DESCRIPTION

Package: eefAnalytics
Version: 1.0.11
Date: 2021-02-11
Title: Robust Analytical Methods for Evaluating Educational
        Interventions using Randomised Controlled Trials Designs
Description: Analysing data from evaluations of educational interventions using a randomised controlled trial design. Various analytical tools to perform sensitivity analysis using different methods are supported (e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The included commands can be used for simple randomised trials, cluster randomised trials and multisite trials. The methods can also be used more widely beyond education trials. This package can be used to evaluate other intervention designs using Frequentist and Bayesian multilevel models.
Author: Germaine Uwimpuhwe, Akansha Singh, Steve Higgins, ZhiMin Xiao, Ewoud De Troyer and Adetayo Kasim
Maintainer: Germaine Uwimpuhwe <germaine.uwimpuhwe@durham.ac.uk>
Imports: lme4, mvtnorm, graphics, stats, rstanarm, ggplot2, methods
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
URL: https://github.com/germaine86/eefAnalytics
BugReports: https://github.com/germaine86/eefAnalytics/issues

`ComparePlot`: A plot function to compare different eefAnalytics S3 objects from the eefAnalytics package.

Description

It generates bar plot that compares the effect size from eefAnalytics’ methods.

Usage

ComparePlot(
  eefAnalyticsList,
  group,
  Conditional = TRUE,
  ES_Total = TRUE,
  modelNames
)

Arguments

Argument	Description
`eefAnalyticsList`	A list of eefAnalytics S3 objects from eefAnalytics package.
`group`	a string/scalar value indicating which intervention to plot. This must be one of the values of intervention variable excluding the control group. For a two arm trial, the maximum number of values to consider is 1 and 2 for three arm trial.
`Conditional`	a logical value to indicate whether to plot conditional effect size. The default is Conditional=TRUE, otherwise Conditional=FALSE should be specified for plot based on unconditional effect size. Conditional variance is total or residual variance a multilevel model with fixed effects, whilst unconditional variance is total variance or residual variance from a multilevel model with only intercept as fixed effect.
`ES_Total`	A logical value indicating whether to plot the effect size based on total variance or within school variance. The default is ES_Total=TRUE, to plot effect size using total variance. ES_Total=FALSE should be specified for effect size based on within school or residuals variance.
`modelNames`	a string factor containing the names of model to compare. See examples below.

Details

ComparePlot produces a bar plot which compares the effect sizes and the associated confidence intervals from the different models. For a multilevel model, it shows the effect size based on residual variance and total variance.

Value

Returns a bar plot to compare the different methods. The returned figure can be further modified as any ggplot

Examples

 
 
 data(mstData)
 ###############
 ##### SRT #####
 ###############
 
 outputSRT <- srtFREQ(Posttest~ Intervention + Prettest,
 intervention = "Intervention", data = mstData)
 
 outputSRTBoot <- srtFREQ(Posttest~ Intervention + Prettest,
 intervention = "Intervention",nBoot=1000, data = mstData)
 
 ###############
 ##### MST #####
 ###############
 
 outputMST <- mstFREQ(Posttest~ Intervention + Prettest,
 random = "School", intervention = "Intervention", data = mstData)
 
 outputMSTBoot <- mstFREQ(Posttest~ Intervention + Prettest,
 random = "School", intervention = "Intervention",
 nBoot = 1000, data = mstData)
 
 ##################
 ##### Bayesian #####
 ##################
 
 outputSRTbayes <- srtBayes(Posttest~ Intervention + Prettest,
 intervention = "Intervention",
 nSim = 2000, data = mstData)
 
 ## comparing different results
 
 ComparePlot(list(outputSRT,outputSRTBoot,outputMST,outputMSTBoot,outputSRTbayes),
 modelNames =c("ols", "olsBoot","MLM","MLMBoot","OLSBayes"),group=1)

`crtBayes`: Bayesian analysis of cluster randomised education trials using Vague Priors.

Description

crtBayes performs analysis of cluster randomised education trials using a multilevel model under a Bayesian setting, assuming vague priors.

