Package goftest. R topics documented: April 3, Type Package
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1 Type Package Package goftest April 3, 2017 Title Classical Goodness-of-Fit Tests for Univariate Distributions Version Date Depends R (>= 3.3) Imports stats Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit for continuous univariate distributions, using efficient algorithms. License GPL (>= 2) NeedsCompilation yes Author Julian Faraway [aut], George Marsaglia [aut], John Marsaglia [aut], Adrian Baddeley [aut, cre] Maintainer Adrian Baddeley <Adrian.Baddeley@curtin.edu.au> Repository CRAN Date/Publication :17:16 UTC R topics documented: goftest-package ad.test cvm.test pad pcvm recognisecdf Index 9 1
2 2 goftest-package goftest-package Classical Goodness-of-Fit Tests Cramér-von Mises and Anderson-Darling tests of goodness-of-fit for continuous univariate distributions, using modern algorithms to compute the null distributions. The goftest package contains implementations of the classical Cramér-von Mises and Anderson- Darling tests of goodness-of-fit for continuous univariate distributions. The Cramér-von Mises test is performed by cvm.test. The cumulative distribution function of the null distribution of the test statistic is computed by pcvm using the algorithm of Csörgő and Faraway (1996). The quantiles are computed by qcvm by root-finding. The Anderson-Darling test is performed by ad.test. The cumulative distribution function of the null distribution of the test statistic is computed by pad using the algorithm of Marsaglia and Marsaglia (2004). The quantiles are computed by qad by root-finding. Adrian Baddeley, Julian Faraway, John Marsaglia, George Marsaglia. Maintainer: Adrian Baddeley Csörgő, S. and Faraway, J.J. (1996) The exact and asymptotic distributions of Cramér-von Mises statistics. Journal of the Royal Statistical Society, Series B 58, Marsaglia, G. and Marsaglia, J. (2004) Evaluating the Anderson-Darling Distribution. Journal of Statistical Software 9 (2), 1 5. February ks.test x <- rnorm(10, mean=2, sd=1) cvm.test(x, "pnorm", mean=2, sd=1) ad.test(x, "pnorm", mean=2, sd=1)
3 ad.test 3 ad.test Anderson-Darling Test of Goodness-of-Fit Performs the Anderson-Darling test of goodness-of-fit to a specified continuous univariate probability distribution. ad.test(x, null = "punif",..., nullname) x null Numeric vector of data values. A function, or a character string giving the name of a function, to compute the cumulative distribution function for the null distribution.... Additional arguments for the cumulative distribution function. nullname Optional character string describing the null distribution. The default is "uniform distribution". This command performs the Anderson-Darling test of goodness-of-fit to the distribution specified by the argument null. It is assumed that the values in x are independent and identically distributed random values, with some cumulative distribution function F. The null hypothesis is that F is the function specified by the argument null, while the alternative hypothesis is that F is some other function. An object of class "htest" representing the result of the hypothesis test. Original C code by George Marsaglia and John Marsaglia. R interface by Adrian Baddeley. Anderson, T.W. and Darling, D.A. (1952) Asymptotic theory of certain goodness-of-fit criteria based on stochastic processes. Annals of Mathematical Statistics 23, Anderson, T.W. and Darling, D.A. (1954) A test of goodness of fit. Journal of the American Statistical Association 49, Marsaglia, G. and Marsaglia, J. (2004) Evaluating the Anderson-Darling Distribution. Journal of Statistical Software 9 (2), 1 5. February
4 4 cvm.test pad for the null distribution of the test statistic. x <- rnorm(10, mean=2, sd=1) ad.test(x, "pnorm", mean=2, sd=1) cvm.test Cramer-Von Mises Test of Goodness-of-Fit Performs the Cramér-von Mises test of goodness-of-fit to a specified continuous univariate probability distribution. cvm.test(x, null = "punif",..., nullname) x null Numeric vector of data values. A function, or a character string giving the name of a function, to compute the cumulative distribution function for the null distribution.... Additional arguments for the cumulative distribution function. nullname Optional character string describing the null distribution. The default is "uniform distribution". This command performs the Cramér-von Mises test of goodness-of-fit to the distribution specified by the argument null. It is assumed that the values in x are independent and identically distributed random values, with some cumulative distribution function F. The null hypothesis is that F is the function specified by the argument null, while the alternative hypothesis is that F is some other function. An object of class "htest" representing the result of the hypothesis test. Adrian Baddeley. Csörgő, S. and Faraway, J.J. (1996) The exact and asymptotic distributions of Cramér-von Mises statistics. Journal of the Royal Statistical Society, Series B 58,
5 pad 5 pcvm for the null distribution of the test statistic. x <- rnorm(10, mean=2, sd=1) cvm.test(x, "pnorm", mean=2, sd=1) pad Null Distribution of Anderson-Darling Test Statistic pad computes the cumulative distribution function, and qad computes the quantile function, of the null distribution of the Anderson-Darling test statistic. pad(q, n = Inf, lower.tail = TRUE, fast=true) qad(p, n = Inf, lower.tail = TRUE, fast=true) q p n lower.tail fast Numeric vector of quantiles (values for which the cumulative probability is required). Numeric vector of probabilities. Integer. Sample size for the Anderson-Darling test. Logical. If TRUE (the default), probabilities are P (X q), and otherwise they are P (X > q). Logical value indicating whether to use a fast algorithm or a slower, more accurate algorithm, in the case n=inf. pad uses the algorithms and C code described in Marsaglia and Marsaglia (2004). qad uses uniroot to find the quantiles. The argument fast applies only when n=inf and determines whether the asymptotic distribution is approximated using the faster algorithm adinf (accurate to 4-5 places) or the slower algorithm ADinf (accurate to 11 places) described in Marsaglia and Marsaglia (2004). A numeric vector of the same length as p or q.
