crtFREQ: Analysis of Cluster Randomised Education Trials using...
In ewouddt/eefAnalytics: Analysing Education Trials

Description Usage Arguments Value Examples

crtFREQ performs Analysis of cluster randomised education trial using multilevel model under the frequentist framework.

1	crtFREQ(formula, random, intervention, nPerm = NULL, nBoot = NULL, data)

`formula`	the model to be analysed. It is of the form y ~ x1+x2+.... Where y is the outcome variable and Xs are the predictors.
`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 appeared in the formula. See example below
`nPerm`	number of permutations required to generate permutated p-value. Default is NULL.
`nBoot`	number of bootstraps required to generate bootstrap confidence interval. Default is NULL.
`data`	data frame containing the data to be analysed.

S3 object; a list consisting of

Beta. Estimates and confidence intervals for preditors specified in the model.
ES. Hedges' g effect size for the intervention(s). If nBoot is not specified, the confidence intervals are 95
covParm. Vector of variance decomposition into between cluster variance (Schools) and within cluster variance (Pupils). It also contains the intral-cluster correlation (ICC).
SchEffects. Random intercepts for clusters, e.g schools.
Perm. A "nPerm x w" 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 "w x nBoot" matrix containing the bootstrapped effect sizes using residual variance (Within) and total variance (Total). "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.

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 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)
}