Usage

crtBayes(
  formula,
  random,
  intervention,
  baseln,
  adaptD,
  nsim = 2000,
  data,
  threshold = 1:10/10,
  ...
)

Arguments

Argument	Description
`formula`	the model to be analysed is of the form y ~ x1+x2+…. Where y is the outcome variable and Xs are the independent variables.
`random`	a string variable specifying the “clustering variable” as contained in the data. See example below.
`intervention`	a string variable specifying the “intervention variable” as appearing in the formula and the data. See example below.
`baseln`	A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.
`adaptD`	As this function uses rstanarm, this term provides the target average proposal acceptance probability during Stan’s adaptation period. Default is NULL.
`nsim`	number of MCMC iterations per chain. Default is 2000.
`data`	data frame containing the data to be analysed.
`threshold`	a scalar or vector of pre-specified threshold(s) for estimating Bayesian posterior probability such that the observed effect size is greater than or equal to the threshold(s).
`...`	additional arguments of `stan_lmer` to be passed to the function.

Value

S3 object; a list consisting of

Beta : Estimates and credible intervals for variables specified in the model. Use summary.eefAnalytics to get Rhat and effective sample size for each estimate.
ES : Conditional Hedges’ g effect size and its 95 % credible intervals.
covParm : A vector of variance decomposition into between cluster variance (Schools) and within cluster variance (Pupils). It also contains intra-cluster correlation (ICC).
SchEffects : A vector of the estimated deviation of each school from the intercept.
ProbES : A matrix of Bayesian Posterior Probabilities such that the observed effect size is greater than or equal to a pre-specified threshold(s).
Model : A stan_glm object used in ES computation, this object can be used for convergence diagnostic.
Unconditional : A list of unconditional effect sizes, covParm and ProbES obtained based on between and within cluster variances from the unconditional model (model with only the intercept as a fixed effect).

Examples

 
 
 data(crtData)
 
 ########################################################
 ## Bayesian analysis of cluster randomised trials     ##
 ########################################################
 
 output <- crtBayes(Posttest~ Intervention+Prettest,random="School",
 intervention="Intervention",nsim=2000,data=crtData)
 
 ### Fixed effects
 beta <- output$Beta
 beta
 
 ### Effect size
 ES1 <- output$ES
 ES1
 
 ## Covariance matrix
 covParm <- output$covParm
 covParm
 
 ### plot random effects for schools
 
 plot(output)
 
 ### plot posterior probability of an effect size to be bigger than a pre-specified threshold
 
 plot(output,group=1)

`crtData`: Cluster Randomised Trial Data.

Description

A cluster randomised trial dataset containing 22 schools. The data contains a random sample of test data of pupils and not actual trial data.

Format

A data frame with 265 rows and 5 variables

Details

Posttest: posttest scores
Prettest: prettest scores
Intervention: the indicator for intervention groups in a two arm trial, coded as 1 for intervention group and 0 for control group.
Intervention2: a simulated indicator for intervention groups in a three arm trial.
School: numeric school identifier

`crtFREQ`: Analysis of Cluster Randomised Education Trials using Multilevel Model under a Frequentist Setting.

Description

crtFREQ performs analysis of cluster randomised education trials using a multilevel model under a frequentist setting.

Usage

crtFREQ(formula, random, intervention, baseln, nPerm, nBoot, seed, data)

Arguments

Argument	Description
`formula`	the model to be analysed is of the form y ~ x1+x2+…. Where y is the outcome variable and Xs are the independent variables.
`random`	a string variable specifying the “clustering variable” as contained in the data. See example below.
`intervention`	a string variable specifying the “intervention variable” as appearing in the formula and the data. See example below.
`baseln`	A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.
`nPerm`	number of permutations required to generate a permutated p-value.
`nBoot`	number of bootstraps required to generate bootstrap confidence intervals.
`seed`	seed required for bootstrapping and permutation procedure, if not provided default seed will be used.
`data`	data frame containing the data to be analysed.

