Title: Analyze Clustered Data with Generalized Linear Models using the Cluster Bootstrap
Date: 2021-02-16
Version: 1.1.2
Description: Provides functionality for the analysis of clustered data using the cluster bootstrap.
Depends: R (≥ 3.0), stats, utils, graphics, parallel, magrittr, dplyr
License: GPL-3 | file LICENSE
URL: https://github.com/mathijsdeen/ClusterBootstrap
BugReports: https://github.com/mathijsdeen/ClusterBootstrap/issues
Maintainer: Mathijs Deen <m.l.deen@fsw.leidenuniv.nl>
LazyData: true
RoxygenNote: 7.1.1
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-02-16 11:31:31 UTC; mathijs
Author: Mathijs Deen [aut, cre], Mark de Rooij [aut]
Repository: CRAN
Date/Publication: 2021-02-16 12:00:02 UTC

Fit generalized linear models with the cluster bootstrap

Description

Fit a generalized linear model with the cluster bootstrap for analysis of clustered data.

Usage

clusbootglm(
  model,
  data,
  clusterid,
  family = gaussian,
  B = 5000,
  confint.level = 0.95,
  n.cores = 1
)

Arguments

model

generalized linear model to be fitted with the cluster bootstrap. This should either be a formula (or be able to be interpreted as one) or a glm / lm object. From the (g)lm objects, the formula will be used.

data

dataframe that contains the data.

clusterid

variable in data that identifies the clusters.

family

error distribution to be used in the model, e.g. gaussian or binomial.

B

number of bootstrap samples.

confint.level

level of confidence interval.

n.cores

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

Parameter estimates from a single (generalized) linear model.

boot.coefs

Mean values of the paramater estimates, derived from the bootstrap coefficients.

boot.sds

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.

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.

Author(s)

Mathijs Deen, Mark de Rooij

Examples

## Not run: 
data(opposites)
clusbootglm(SCORE~Time*COG,data=opposites,clusterid=Subject)
## End(Not run)

Return data for specified bootstrap sample

Description

Returns the full data frame for a specified bootstrap sample in a clusbootglm object.

Usage

clusbootsample(object, samplenr)

Arguments

object

object of class clusbootglm, created with the clusbootglm function.

samplenr

sample number for which the data frame should be returned.

Author(s)

Mark de Rooij, Mathijs Deen

Examples

## Not run: 
data(opposites)
cbglm.1 <- clusbootglm(SCORE~Time*COG,data=opposites,clusterid=Subject)
clusbootsample(cbglm.1, samplenr=1)
## End(Not run)

Obtain coefficients from cluster bootstrap object

Description

Returns the coefficients of an object of class clusbootglm.

Usage

## S3 method for class 'clusbootglm'
coef(object, estimate.type = "bootstrap", ...)

Arguments

object

object of class clusbootglm.

estimate.type

type of coefficient (bootstrap or GLM).

...

other arguments.

Author(s)

Mathijs Deen

Examples

## Not run: 
data(opposites)
cbglm.1 <- clusbootglm(SCORE~Time*COG,data=opposites,clusterid=Subject)
coef(cbglm.1, estimate.type="bootstrap")
## End(Not run)

Confidence intervals for cluster bootstrap model parameters

Description

Computes confidence intervals for one or more parameters in a fitted GLM with the cluster bootstrap.

Usage

## S3 method for class 'clusbootglm'
confint(object, parm = "all", level = 0.95, interval.type = "BCa", ...)

Arguments

object

object of class clusbootglm.

parm

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.

level

the required confidence level

interval.type

type of confidence level. Options are BCa, percentile, and parametric.

...

other arguments.

Author(s)

Mathijs Deen

Examples

## Not run: 
data(opposites)
cbglm.1 <- clusbootglm(SCORE~Time*COG,data=opposites,clusterid=Subject)
confint(cbglm.1,parm=c("Time","COG"), level=.90, interval.type="percentile")
## End(Not run)

Calculate estimated marginal means for a cluster bootstrap GLM

Description

Returns the estimated marginal means of an clusbootglm object. This function works with a maximum of one between-subjects and one within-subjects variable.

Usage

emm(object, confint.level = 0.95)

Arguments

object

object of class clusbootglm.

confint.level

level of the confidence interval.

Value

emmeans returns an object of class clusbootemm, containing the following components:

grid

Grid with estimated marginal means for each combination of levels of the variables.

bootstrapsample.emm

p*B matrix, with p being the number of estimates and B being the number of bootstrap samples.

Author(s)

Mathijs Deen

Examples

## Not run: 
medication <- medication[medication$time %% 1 == 0,]
medication$time_f <- as.factor(medication$time)
set.seed(1)
model.1 <- clusbootglm(pos~time_f*treat, clusterid = id, data = medication)
emm.1 <- emm(object = model.1)
summary(object = emm.1)
## End(Not run)

Medication data

Description

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 https://stats.idre.ucla.edu/other/examples/alda/.

Usage

medication

Format

the following variables are available:

References

Opposites naming data

Description

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.

