Package bmem. February 15, 2013

Transcription

1 Package bmem February 15, 2013 Type Package Title Mediation analysis with missing data using bootstrap Version 1.3 Date Author Maintainer Zhiyong Zhang Depends R (>= 1.7), sem, Amelia, MASS Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maimum Likelihood algorithm. For MI and TS- ML,auiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. License GPL-2 LazyLoad yes URL ZipData no Repository CRAN Repository/R-Forge/Project semdiag Repository/R-Forge/Revision 6 Date/Publication :50:12 NeedsCompilation no 1

2 2 R topics documented: R topics documented: bmem-package bmem bmem.bs bmem.ci.bc bmem.ci.bc bmem.ci.bca bmem.ci.bca bmem.ci.norm bmem.ci.p bmem.cov bmem.em bmem.em.boot bmem.em.cov bmem.em.jack bmem.em.rcov bmem.list bmem.list.boot bmem.list.cov bmem.list.jack bmem.mi bmem.mi.boot bmem.mi.cov bmem.mi.jack bmem.s bmem.pair bmem.pair.boot bmem.pair.cov bmem.pair.jack bmem.pattern bmem.plot bmem.raw2cov bmem.sem bmem.sobel bmem.sobel.ind bmem.ssq bmem.v plot.bmem summary.bmem Inde 26

3 bmem 3 bmem-package Mediation analysis with missing data using bootstrap Details Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two-stage ML. For MI and TSML, auiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. Package: bmem Type: Package Version: 1.0 Date: License: GPL-2 LazyLoad: yes Maintainer: Zhiyong Zhang <zhiyongzhang@nd.edu> bmem Mediation analysis based on bootstrap Mediation analysis based on bootstrap bmem(,,, v, method= tsml, ci= bc, cl=.95, boot=1000, m=10, varphi=.1, st= i, robust=false, ma_it=500, =FALSE,...) A vector of effec

4 4 bmem v method ci cl boot m varphi st robust ma_it Indices of variables used in the mediation model. If omitted, all variables are used. list: listwise deletion, pair: pairwise deletion, mi: multiple imputation, em: EM algorithm. norm: normal approimation CI, perc: percentile CI, bc: bias-corrected CI, bca: BCa Confidence level. Can be a vector. Number of bootstraps Number of imputations Percent of data to be downweighted Starting values Robust method Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. Maimum number of iterations in EM Details The effect can be specified using equations such as a*b, a*b+c, and a*b*c+d*e+f. A vector of effects can be used =c( a*b, a*b+c ). The on-screen output includes the paeter estimates, bootstrap standard errors, and CIs. References Zhang, Z. & Wang, L. (2012) Four methods for mediation analysis with missing data. Psychometrika. Zhang, Z. (2012) Robust mediation analysis with missing data and auiliary variables.

5 bmem.bs 5 bmem.bs Bootstrap but using the Bollen-Stine method The same as bmem but using the Bollen-Stine method bmem.bs(,,, v, ci= bc, cl=.95, boot=1000, ma_it=500,...) v ci cl boot ma_it A vector of effec Indices of variables used in the mediation model. If omitted, all variables are used. norm: normal approimation CI, perc: percentile CI, bc: bias-corrected CI, bca: BCa Confidence level. Can be a vector. Number of bootstraps Maimum number of iterations in EM The on-screen output includes the paeter estimates, bootstrap standard errors, and CIs. References Zhang, Z. & Wang, L. (2011) Four methods for mediation analysis with missing data. Zhang, Z. (2011) Robust mediation analysis with missing data and auiliary variables. See Also bmem, bmem.sobel, bmem.plot

6 6 bmem.ci.bc1 bmem.ci.bc Bias-corrected confidence intervals Bias-corrected confidence intervals bmem.ci.bc(par.boot, par0, cl=.95) par.boot par0 A bootstrap object. Original estimate cl Confidence level. Default BC confidence intervals. The output includes - estimates, bootstrap standard errors, and confidence intervals. See Also bmem.ci.norm, bmem.ci.p, bmem.ci.bca bmem.ci.bc1 Bias-corrected confidence intervals (for a single variable) Bias-corrected confidence intervals (for a single variable) bmem.ci.bc1(, b, cl = 0.95) A vector from a bootstrap output. b Paeter estimate from the original sample cl Confidence level. Default 0.95.

