Package BinNor. February 10, 2018
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1 Type Package Package BinNor February 10, 2018 Title Simultaneous Generation of Multivariate Binary and Normal Variates Version 2.2 Date Author Anup Amatya, Hakan Demirtas Maintainer Anup Amatya Depends mvtnorm, corpcor, psych, Matrix Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012). License GPL-2 LazyLoad yes NeedsCompilation no Repository CRAN Date/Publication :44:58 UTC R topics documented: BinNor-package compute.sigma.star jointly.generate.binary.normal lower.tri.to simulation validation.bin validation.corr validation.nor validation.range Index 10 1
2 2 BinNor-package BinNor-package A package for simultaneous generation of binary and normal data. Details Provides R functions for generating multiple binary and normal variables simultaneously given marginal characteristics and association structure via combining well established results from the random number generation literature, based on the methodology proposed by Demirtas and Doganay (2012). Package: BinNor Type: Package Version: 2.2 Date: License: GPL-2 LazyLoad: yes There are eight functions in this package. The functions lower.tri.to., validation.bin, validation.nor, validation.range and validation.nor are designed to prevent obvious specification errors and to validate the specified quantities. The most important functions are compute.sigma.star, jointly.generate.binary.normal and simulation. The function compute.sigma.star computes the matrix of tetrachoric correlations that will be used in the generation of multivariate normal data whose some components are dichotomized to obtain binary variables. The function jointly.generate.binary.normal generates mixed data, and the function simulation is capable of repating this process many times and produces averages of some key statistical quantities across replications. Author(s) Anup Amatya, Hakan Demirtas Maintainer: Anup Amatya<aamatya@nmsu.edu> References Demirtas, H., Doganay, B. (2012). Simultaneous generation of binary and normal data with specified marginal and association structures. Journal of Biopharmaceutical Statistics; 22(2), Demirtas, H., Amatya, A., and Doganay, B. (2014). BinNor: An R package for con-current generation of binary and normal data. Communications in Statistic-Simulation and Computation; 43(3),
3 compute.sigma.star 3 compute.sigma.star Computes intermediate (tetrachoric) correlation matrix This function computes the intermediate correlation matrix by combining tetrachoric correlation for binary-binary combinations, biserial correlations for binary-normal combinations and Pearson correlation for normal-normal combinations. If the resulting correlation matrix is not positive definite, a nearest positive matrix will be used. compute.sigma.star(,, = NULL, corr.vec = NULL, = NULL) corr.vec Number of binary variables Vector of elements below the diagonal of correlation matrix ordered columnwise Specified correlation matrix Value sigma_star nonpd PD eigenv A resulting intermediate correlation matrix Σ If a resulting intermediate correlation matrix is non-positive definite, it is stored in this value. Otherwise it is NULL. TRUE if Σ is positive definite, FALSE otherwise. A FALSE indicates that the nearest positive definite matrix is returned. Eigenvalues of the Σ before the conversion See Also validation.corr, nearpd, phi2poly, is.positive.definite, jointly.generate.binary.normal, simulation cmat = lower.tri.to.(corr.vec= c(0.16, 0.04, 0.38, 0.14, 0.47, 0.68),4) compute.sigma.star(=2, =2, =c(0.4,0.7), corr.vec=null,=cmat)
4 4 jointly.generate.binary.normal jointly.generate.binary.normal Generates mixed data Generates multiple binary and normal variables simultaneously given marginal characteristics and association structures. jointly.generate.binary.normal(no.rows,,, = NULL, mean.vec.nor = NULL, var.nor = NULL, sigma_star = NULL, corr.vec = NULL, = NULL, continue.with.warning = TRUE) no.rows mean.vec.nor var.nor sigma_star corr.vec Number of rows. Number of binary variables Vector of means for normal variables Vector of variances for normal variables Intermidiate correlation matrix Vector of elements below the diagonal of correlation matrix ordered columnwise Specified correlation matrix continue.with.warning TRUE to proceed with the nearest positive definite Σ. FALSE to terminate program execution if Σ is not positive definite Value data A matrix of generated data. See Also compute.sigma.star, validation.corr, validation.bin, validation.nor, nearpd, simulation, rmvnorm
