eefAnalytics (version 1.0.6)

mstFREQ: Analysis of Multisite Randomised Education Trials using Multilevel Model.

Description

mstFREQ performs analysis of multisite randomised education trial using multilevel model within the frequentist framework.

Usage

mstFREQ(formula, random, intervention, nPerm = NULL, data, nBoot = NULL)

Arguments

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.

data

data frame containing the data to be analysed.

nBoot

number of bootstraps required to generate bootstrap confidence interval. Default is NULL.

Value

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), clustering by intervention interacttion (Intervention:School) 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.

Examples

if(interactive()){

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)
}