7 bmem.ci.bca 7 See Also bmem.ci.norm, bmem.ci.p, bmem.ci.bca bmem.ci.bca Bias-corrected and accelerated confidence intervals Bias-corrected and accelerated confidence intervals bmem.ci.bca(par.boot, par0, jack, cl = 0.95) par.boot A bootstrap object. par0 Original estimate jack A Jackknife object. cl Confidence level. Default BCa confidence intervals. The output includes - estimates, bootstrap standard errors, and confidence intervals. See Also bmem.ci.norm, bmem.ci.p, bmem.ci.bc, bmem.list.jack, bmem.pair.jack, bmem.mi.jack, bmem.em.jack,

8 8 bmem.ci.norm bmem.ci.bca1 BCa for a single variable BCa for a single variable bmem.ci.bca1(, b, jack, cl = 0.95) A vector from a bootstrap output. b Paeter estimate from the original sample jack A vector from a Jackknife analysis cl Confidence level. Default bmem.ci.norm Confidence interval based on normal approimation Confidence interval based on normal approimation bmem.ci.norm(par.boot, par0, cl = 0.95) par.boot A bootstrap object. par0 Original estimate cl Confidence level. Default Normal confidence intervals. The output includes - estimates, bootstrap standard errors, and confidence intervals. See Also bmem.ci.bca, bmem.ci.p, bmem.ci.bc

9 bmem.ci.p 9 bmem.ci.p Percentile confidence interval Percentile confidence interval bmem.ci.p(par.boot, par0, cl = 0.95) par.boot A bootstrap object. par0 Original estimate cl Confidence level. Default Percentile confidence intervals. The output includes - estimates, bootstrap standard errors, and confidence intervals. See Also bmem.ci.bca, bmem.ci.norm, bmem.ci.bc bmem.cov Calculate the covariance matri based on a given model Can be used to simulated data for an SEM model. bmem.cov(,obs.variables,=false, debug=false) obs.variables debug An model Names of the observed variables Whether to use the mean structure debug mode

10 10 bmem.em.boot bmem.em Estimate a mediation model based on EM covariance matri Estimate a mediation model based on EM covariance matri bmem.em(,,, v, robust = FALSE, varphi = 0.1, st= "i", = FALSE, ma_it = 500,...) v robust varphi st ma_it A vector of effec Indices of variables used in the mediation model. If omitted, all variables are used. Roubst method Percent of data to be downweighted Starting values Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. Maimum number of iterations in EM bmem.em.boot Bootstrap for EM Bootstrap for EM bmem.em.boot(,,, v, robust = FALSE, varphi = 0.1, st= "i", boot = 1000, = FALSE, ma_it = 500,...)

11 bmem.em.cov 11 v robust varphi st Details A vector of effec Indices of variables used in the mediation model. If omitted, all variables are used. Roubst method Percent of data to be downweighted Starting values boot Number of bootstraps. Default is ma_it Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. Maimum number of iterations in EM The effect can be specified using equations such as a*b, a*b+c, and a*b*c+d*e+f. A vector of effects can be used =c( a*b, a*b+c ). par.boot par0 Paeter estimates from bootstrap samples Paeter estimates from the orignal samples bmem.em.cov Covariance matri from EM Covariance matri from EM bmem.em.cov(mis, = FALSE, ma_it = 500) mis ma_it An object from output of bmem.pattern. Whether estimating mean Maimum number of iterations

12 12 bmem.em.rcov bmem.em.jack Jackknife estimate using EM Jackknife estimate using EM bmem.em.jack(,,, v, robust = FALSE, varphi = 0.1, st= "i", = FALSE, ma_it = 500,...) v robust varphi st ma_it A vector of effec Indices of variables used in the mediation model. If omitted, all variables are used. Roubst method Percent of data to be downweighted Starting values Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. Maimum number of iterations in EM bmem.em.rcov Estimation of robust covariance matri Estimation of robust covariance matri bmem.em.rcov(mis, varphi=.1, =FALSE, ma_it=1000, st= i )

13 bmem.list 13 mis varphi ma_it st Missing data pattern Percent of data to be downweighted Moment analysis if TRUE Maimum number of iteration Starting values An interval function to calculate the robust covaraince matri bmem.list Estimate a mediaiton model based on listwise deletion Estimate a mediaiton model based on listwise deletion bmem.list(,,, = FALSE,...) A vector of effec Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis.

