srtFREQ: Analysis of Simple Randomised Education Trial using Linear...
In ewouddt/eefAnalytics: Analysing Education Trials

Description Usage Arguments Value Examples

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

1	srtFREQ(formula, intervention, nBoot = NULL, nPerm = 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.
`intervention`	a string variable specifying the "intervention variable" as appeared in the formula. See example below
`nBoot`	number of bootstraps required to generate bootstrap confidence interval. Default is NULL.
`nPerm`	number of permutations required to generate permutated p-value. Default is NULL.
`data`	data frame containing the data to be analysed.

S3 object; a list consisting of

Beta. Estimates and confidence intervals for the predictors specified in the model.
ES. Hedges' g effect size for the intervention(s). If nBoot is not specified, the confidence intervals are 95
sigma2. Residual variance.
Perm. A vector containing permutated effect sizes under null hypothesis. It is produced only if nPerm is specified.
Bootstrap. A vector containing bootstrapped effect sizes. It is prduced only if nBoot is specified.

if(interactive()){

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)
}