6 6 pcvm Original C code by G. and J. Marsaglia. R interface by Adrian Baddeley. Anderson, T.W. and Darling, D.A. (1952) Asymptotic theory of certain goodness-of-fit criteria based on stochastic processes. Annals of Mathematical Statistics 23, Anderson, T.W. and Darling, D.A. (1954) A test of goodness of fit. Journal of the American Statistical Association 49, Marsaglia, G. and Marsaglia, J. (2004) Evaluating the Anderson-Darling Distribution. Journal of Statistical Software 9 (2), 1 5. February ad.test pad(1.1, n=5) pad(1.1) pad(1.1, fast=false) qad(0.5, n=5) qad(0.5) pcvm Null Distribution of Cramer-von Mises Test Statistic pcvm computes the cumulative distribution function, and qcvm computes the quantile function, of the null distribution of the Cramér-von Mises test statistic. pcvm(q, n = Inf, lower.tail = TRUE) qcvm(p, n = Inf, lower.tail = TRUE) q p n lower.tail Numeric vector of quantiles (values for which the cumulative probability is required). Numeric vector of probabilities. Integer. Sample size for the Cramér-von Mises test. Logical. If TRUE (the default), probabilities are P (X q), and otherwise they are P (X > q).
7 recognisecdf 7 For finite n the cumulative distribution function is approximated by the first order expansion V (x)+ ψ 1 (x)/n, equation (1.8) of Csörgö and Faraway (1996). qcvm uses uniroot to find the quantiles. A numeric vector of the same length as p or q. Original Matlab code by Julian Faraway, translated to R by Adrian Baddeley. Csörgő, S. and Faraway, J.J. (1996) The exact and asymptotic distributions of Cramér-von Mises statistics. Journal of the Royal Statistical Society, Series B 58, cvm.test pcvm(1.1, n=5) pcvm(1.1) qcvm(0.5, n=5) qcvm(0.5) recognisecdf Explanatory Name of Distribution Function Recognises many standard cumulative distribution functions and returns a string describing the distribution. recognisecdf(s="punif") s A single character string giving the name of an R function that calculates cumulative probabilities.
8 8 recognisecdf The list of recognised distribution functions includes all those available in the stats package and in goftest. Character string, or NULL if the name is not recognised. Adrian Baddeley. pad recognisecdf("punif") recognisecdf("pt")
9 Index Topic distribution pad, 5 pcvm, 6 recognisecdf, 7 Topic htest ad.test, 3 cvm.test, 4 goftest-package, 2 pad, 5 pcvm, 6 Topic package goftest-package, 2 ad.test, 2, 3, 6 cvm.test, 2, 4, 7 goftest (goftest-package), 2 goftest-package, 2 ks.test, 2 pad, 2, 4, 5, 8 pcvm, 2, 5, 6 qad, 2 qad (pad), 5 qcvm, 2 qcvm (pcvm), 6 recognisecdf, 7 uniroot, 5, 7 9
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Package November 29, 2016 Title Bayesian Effect Fusion for Categorical Predictors Version 1.0 Date 2016-11-21 Author Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut] Maintainer
Package bpp. December 13, 2016
Type Package Package bpp December 13, 2016 Title Computations Around Bayesian Predictive Power Version 1.0.0 Date 2016-12-13 Author Kaspar Rufibach, Paul Jordan, Markus Abt Maintainer Kaspar Rufibach Depends
Package CCP. R topics documented: December 17, Type Package. Title Significance Tests for Canonical Correlation Analysis (CCA) Version 0.
Package CCP December 17, 2009 Type Package Title Significance Tests for Canonical Correlation Analysis (CCA) Version 0.1 Date 2009-12-14 Author Uwe Menzel Maintainer Uwe Menzel to
Package PVR. February 15, 2013
Package PVR February 15, 2013 Type Package Title Computes phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation curve (PSR) (with null and Brownian expectations) Version 0.2.1
Package rnmf. February 20, 2015
Type Package Title Robust Nonnegative Matrix Factorization Package rnmf February 20, 2015 An implementation of robust nonnegative matrix factorization (rnmf). The rnmf algorithm decomposes a nonnegative
Package msu. September 30, 2017
Package msu September 30, 2017 Title Multivariate Symmetric Uncertainty and Other Measurements Version 0.0.1 Estimators for multivariate symmetrical uncertainty based on the work of Gustavo Sosa et al.
Package sbgcop. May 29, 2018
Package sbgcop May 29, 2018 Title Semiparametric Bayesian Gaussian Copula Estimation and Imputation Version 0.980 Date 2018-05-25 Author Maintainer Estimation and inference for parameters