Value

S3 object; a list consisting of

Beta : Estimates and confidence intervals for variables specified in the model.
ES : Conditional Hedges’ g effect size and its 95 % confidence intervals. If nBoot is not specified, 95% confidence intervals are based on standard errors. If nBoot is specified, they are non-parametric bootstrapped confidence intervals.
covParm : A vector of variance decomposition into between cluster variance (Schools) and within cluster variance (Pupils). It also contains intra-cluster correlation (ICC).
SchEffects : A vector of the estimated deviation of each school from the intercept.
Perm : A “nPerm x 2w” matrix containing permutated effect sizes using residual variance and total variance. “w” denotes number of intervention. “w=1” for two arm trial and “w=2” for three arm trial excluding the control group. It is produced only when nPerm is specified.
Bootstrap : A “nBoot x 2w” matrix containing the bootstrapped effect sizes using residual variance (Within) and total variance (Total). “w” denotes number of intervention. “w=1” for two arm trial and “w=2” for three arm trial excluding the control group. It is only produced when nBoot is specified.
Unconditional : A list of unconditional effect sizes, covParm, Perm and Bootstrap obtained based on variances from the unconditional model (model with only the intercept as a fixed effect).

Examples

 
 
 data(crtData)
 
 ########################################################
 ## MLM analysis of cluster randomised trials + 1.96SE ##
 ########################################################
 
 output1 <- crtFREQ(Posttest~ Intervention+Prettest,random="School",
 intervention="Intervention",data=crtData)
 
 
 ### Fixed effects
 beta <- output1$Beta
 beta
 
 ### Effect size
 ES1 <- output1$ES
 ES1
 
 ## Covariance matrix
 covParm <- output1$covParm
 covParm
 
 ### plot random effects for schools
 
 plot(output1)
 
 ###############################################
 ## MLM analysis of cluster randomised trials ##
 ## with bootstrap confidence intervals       ##
 ###############################################
 
 output2 <- crtFREQ(Posttest~ Intervention+Prettest,random="School",
 intervention="Intervention",nBoot=1000,data=crtData)
 
 
 ### Effect size
 
 ES2 <- output2$ES
 ES2
 
 ### plot bootstrapped values
 
 plot(output2, group=1)
 
 #######################################################################
 ## MLM analysis of cluster randomised trials with permutation p-value##
 #######################################################################
 
 output3 <- crtFREQ(Posttest~ Intervention+Prettest,random="School",
 intervention="Intervention",nPerm=1000,data=crtData)
 
 ### Effect size
 
 ES3 <- output3$ES
 ES3
 
 
 ### plot permutated values
 
 plot(output3, group=1)

`eefAnalytics-defunct`: Defunct functions in eefAnalytics

Description

These functions are marked as defunct and have been removed from eefAnalytics.

Usage

mlmbayes(...)
caceMSTBoot(...)
caceCRTBoot(...)
caceSRTBoot(...)

`mstBayes`: Bayesian analysis of Multisite Randomised Education Trials using Vague Priors.

Description

mstBayes performs analysis of multisite randomised education trials using a multilevel model under a Bayesian setting assuming vague priors.

Usage

mstBayes(
  formula,
  random,
  intervention,
  baseln,
  adaptD,
  nsim = 2000,
  data,
  threshold = 1:10/10,
  ...
)

Arguments

Argument	Description
`formula`	the model to be analysed is of the form y ~ x1+x2+…. Where y is the outcome variable and Xs are the independent variables.
`random`	a string variable specifying the “clustering variable” as contained in the data. See example below.
`intervention`	a string variable specifying the “intervention variable” as appearing in the formula and the data. See example below.
`baseln`	A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.
`adaptD`	As this function uses rstanarm, this term provides the target average proposal acceptance probability during Stan’s adaptation period. Default is NULL.
`nsim`	number of MCMC iterations per chain. Default is 2000.
`data`	data frame containing the data to be analysed.
`threshold`	a scalar or vector of pre-specified threshold(s) for estimating Bayesian posterior probability that the observed effect size is greater than or equal to the threshold(s).
`...`	additional arguments of `stan_lmer` to be passed to the function.