14 14 bmem.list.cov bmem.list.boot Bootstrap for listwise deletion method Bootstrap for listwise deletion method bmem.list.boot(,,, boot = 1000, = FALSE,...) A vector of effec boot Number of bootstraps. Default is Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. bmem.list.cov Covariance matri for listwise deletion Covariance matri for listwise deletion bmem.list.cov(, = FALSE) Estimate mean or not

15 bmem.list.jack 15 bmem.list.jack Jackknife for listwise deletion Jackknife for listwise deletion bmem.list.jack(,,, = FALSE,...) A vector of effec Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. bmem.mi Estimate a mediation model based on multiple imputation Estimate a mediation model based on multiple imputation bmem.mi(,,, v, m = 10, = FALSE,...) v m A vector of effec Indices of variables used in the mediation model. If omitted, all variables are used. Number of imputations. Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis.

16 16 bmem.mi.cov bmem.mi.boot Bootstrap for multiple imputation Bootstrap for multiple imputation bmem.mi.boot(,,, v, m = 10, boot = 1000, = FALSE,...) v m A vector of effec Indices of variables used in the mediation model. If omitted, all variables are used. Number of imputations boot Number of bootstraps. Default is Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. bmem.mi.cov Covariance estimation for multiple imputation Covariance estimation for multiple imputation bmem.mi.cov(, m = 10, = FALSE) m Number of imputations Estimate mean or not

17 bmem.mi.jack 17 bmem.mi.jack Jackknife for multiple imputation Jackknife for multiple imputation bmem.mi.jack(,,, v, m = 10, = FALSE,...) v m A vector of effec Indices of variables used in the mediation model. If omitted, all variables are used. Number of imputations. Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. bmem.s Calculate the s of a data set Calculate the s of a data set using either listwise deletion or pairwise deletion bmem.s(, type=0) type How to deal with missing data. 0: listwise deletion; 1: pairwise deletion

18 18 bmem.pair.boot bmem.pair Estimate a mediaiton model based on pairwise deletion Estimate a mediaiton model based on pairwise deletion bmem.pair(,,, = FALSE,...) A vector of effec Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis. bmem.pair.boot Bootstrap for pairwise deletion Bootstrap for pairwise deletion bmem.pair.boot(,,, boot = 1000, = FALSE,...) A vector of effec boot Number of bootstraps. Default is Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis.

19 bmem.pair.cov 19 bmem.pair.cov Covariance matri estimation based on pairwise deletion Covariance matri estimation based on pairwise deletion bmem.pair.cov(, = FALSE) Estimate mean or not bmem.pair.jack Jackknife for pairwise deletion Jackknife for pairwise deletion bmem.pair.jack(,,, = FALSE,...) A vector of effec Select mean structure or covariance analysis. =FALSE, covariance analysis. =TRUE, mean and covariance analysis.

20 20 bmem.plot bmem.pattern Obtain missing data pattern information Obtain missing data pattern information bmem.pattern() bmem.plot Plot of the bootstrap distribution. This function is replaced by plot. Plot of the bootstrap distribution bmem.plot(, par,...) A bmem object par Name of paeter to be plotted.... Options used for the generic plot function. A plot References Zhang, Z. & Wang, L. (2011) Four methods for mediation analysis with missing data. Zhang, Z. (2011) Robust mediation analysis with missing data and auiliary variables. See Also bmem, bmem.sobel, bmem.plot

21 bmem.raw2cov 21 bmem.raw2cov Convert a raw matri to covariance matri Convert a raw matri to covariance matri bmem.raw2cov() A matri A covariance matri References Zhang, Z. & Wang, L. (2011) Four methods for mediation analysis with missing data. Zhang, Z. (2011) Robust mediation analysis with missing data and auiliary variables. See Also bmem, bmem.sobel, bmem.plot bmem.sem Estimate a mediaiton model using SEM technique Estimate a mediaiton model using SEM technique bmem.sem(,, N,, =FALSE,...)

