdf.f, 2 bmbvcdf.f (returned_functions), 7 bmbvpdf (bimodal), 2 bmbvpdf.f, 2 bmbvpdf.f (returned_functions), 7 bnbvcdf (binomial), 3 bnbvcdf.f, 3 bnbvcdf.f (returned_functions), 7 bnbvpmf (binomial), 3 bnbvpmf.f, 3 bnbvpmf.f (returned_functions), 7 cubvcdf (uniform), 8 cubvcdf.f, 8 cubvcdf.f (returned_functions), 7 cubvpdf (uniform), 8 cubvpdf.f, 8 cubvpdf.f (returned_functions), 7 dubvcdf (uniform), 8 dubvcdf.f, 8 dubvcdf.f (returned_functions), 7 dubvpmf (uniform), 8 dubvpmf.f, 8 dubvpmf.f (returned_functions), 7 normal, 5 pbvcdf (poisson), 6 pbvcdf.f, 7 pbvcdf.f (returned_functions), 7 pbvpmf (poisson), 6 pbvpmf.f, 7 pbvpmf.f (returned_functions), 7 plot.bmbvcdf (methods), 4 plot.bmbvpdf (methods), 4 plot.bnbvcdf (methods), 4 plot.bnbvpmf (methods), 4 plot.cubvcdf (methods), 4 plot.cubvpdf (methods), 4 plot.dubvcdf (methods), 4 plot.dubvpmf (methods), 4 plot.kbvcdf (methods), 4 plot.kbvpdf (methods), 4 plot.nbvcdf (methods), 4 plot.nbvpdf (methods), 4 plot.pbvcdf (methods), 4 plot.pbvpmf (methods), 4 poisson, 6 returned_functions, 7 uniform, 8 kbvcdf (kernel), 3 kbvpdf (kernel), 3 kernel, 3 methods, 4 nbvcdf (normal), 5 nbvcdf.f, 6 nbvcdf.f (returned_functions), 7 nbvpdf (normal), 5 nbvpdf.f, 6 nbvpdf.f (returned_functions), 7 9
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Contents 1. Contents
Contents 1 Contents 6 Distributions of Functions of Random Variables 2 6.1 Transformation of Discrete r.v.s............. 3 6.2 Method of Distribution Functions............. 6 6.3 Method of Transformations................
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Type Package Package IsingFit September 7, 2016 Title Fitting Ising Models Using the ELasso Method Version 0.3.1 Date 2016-9-6 Depends R (>= 3.0.0) Imports qgraph, Matrix, glmnet Suggests IsingSampler
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Type Package Package decon February 19, 2015 Title Deconvolution Estimation in Measurement Error Models Version 1.2-4 Date 2013-04-08 Author Xiao-Feng Wang, Bin Wang Maintainer Xiao-Feng Wang
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Type Package Package r2glmm August 5, 2017 Title Computes R Squared for Mixed (Multilevel) Models Date 2017-08-04 Version 0.1.2 The model R squared and semi-partial R squared for the linear and generalized
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Type Package Version 1.1-5 Date 2016-12-30 Package sdprisk December 31, 2016 Title Measures of Risk for the Compound Poisson Risk Process with Diffusion Maintainer Benjamin Baumgartner
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Title Cross-Entropy Clustering Version 0.9.4 Date 2016-04-23 Package CEC August 29, 2016 Author Konrad Kamieniecki [aut, cre], Przemyslaw Spurek [ctb] Maintainer Konrad Kamieniecki
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Type Package Package symmoments February 20, 2015 Title Symbolic central and noncentral moments of the multivariate normal distribution Version 1.2 Date 2014-05-21 Author Kem Phillips Depends mvtnorm,
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Type Package Package IndepTest August 23, 2018 Title Nonparametric Independence Tests Based on Entropy Estimation Version 0.2.0 Date 2018-08-23 Author Thomas B. Berrett [aut],
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Type Package Title Etended Empirical Likelihood Version 1.1 Date 2015-08-30 Author Fan Wu and Yu Zhang Maintainer Yu Zhang Package eel September 1, 2015 Compute the etended empirical log
Random Variables and Their Distributions
Chapter 3 Random Variables and Their Distributions A random variable (r.v.) is a function that assigns one and only one numerical value to each simple event in an experiment. We will denote r.vs by capital
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Type Package Title Granger Mediation Analysis Version 1.0 Date 2018-08-23 Package gma September 19, 2017 Author Yi Zhao , Xi Luo Maintainer Yi Zhao
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Package ForwardSearch February 19, 2015 Type Package Title Forward Search using asymptotic theory Version 1.0 Date 2014-09-10 Author Bent Nielsen Maintainer Bent Nielsen
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Type Package Package MicroMacroMultilevel July 1, 2017 Description Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g.,
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Type Package Package weightquant December 30, 2018 Title Weights for Incomplete Longitudinal Data and Quantile Regression Version 1.0 Date 2018-12-18 Author Viviane Philipps Maintainer Viviane Philipps
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Type Package Package My.stepwise June 29, 2017 Title Stepwise Variable Selection Procedures for Regression Analysis Version 0.1.0 Author International-Harvard Statistical Consulting Company
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Type Package Title Polynomials in R Package PolynomF May 17, 2018 Implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists
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Type Package Package OrdNor February 28, 2019 Title Concurrent Generation of Ordinal and Normal Data with Given Correlation Matrix and Marginal Distributions Version 2.2 Date 2019-02-24 Author Anup Amatya,