Value

S3 object; a list consisting of

Beta : Estimates and credible intervals for variables specified in the model. Use summary.eefAnalytics to get Rhat and effective sample size for each estimate.
ES : Conditional Hedges’ g effect size and its 95 % credible intervals.
covParm : A list of variance decomposition into between cluster variance-covariance matrix (schools and school by intervention) and within cluster variance (Pupils). It also contains intra-cluster correlation (ICC).
SchEffects : A vector of the estimated deviation of each school from the intercept and intervention slope.
ProbES : A matrix of Bayesian posterior probabilities such that the observed effect size is greater than or equal to a pre-specified threshold(s).
Model : A stan_glm object used in ES computation, this object can be used for convergence diagnostic.
Unconditional : A list of unconditional effect sizes, covParm and ProbES obtained based on between and within cluster variances from the unconditional model (model with only the intercept as a fixed effect).

Examples

 
 
 data(mstData)
 
 ########################################################
 ## Bayesian analysis of cluster randomised trials     ##
 ########################################################
 
 output <- mstBayes(Posttest~ Intervention+Prettest,random="School",
 intervention="Intervention",nsim=2000,data=mstData)
 
 ### Fixed effects
 beta <- output$Beta
 beta
 
 ### Effect size
 ES1 <- output$ES
 ES1
 
 ## Covariance matrix
 covParm <- output$covParm
 covParm
 
 ### plot random effects for schools
 
 plot(output)
 
 ### plot posterior probability of an effect size to be bigger than a pre-specified threshold
 
 plot(output,group=1)

`mstData`: Multisite Trial Data.

Description

A multisite trial dataset containing 54 schools. This data contains a random sample of test data of pupils and not actual trial data.

Format

A data frame with 210 rows and 5 variables

Details

Posttest: posttest scores
Prettest: prettest scores
Intervention: the indicator for the intervention groups in a two arm trial, coded as 1 for intervention group and 0 for control group.
Intervention2: a simulated indicator for intervention groups in a three arm trial.
School: numeric school identifier

`mstFREQ`: Analysis of Multisite Randomised Education Trials using Multilevel Model under a Frequentist Setting.

Description

mstFREQ performs analysis of multisite randomised education trials using a multilevel model under a frequentist setting.

Usage

mstFREQ(formula, random, intervention, baseln, nPerm, data, seed, nBoot)

Arguments

Argument	Description
`formula`	the model to be analysed is of the form y ~ x1+x2+…. Where y is the outcome variable and Xs are the independent variables.
`random`	a string variable specifying the “clustering variable” as contained in the data. See example below.
`intervention`	a string variable specifying the “intervention variable” as appearing in the formula and the data. See example below.
`baseln`	A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.
`nPerm`	number of permutations required to generate permutated p-value.
`data`	data frame containing the data to be analysed.
`seed`	seed required for bootstrapping and permutation procedure, if not provided default seed will be used.
`nBoot`	number of bootstraps required to generate bootstrap confidence intervals.

Value

S3 object; a list consisting of

Beta : Estimates and confidence intervals for variables specified in the model.
ES : Conditional Hedge’s g effect size (ES) and its 95 % confidence intervals. If nBoot is not specified, 95% confidence intervals are based on standard errors. If nBoot is specified, they are non-parametric bootstrapped confidence intervals.
covParm : A list of variance decomposition into between cluster variance-covariance matrix (schools and school by intervention) and within cluster variance (Pupils). It also contains intra-cluster correlation (ICC).
SchEffects : A vector of the estimated deviation of each school from the intercept and intervention slope.
Perm : A “nPerm x 2w” matrix containing permutated effect sizes using residual variance and total variance. “w” denotes number of intervention. “w=1” for two arm trial and “w=2” for three arm trial excluding the control group. It is produced only when nPerm is specified.
Bootstrap : A “nBoot x 2w” matrix containing the bootstrapped effect sizes using residual variance (Within) and total variance (Total). “w” denotes number of intervention. “w=1” for two arm trial and “w=2” for three arm trial excluding the control group. It is only prduced when nBoot is specified.
Unconditional : A list of unconditional effect sizes, covParm, Perm and Bootstrap obtained based on variances from the unconditional model (model with only the intercept as a fixed effect).