5 lower.tri.to. 5 no.rows=100 =2; =2 mean.vec.nor=c(3,1); var.nor=c(4,2) =c(0.4,0.7) corr.vec=c(0.16,0.04,0.38,0.14,0.47,0.68); cmat = lower.tri.to.(corr.vec,4) sigma.star=compute.sigma.star(=2, =2, =c(0.4,0.7), =cmat) mydata=jointly.generate.binary.normal(no.rows,,,, mean.vec.nor,var.nor, sigma_star=sigma.star$sigma_star, continue.with.warning=true) lower.tri.to. Converts a lower vector to a full correlation matrix This function creates full correlation matrix from the vector containing elements below the diagonal. lower.tri.to.(corr.vec = NULL, d) corr.vec d A vector of elements below daigonal of correlation matrix. The elements must be ordered starting from first element below diagonal of the first column, then second element below diagonal of the first column and so on. Number of column in final correlation matrix. Value Full correlation matrix See Also lower.tri corr.vec=c(0.16,0.04,0.38,0.14,0.47,0.68) lower.tri.to.(corr.vec,4)
6 6 simulation simulation Repeats the data generation process in a simulation scheme Simulates many versions of mixed data, and reports averaged proportion, mean, variance and correlation estimates across replications. simulation(seed = NULL, nsim, no.rows,,, mean.vec.nor = NULL, var.nor = NULL, = NULL, corr.vec = NULL, = NULL, continue.with.warning = TRUE) seed nsim no.rows mean.vec.nor var.nor corr.vec A seed value for the random number generator. Seed value will be randomly generated unless specified. Number of simulation runs. Number of rows. Number of binary variables Vector of means for normal variables Vector of variances for normal variables Vector of elements below the diagonal of correlation matrix ordered columnwise Specified correlation matrix continue.with.warning TRUE to proceed with the nearest positive definite Σ. FALSE to terminate program execution if Σ is not positive definite See Also compute.sigma.star, jointly.generate.binary.normal simulation(nsim=10, no.rows=100, =2, =2, mean.vec.nor=c(3,1), var.nor=c(4,2), =c(0.4,0.7), corr.vec=c(0.16,0.04,0.38,0.14,0.47,0.68), =NULL)
7 validation.bin 7 validation.bin Validates the marginal specification of the binary part Checks whether the marginal specification of the binary part is valid and consistent. validation.bin(, = NULL) Number of binary variates. ## Not run: validation.bin (3, rep(0.6,4)) validation.bin (4, rep(0.6,4)) validation.corr Validates the specified correlation matrix This function validates the correlation vector and/or matrix for appropriate dimension, symmetry, range, and positive definiteness. If both correlation matrix and correlation vector were supplied, it checks whether the matrix and vector are conformable. validation.corr(,, = NULL, corr.vec = NULL, = NULL) corr.vec Number of binary variables Vector of elements below the diagonal of correlation matrix ordered columnwise Specified correlation matrix
8 8 validation.range See Also validation.bin, validation.range d=4 corr.vec=c(0.21,0.61,0.78,0.10,0.12,0.65) =lower.tri.to.(corr.vec,d) validation.corr (=2, =2,=c(0.4,0.7), corr.vec,=) validation.nor Validates the marginal specification of the normal part This function checks whether mean and variance parameters for the normal part are valid and consistent. validation.nor(, mean.vec.nor = NULL, var.nor = NULL) mean.vec.nor var.nor Vector of means for normal variables Vector of variances for normal variables validation.range Checks if the correlation terms are within the feasible range This function checks if there are correlation range violations among binary-binary, binary-normal and normal-normal combinations. validation.range(,, = NULL, )
9 validation.range 9 Number of binary variables Specified correlation matrix cmat=lower.tri.to.(corr.vec=c(0.16,0.04,0.38,0.4,0.47,0.68),4) validation.range(=2, =2, =c(0.4,0.7), =cmat)
10 Index BinNor (BinNor-package), 2 BinNor-package, 2 compute.sigma.star, 2, 3, 4, 6 is.positive.definite, 3 jointly.generate.binary.normal, 2, 3, 4, 6 lower.tri, 5 lower.tri.to., 2, 5 nearpd, 3, 4 phi2poly, 3 rmvnorm, 4 simulation, 2 4, 6 validation.bin, 2, 4, 7, 8 validation.corr, 3, 4, 7 validation.nor, 2, 4, 8 validation.range, 2, 8, 8 10
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Package GGMselect. February 1, 2018
Type Package Title Gaussian Graphs Models Selection Version 0.1-12.1 Date 2017-04-06 Package GGMselect February 1, 2018 Author Annie Bouvier, Christophe Giraud, Sylvie Huet, Verzelen N. Maintainer ORPHANED
Package ebdbnet. February 15, 2013
Type Package Package ebdbnet February 15, 2013 Title Empirical Bayes Estimation of Dynamic Bayesian Networks Version 1.2.2 Date 2012-05-30 Author Maintainer
Package lhs. R topics documented: January 4, 2018
Package lhs January 4, 2018 Version 0.16 Date 2017-12-23 Title Latin Hypercube Samples Author [aut, cre] Maintainer Depends R (>= 3.3.0) Suggests RUnit Provides a number of methods
Package sglr. February 20, 2015
Package sglr February 20, 2015 Type Package Title An R package for power and boundary calculations in pre-licensure vaccine trials using a sequential generalized likelihood ratio test Version 0.7 Date
Package SIDES. May 30, 2017
Type Package Package SIDES May 30, 2017 Title Subgroup Identification Based on Differential Effect Search Version 1.11 Date 2017-05-30 Author Marie-Karelle Riviere Maintainer Marie-Karelle Riviere