Package ClusterBootstrap
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1 Package ClusterBootstrap June 26, 2018 Title Analyze Clustered Data with Generalized Linear Models using the Cluster Bootstrap Date Version Provides functionality for the analysis of clustered data using the cluster bootstrap. Depends R (>= 3.0), stats, utils, graphics, parallel License GPL-3 URL BugReports Maintainer Mathijs Deen <m.l.deen@fsw.leidenuniv.nl> LazyData true RoxygenNote NeedsCompilation no Author Mathijs Deen [aut, cre], Mark de Rooij [aut] Repository CRAN Date/Publication :39:06 UTC R topics documented: clusbootglm clusbootsample coef.clusbootglm confint.clusbootglm medication opposites summary.clusbootglm Index 8 1
2 2 clusbootglm clusbootglm Fit generalized linear models with the cluster bootstrap Fit a generalized linear model with the cluster bootstrap for analysis of clustered data. clusbootglm(model, data, clusterid, family = gaussian, B = 5000, confint.level = 0.95, n.cores = 1) model data clusterid family B confint.level n.cores generalized linear model to be fitted with the cluster bootstrap. dataframe that contains the data. variable in data that identifies the clusters. error distribution to be used in the model, e.g. gaussian or binomial. number of bootstrap samples. level of confidence interval. number of CPU cores to be used. Details Some useful methods for the obtained clusbootglm class object are summary.clusbootglm, coef.clusbootglm, and clusbootsample. Value clusbootglm produces an object of class "clusbootglm", containing the following relevant components: coefficients A matrix of B rows, containing the parameter estimates for all bootstrap samples. bootstrap.matrix n*b matrix, of which each column represents a bootstrap sample; each value in a column represents a unit of subjectid. lm.coefs boot.coefs boot.sds Parameter estimates from a single (generalized) linear model. Mean values of the paramater estimates, derived from the bootstrap coefficients. Standard deviations of cluster bootstrap parameter estimates. ci.level User defined confidence interval level. percentile.interval Confidence interval based on percentiles, given the user defined confidence interval level.
3 clusbootsample 3 parametric.interval Confidence interval based on lm.coefs and column standard deviations of coefficients, given the user defined confidence interval level. BCa.interval Confidence interval based on percentiles with bias correction and acceleration, given the user defined confidence interval level. samples.with.na.coef Cluster bootstrap sample numbers with at least one coefficient being NA. failed.bootstrap.samples For each of the coefficients, the number of failed bootstrap samples are given. Mathijs Deen, Mark de Rooij clusbootglm(score~time*cog,data=opposites,clusterid=subject) clusbootsample Return data for specified bootstrap sample Returns the full data frame for a specified bootstrap sample in a clusbootglm object. clusbootsample(object, samplenr) object samplenr object of class clusbootglm, created with the clusbootglm function. sample number for which the data frame should be returned. Mark de Rooij, Mathijs Deen cbglm.1 <- clusbootglm(score~time*cog,data=opposites,clusterid=subject) clusbootsample(cbglm.1, samplenr=1)
4 4 confint.clusbootglm coef.clusbootglm Obtain coefficients from cluster bootstrap object Returns the coefficients of an object of class clusbootglm. ## S3 method for class 'clusbootglm' coef(object, estimate.type = "bootstrap",...) object object of class clusbootglm. estimate.type type of coefficient (bootstrap or GLM).... other arguments. Mathijs Deen cbglm.1 <- clusbootglm(score~time*cog,data=opposites,clusterid=subject) coef(cbglm.1, estimate.type="bootstrap") confint.clusbootglm Confidence intervals for cluster bootstrap model parameters Computes confidence intervals for one or more parameters in a fitted GLM with the cluster bootstrap. ## S3 method for class 'clusbootglm' confint(object, parm = "all", level = 0.95, interval.type = "BCa",...)
5 medication 5 object parm level interval.type object of class clusbootglm. a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. Defaults to all parameters. the required confidence level... other arguments. type of confidence level. Options are BCa, percentile, and parametric. Mathijs Deen cbglm.1 <- clusbootglm(score~time*cog,data=opposites,clusterid=subject) confint(cbglm.1,parm=c("time","cog"), level=.90, interval.type="percentile") medication Medication data The medication dataframe consists of 1242 observations within 73 individuals that were part of a placebo controlled clinical trial, as reported in Tomarken, Shelton, Elkins, and Anderson (1997). The data were retrieved from the accompanied website of Singer & Willett (2003), at medication Format the following variables are available: id: subject indicator treatment: either placebo (0) or antidepressant (1) time: number of days since trial start. pos: positive affect. Higher scores indicate a more positive mood.
6 6 opposites References Singer, J.D., & Willett, J.B. (2003). Applied longitudinal data analysis. Modeling change and event occurence. NY: Oxford University Press, Inc. Tomarken, A.J., Shelton, R.C., Elkins, L., & Anderson, T (1997). Sleep deprivation and anti-depressant medication: Unique effects on positive and negative affect. Poster session presented at the 9th annual meeting of the American Psychological Society, Washington, DC. opposites Opposites naming data The opposites dataframe consists of 144 observations within 36 individuals that completed an inventory that assesses their performance on a timed cognitive task called "opposites naming". The dataset does not contain the empirical data within 35 individuals from the experiment by Willett (1988), but a simulation based on the multilevel model from Singer & Willett (2003) within 36 individuals. opposites Format the following variables are available: Subject: subject indicator Time: a time variable, ranging 0-3 COG: cognitive skill, measured once (at time=0) SCORE: score on opposites naming task References Willett, J.B. (1988). Questions and answers in the measurement of change. In: E. Rothkopf (Ed.), Review of research in education ( ) (pp ). Washington, DC: American Educational Research Association. Singer, J.D., & Willett, J.B. (2003). Applied longitudinal data analysis. Modeling change and event occurence. NY: Oxford University Press, Inc.
7 summary.clusbootglm 7 summary.clusbootglm Summarize output of cluster bootstrap GLM Returns the summary of an object of class clusbootglm. ## S3 method for class 'clusbootglm' summary(object, estimate.type = "bootstrap", interval.type = "BCa",...) object estimate.type interval.type object of class clusbootglm.... other arguments. Mathijs Deen specify which type of estimate should be returned, either bootstrap means (default) or GLM estimates from model fitted on original data. which confidence interval should be used. Options are parametric, percentile, and BCa intervals. cbglm.1 <- clusbootglm(score~time*cog,data=opposites,clusterid=subject) summary(cbglm.1, interval.type="percentile")
8 Index clusbootglm, 2 clusbootsample, 2, 3 coef.clusbootglm, 2, 4 confint.clusbootglm, 4 medication, 5 opposites, 6 summary.clusbootglm, 2, 7 8
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