Examples

 
 
 data(mstData)
 
 ########################################################
 ## MLM analysis of multisite trials + 1.96SE ##
 ########################################################
 
 output1 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
 intervention="Intervention",data=mstData)
 
 
 ### Fixed effects
 beta <- output1$Beta
 beta
 
 ### Effect size
 ES1 <- output1$ES
 ES1
 
 ## Covariance matrix
 covParm <- output1$covParm
 covParm
 
 ### plot random effects for schools
 
 plot(output1)
 
 ###############################################
 ## MLM analysis of multisite trials          ##
 ## with bootstrap confidence intervals       ##
 ###############################################
 
 output2 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
 intervention="Intervention",nBoot=1000,data=mstData)
 
 tp <- output2$Bootstrap
 ### Effect size
 
 ES2 <- output2$ES
 ES2
 
 ### plot bootstrapped values
 
 plot(output2, group=1)
 
 #######################################################################
 ## MLM analysis of mutltisite trials with permutation p-value##
 #######################################################################
 
 output3 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
 intervention="Intervention",nPerm=1000,data=mstData)
 
 ES3 <- output3$ES
 ES3
 
 #### plot permutated values
 
 plot(output3, group=1)

`plot.eefAnalytics`: A plot method for an eefAnalytics S3 object obtained from the eefAnalytics package.

Description

Plots different figures based on output from eefAnalytics package.

Usage

list(list("plot"), list("eefAnalytics"))(x, group, Conditional = TRUE, ES_Total = TRUE, slope = FALSE, ...)

Arguments

Argument	Description
`x`	an output object from the eefAnalytics package.
`group`	a string/scalar value indicating which intervention to plot. This must be one of the values of intervention variable excluding the control group. For a two arm trial, the maximum number of values to consider is 1 and 2 for three arm trial.
`Conditional`	a logical value to indicate whether to plot the conditional effect size. The default is Conditional=TRUE, otherwise Conditional=FALSE should be specified for plot based on the unconditional effect size. Conditional variance is total or residual variance from a multilevel model with fixed effects, whilst unconditional variance is total variance or residual variance from a multilevel model with only intercept as fixed effect.
`ES_Total`	A logical value indicating whether to plot the effect size based on total variance or within school variance. The default is ES_Total=TRUE, to plot the effect size using total variance. ES_Total=FALSE should be specified for the effect size based on within school or residuals variance.
`slope`	A logical value indicating whether to return the plot of random intercept (default is slope=FALSE). return other school-by-intervention interaction random slope (s) is slope=TRUE. This argument is suitable only for mstBayes and mstFREQ functions.
`...`	arguments passed to `plot.default`

Details

Plot produces a graphical visualisation depending on which model is fitted:

For srtFREQ() , plot can only be used when nBoot or nPerm is specified to visualise the distribution of bootstrapped or permutated values.
For crtFREQ() or mstFREQ() , plot shows the distribution of random intercepts when group=NULL . It produces histogram of permutated or bootstrapped values when group is specified and either nBoot or nPerm is also specified.

Value

Returns relevant plots for each model.

Examples

 
 
 #### read data
 data(mstData)
 data(crtData)
 
 
 ###############
 ##### SRT #####
 ###############
 
 ##### Bootstrapped
 
 outputSRTBoot <- srtFREQ(Posttest~ Intervention + Prettest,
 intervention = "Intervention",nBoot=1000, data = mstData)
 plot(outputSRTBoot,group=1)
 
 ##### Permutation
 outputSRTPerm <- srtFREQ(Posttest~ Intervention + Prettest,
 intervention = "Intervention",nPerm=1000, data = mstData)
 
 plot(outputSRTPerm,group=1)
 