22 22 bmem.sobel A covariance matri A path diag from specify.model N Sample size A vector of effects Whether mean strucuture is used. The default is FALSE... Options that can be supplied to function sem. See Also bmem.list.cov, bmem.pair.cov, bmem.mi.cov, bmem.em.cov bmem.sobel Mediation analysis using sobel test (for complete data only) Mediation analysis using sobel test (for complete data only) bmem.sobel(,,, =FALSE,...) A vector of effec Covariance or analysis The on-screen output includes the paeter estimates and sobel standard errors. References Zhang, Z. & Wang, L. (2011) Four methods for mediation analysis with missing data. Zhang, Z. (2011) Robust mediation analysis with missing data and auiliary variables. See Also bmem, bmem.sobel, bmem.plot

23 bmem.sobel.ind 23 bmem.sobel.ind Mediation analysis using sobel test for one effect Internal function bmem.sobel.ind(sem.object, ind) sem.object ind A sem object Indirect effect Internal output References Zhang, Z. & Wang, L. (2011) Four methods for mediation analysis with missing data. Zhang, Z. (2011) Robust mediation analysis with missing data and auiliary variables. See Also bmem, bmem.sobel, bmem.plot bmem.ssq Sum square of a matri Sum square of a matri bmem.ssq() A matri

24 24 plot.bmem bmem.v Select data according to a vector of indices Select data according to a vector of indices bmem.v(, v, = FALSE) v A matri A vector of indices Covariane analysis or mean and covariance analysis plot.bmem Plot of the bootstrap distribution Plot of the bootstrap distribution ## S3 method for class bmem plot(, par,...) A bmem object par Name of paeter to be plotted.... Options used for the generic plot function. Generate the bootstrap histog for a chosen paeter.

25 summary.bmem 25 References Zhang, Z. & Wang, L. (2011) Four methods for mediation analysis with missing data. Zhang, Z. (2011) Robust mediation analysis with missing data and auiliary variables. See Also bmem, bmem.sobel, bmem.plot summary.bmem Calculate bootstrap confidence intervals Calculate bootstrap confidence intervals ## S3 method for class bmem summary(object, ci= bc, cl=.95,...) object ci cl Details An output object from the function bmem norm: normal approimation CI, perc: percentile CI, bc: bias-corrected CI, bca: BCa Confidence level. Can be a vector.... other options can be used for the generic summary function. The other type of confidence intervals can be constructed from the output of the function bmem. Note if the BCa is required, the ci= BCa should have been specified in the function bmem. The on-screen output includes the paeter estimates, bootstrap standard errors, and CIs.

26 Inde Topic bmem bmem-package, 3 bmem, 3, 5, 20 23, 25 bmem-package, 3 bmem.bs, 5 bmem.ci.bc, 6, 7 9 bmem.ci.bc1, 6 bmem.ci.bca, 6, 7, 7, 8, 9 bmem.ci.bca1, 8 bmem.ci.norm, 6, 7, 8, 9 bmem.ci.p, 6 8, 9 bmem.cov, 9 bmem.em, 10 bmem.em.boot, 10 bmem.em.cov, 11, 22 bmem.em.jack, 7, 12 bmem.em.rcov, 12 bmem.list, 13 bmem.list.boot, 14 bmem.list.cov, 14, 22 bmem.list.jack, 7, 15 bmem.mi, 15 bmem.mi.boot, 16 bmem.mi.cov, 16, 22 bmem.mi.jack, 7, 17 bmem.s, 17 bmem.pair, 18 bmem.pair.boot, 18 bmem.pair.cov, 19, 22 bmem.pair.jack, 7, 19 bmem.pattern, 11, 20 bmem.plot, 5, 20, 20, 21 23, 25 bmem.raw2cov, 21 bmem.sem, 21 bmem.sobel, 5, 20 22, 22, 23, 25 bmem.sobel.ind, 23 bmem.ssq, 23 bmem.v, 24 plot (plot.bmem), 24 plot.bmem, 24 sem, 4, 5, 10 19, 22 summary (summary.bmem), 25 summary.bmem, 25 26