crtFREQ {eefAnalytics} | R Documentation |
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,
type,
ci,
seed,
data
)
Arguments
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. |
type |
method of bootstrapping including case re-sampling at student level "case(1)", case re-sampling at school level "case(2)", case re-sampling at both levels "case(1,2)" and residual bootstrapping using "residual". If not provided, default will be case re-sampling at student level. |
ci |
method for bootstrap confidence interval calculations; options are the Basic (Hall's) confidence interval "basic" or the simple percentile confidence interval "percentile". If not provided default will be percentile. |
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 whennPerm
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 whennBoot
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
if(interactive()){
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$covParm
covParm
### plot random effects for schools
plot(output1)
##################################################
## MLM analysis of cluster randomised trials ##
## with residual bootstrap confidence intervals ##
##################################################
output2 <- crtFREQ(Posttest~ Intervention+Prettest,random="School",
intervention="Intervention",nBoot=1000,type="residual",data=crtData)
### Effect size
ES2 <- output2$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)
}