 
 ###############
 ##### MST #####
 ###############
 
 
 #### Random intercepts
 outputMST <- mstFREQ(Posttest~ Intervention + Prettest,
 random = "School", intervention = "Intervention", data = mstData)
 plot(outputMST)
 
 
 #### Bootstrapped
 outputMSTBoot <- mstFREQ(Posttest~ Intervention + Prettest,
 random = "School", intervention = "Intervention",
 nBoot = 1000, data = mstData)
 
 plot(outputMSTBoot)
 plot(outputMSTBoot,group=1)
 
 #### Permutation
 outputMSTPerm <- mstFREQ(Posttest~ Intervention + Prettest,
 random = "School", intervention = "Intervention",
 nPerm = 1000, data = mstData)
 plot(outputMSTPerm)
 plot(outputMSTPerm,group=1)
 
 
 
 ###############
 ##### CRT #####
 ###############
 
 #### Random intercepts
 outputCRT <- crtFREQ(Posttest~ Intervention + Prettest, random = "School",
 intervention = "Intervention", data = crtData)
 plot(outputCRT)
 
 
 ## Bootstrapped
 outputCRTBoot <- crtFREQ(Posttest~ Intervention + Prettest, random = "School",
 intervention = "Intervention", nBoot = 1000, data = crtData)
 
 plot(outputCRTBoot,group=1)
 
 
 ##Permutation
 outputCRTPerm <- crtFREQ(Posttest~ Intervention + Prettest, random = "School",
 intervention = "Intervention", nPerm = 1000, data = crtData)
 
 plot(outputCRTPerm,group=1)

`print.eefAnalytics`: Print for a fitted model represented by an `eefAnalytics` object.

Description

Print for a fitted model represented by an eefAnalytics object.

Usage

list(list("print"), list("eefAnalytics"))(x, ...)

Arguments

Argument	Description
`x`	Object of class `eefAnalytics`
`...`	Additional arguments of `print`

Value

Print conditional and unconditional effect sizes.

`srtBayes`: Analysis of Simple Randomised Education Trials using Bayesian Linear Regression Model with Vague Priors.

Description

srtBayes performs analysis of educational trials under the assumption of independent errors among pupils using Bayesian framework with Stan. This can also be used with schools as fixed effects.

Usage

srtBayes(
  formula,
  intervention,
  baseln,
  adaptD,
  nsim = 2000,
  data,
  threshold = 1:10/10,
  ...
)

Arguments

Argument	Description
`formula`	The model to be analysed is of the form y~x1+x2+…. Where y is the outcome variable and Xs are the independent variables.
`intervention`	A string variable specifying the “intervention variable” as appearing in the formula and the data. See example below.
`baseln`	A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.
`adaptD`	As this function uses rstanarm, this term provides the target average proposal acceptance probability during Stan’s adaptation period. Default is NULL.
`nsim`	A number of MCMC iterations per chain. Default is 2000.
`data`	Data frame containing the data to be analysed.
`threshold`	a scalar or vector of pre-specified threshold(s) for estimating Bayesian posterior probability such that the observed effect size is greater than or equal to the threshold(s).
`...`	Additional arguments of `stan_lmer` to be passed to the function.

Value

S3 object; a list consisting of

Beta : Estimates and credible intervals for the variables specified in the model. Use summary.eefAnalytics to get Rhat and effective sample size for each estimate.
ES : Conditional Hedges’ g effect size and its 95 % credible intervals.
sigma2 : Residual variance.
ProbES : A matrix of Bayesian posterior probabilities such that the observed effect size is greater than or equal to a pre-specified threshold(s).
Model : A stan_glm object used in ES computation, this object can be used for convergence diagnostic.
Unconditional : A list of unconditional effect sizes, sigma2 and ProbES obtained based on residual variance from the unconditional model (model with only the intercept as a fixed effect).