Usage

opposites

Format

the following variables are available:

References

Plot estimated marginal means for a cluster bootstrap GLM

Description

Plots the estimated marginal means of an clusbootglm object. Works with one within-subjects and/or one between-subjects variable.

Usage

## S3 method for class 'clusbootemm'
plot(
  x,
  within,
  between,
  pch,
  lty,
  ylab = "Estimated marginal mean",
  xlab = "Within subject",
  ...
)

Arguments

x

object of class clusbootemm.

within

within-subjects variable. Should be numeric or numerically labeled factor.

between

between-subjects variable.

pch

point character. Length must be equal to the number of between-subjects levels.

lty

linetype. Length must be equal to the number of between-subjects levels.

ylab

label for y-axis.

xlab

label for x-axis.

...

other arguments to be passed to the plot function (see par).

Author(s)

Mathijs Deen

Examples

## Not run: 
medication <- medication[medication$time %% 1 == 0,]
medication$time_f <- as.factor(medication$time)
set.seed(1)
model.1 <- clusbootglm(pos~time_f*treat, clusterid=id, data=medication)
emm.1 <- emm(object = model.1)
plot(x = emm.1, within = time_f, between = treat, pch = c(15,17), lty = c(1,2))
## End(Not run)

Plot results of a permutation test

Description

Plot results of a permutation test performed with ptest

Usage

## S3 method for class 'clusbootptest'
plot(x, pcol = "red", pty = 1, mfrow = c(1, 1), ...)

Arguments

x

object of class clusbootptest

pcol

color of vertical line indicating the observed Welch t test statistic

pty

type of vertical line indicating the observed Welch t test statistic

mfrow

vector of length 2 indicating the numbers of rows and columns in which the histograms will be drawn on the device.

...

other arguments to be passed into the hist function.

Author(s)

Mathijs Deen, Mark de Rooij

Examples

## Not run: 
medication <- medication[medication$time %% 1 == 0,]
set.seed(1)
permtest.1 <- ptest(data = meds, outcome = pos, within = time, between = treat, 
                    at.within = c(0,2,4,6), at.between = c(0,1), pn = 2000)
plot(permtest.1, pcol = "red", pty=2, mfrow = c(2,2), breaks="FD")
## End(Not run)

Permutation test for group differences at within-subject levels

Description

Perform permutation tests for differences between two groups at given within-subject levels in a long-formatted dataframe

Usage

ptest(
  data,
  outcome,
  within,
  between,
  at.within,
  at.between,
  pn = 1000,
  progress.bar = TRUE
)

Arguments

data

dataframe that contains the data in long format.

outcome

outcome variable (i.e., the variable for which the difference should be tested).

within

within-subject variable.

between

between-subjects variable.

at.within

determine for which within-subject levels (e.g., which timepoint) the difference should be tested.

at.between

determine the groups in the difference test (should always be of length 2).

pn

the number of permutations that should be performed.

progress.bar

indicates whether a progress bar will be shown.

Details

In every permutation cycle, the outcome variable gets permutated and the Welch t test statistic is calculated.

Value

ptest produces an object of class "clusbootptest", containing the following relevant components:

perm.statistics

A matrix of length(at.within) rows and pn columns, containing the Welch t-test statics for all permutations within the at.within level in the columns. The first column contains the t statistic for the observed data.

pvalues

Data frame containing the p values for every at.within level.

Author(s)

Mathijs Deen, Mark de Rooij

See Also

A useful method for the obtained clusbootptest class object is plot.clusbootptest.

Examples

## Not run: 
medication <- medication[medication$time %% 1 == 0,]
set.seed(1)
permtest.1 <- ptest(data = meds, outcome = pos, within = time, between = treat, 
                    at.within = c(0,2,4,6), at.between = c(0,1), pn = 2000)
permtest.1$pvalues
## End(Not run)

Summarize estimated marginal means for cluster bootstrap GLM into a grid

Description

Returns the summary of the EMM for a clusbootglm class object.

Usage

## S3 method for class 'clusbootemm'
summary(object, ...)

Arguments

object

object of class clusbootemm.

...

other arguments.

Author(s)

Mathijs Deen

Examples

## Not run: 
medication <- medication[medication$time %% 1 == 0,]
medication$time_f <- as.factor(medication$time)
set.seed(1)
model.1 <- clusbootglm(pos~time_f*treat, clusterid=id, data=medication)
emm.1 <- emm(object = model.1)
summary(object = emm.1)
## End(Not run)

Summarize output of cluster bootstrap GLM

Description

Returns the summary of an object of class clusbootglm.

Usage

## S3 method for class 'clusbootglm'
summary(object, estimate.type = "bootstrap", interval.type = "BCa", ...)

Arguments

object

object of class clusbootglm.

estimate.type

specify which type of estimate should be returned, either bootstrap means (default) or GLM estimates from model fitted on original data.

interval.type

which confidence interval should be used. Options are parametric, percentile, and BCa intervals.

...

other arguments.

Author(s)

Mathijs Deen

Examples

## Not run: 
data(opposites)
cbglm.1 <- clusbootglm(SCORE~Time*COG,data=opposites,clusterid=Subject)
summary(cbglm.1, interval.type="percentile")
## End(Not run)

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