Examples

 
 
 data(mstData)
 
 ########################################################
 ## Bayesian analysis of cluster randomised trials     ##
 ########################################################
 
 output <- srtBayes(Posttest~ Intervention+Prettest,
 intervention="Intervention",nsim=2000,data=mstData)
 
 ### Fixed effects
 beta <- output$Beta
 beta
 
 ### Effect size
 ES1 <- output$ES
 ES1
 
 ## Covariance matrix
 covParm <- output$covParm
 covParm
 
 ### plot random effects for schools
 
 plot(output)
 
 ### plot posterior probability of an effect size to be bigger than a pre-specified threshold
 
 plot(output,group=1)

`srtFREQ`: Analysis of Simple Randomised Education Trial using Linear Regression Model.

Description

srtFREQ performs analysis of educational trials under the assumption of independent errors among pupils. This can also be used with schools as fixed effects.

Usage

srtFREQ(formula, intervention, baseln, nBoot, nPerm, seed, data)

Arguments

Argument	Description
`formula`	the model to be analysed is of the form y~x1+x2+…. Where y is the outcome variable and Xs are the independent variables.
`intervention`	a string variable specifying the “intervention variable” as appearing in the formula and the data. See example below.
`baseln`	A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.
`nBoot`	number of bootstraps required to generate bootstrap confidence intervals.
`nPerm`	number of permutations required to generate permutated p-value.
`seed`	seed required for bootstrapping and permutation procedure, if not provided default seed will be used.
`data`	data frame containing the data to be analysed.

Value

S3 object; a list consisting of

Beta : Estimates and confidence intervals for the variables specified in the model.
ES : Conditional Hedges’g effect size and its 95 % confidence intervals. If nBoot is not specified, 95% confidence intervals are based on standard errors. If nBoot is specified, they are non-parametric bootstrapped confidence intervals.
sigma2 : Residual variance.
Perm : A “nPerm x w” matrix containing permutated effect sizes using residual variance. “w” denotes number of intervention. “w=1” for two arm trial and “w=2” for three arm trial excluding the control group. It is produced only if nPerm is specified.
Bootstrap : A “nBoot x w” matrix containing the bootstrapped effect sizes using residual variance. “w” denotes number of intervention. “w=1” for two arm trial and “w=2” for three arm trial excluding the control group. It is produced only if nBoot is specified.
Unconditional : A list of unconditional effect size, sigma2, Perm and Bootstrap obtained based on variances from the unconditional model (model with only intercept as fixed effect).

Examples

 
 
 data(mstData)
 
 ###################################################################
 ## Analysis of simple randomised trials using Hedges Effect Size ##
 ###################################################################
 
 output1 <- srtFREQ(Posttest~ Intervention+Prettest,
 intervention="Intervention",data=mstData )
 ES1 <- output1$ES
 ES1
 
 ###################################################################
 ## Analysis of simple randomised trials using Hedges Effect Size ##
 ## with Permutation p-value                                      ##
 ###################################################################
 
 output2 <- srtFREQ(Posttest~ Intervention+Prettest,
 intervention="Intervention",nPerm=1000,data=mstData )
 
 ES2 <- output2$ES
 ES2
 
 
 #### plot permutated values
 
 plot(output2, group=1)
 
 
 
 ###################################################################
 ## Analysis of simple randomised trials using Hedges Effect Size ##
 ## with non-parametric bootstrap confidence intervals            ##
 ###################################################################
 
 output3 <- srtFREQ(Posttest~ Intervention+Prettest,
 intervention="Intervention",nBoot=1000,data=mstData)
 
 ES3 <- output3$ES
 ES3
 
 ### plot bootstrapped values
 
 plot(output3, group=1)
 
 ####################################################################
 ## Analysis of simple randomised trials using Hedges' effect size  ##
 ##  with schools as fixed effects                                  ##
 ####################################################################
 
 output4 <- srtFREQ(Posttest~ Intervention+Prettest+as.factor(School),
 intervention="Intervention",data=mstData )
 
 ES4 <- output4$ES
 ES4
 
 ####################################################################
 ## Analysis of simple randomised trials using Hedges' effect size ##
 ## with schools as fixed effects and with permutation p-value     ##
 ####################################################################
 
 output5 <- srtFREQ(Posttest~ Intervention+Prettest+as.factor(School),
 intervention="Intervention",nPerm=1000,data=mstData )
 
 ES5 <- output5$ES
 ES5
 
 #### plot permutated values
 
 plot(output5, group=1)
 
 ####################################################################
 ## Analysis of simple randomised trials using Hedges' effect size ##
 ## with schools as fixed effects and with permutation p-value      ##
 ####################################################################
 
 output6 <- srtFREQ(Posttest~ Intervention+Prettest+as.factor(School),
 intervention="Intervention",nBoot=1000,data=mstData)
 
 ES6 <- output6$ES
 ES6
 
 ### plot bootstrapped values
 
 plot(output6, group=1)

`summary.eefAnalytics`: Summary for a fitted model represented by an `eefAnalytics` object.

Description

Summary for a fitted model represented by an eefAnalytics object.

Usage

list(list("summary"), list("eefAnalytics"))(object, ...)

Arguments

Argument	Description
`object`	Object of class `eefAnalytics`
`...`	Additional arguments of `summary`

Value

Returns relevant summary including Rhat and effective sample sizes.

eefAnalytics

DESCRIPTION

ComparePlot: A plot function to compare different eefAnalytics S3 objects from the eefAnalytics package.

Description

Usage

Arguments

Details

Value

Examples

crtBayes: Bayesian analysis of cluster randomised education trials using Vague Priors.

Description

Usage

Arguments

Value

Examples

crtData: Cluster Randomised Trial Data.

Description

Format

Details

crtFREQ: Analysis of Cluster Randomised Education Trials using Multilevel Model under a Frequentist Setting.

Description

Usage

Arguments

Value

Examples

eefAnalytics-defunct: Defunct functions in eefAnalytics

Description

Usage

mstBayes: Bayesian analysis of Multisite Randomised Education Trials using Vague Priors.

Description

Usage

Arguments

Value

Examples

mstData: Multisite Trial Data.

Description

Format

Details

mstFREQ: Analysis of Multisite Randomised Education Trials using Multilevel Model under a Frequentist Setting.

Description

Usage

Arguments

Value

Examples

plot.eefAnalytics: A plot method for an eefAnalytics S3 object obtained from the eefAnalytics package.

Description

Usage

Arguments

Details

Value

Examples

print.eefAnalytics: Print for a fitted model represented by an eefAnalytics object.

Description

Usage

Arguments

Value

srtBayes: Analysis of Simple Randomised Education Trials using Bayesian Linear Regression Model with Vague Priors.

Description

Usage

Arguments

Value

Examples

srtFREQ: Analysis of Simple Randomised Education Trial using Linear Regression Model.

Description

Usage

Arguments

Value

Examples

summary.eefAnalytics: Summary for a fitted model represented by an eefAnalytics object.

Description

Usage

Arguments

Value

`ComparePlot`: A plot function to compare different eefAnalytics S3 objects from the eefAnalytics package.

`crtBayes`: Bayesian analysis of cluster randomised education trials using Vague Priors.

`crtData`: Cluster Randomised Trial Data.

`crtFREQ`: Analysis of Cluster Randomised Education Trials using Multilevel Model under a Frequentist Setting.

`eefAnalytics-defunct`: Defunct functions in eefAnalytics

`mstBayes`: Bayesian analysis of Multisite Randomised Education Trials using Vague Priors.

`mstData`: Multisite Trial Data.

`mstFREQ`: Analysis of Multisite Randomised Education Trials using Multilevel Model under a Frequentist Setting.

`plot.eefAnalytics`: A plot method for an eefAnalytics S3 object obtained from the eefAnalytics package.

`print.eefAnalytics`: Print for a fitted model represented by an `eefAnalytics` object.

`srtBayes`: Analysis of Simple Randomised Education Trials using Bayesian Linear Regression Model with Vague Priors.

`srtFREQ`: Analysis of Simple Randomised Education Trial using Linear Regression Model.

`summary.eefAnalytics`: Summary for a fitted model represented by an `eefAnalytics` object.