srtFREQ: Analysis of Simple Randomised Trial (SRT).

Description Usage Arguments Value Examples

View source: R/eefAnalyticPerm_modified_05_02_2016.R

Description

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

Usage

1
srtFREQ(formula, intervention, nBoot = NULL, nPerm = NULL, data)

Arguments

formula

specifies the model to be analysed. It is of the form y~x1+x2+..., where y is the outcome variable and X's are the predictors.

intervention

the name of the intervention variable as appeared in formula. This must be put in quotes. For example "intervention" or "treatment" or "group".

nBoot

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

nPerm

number of permutations required to generate permutation p-value. Default is NULL.

data

data frame containing the data to be analysed.

Value

S3 mcpi object; a list consisting of

Examples

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data(catcht)

###################################################################
## Analysis of simple randomised trials using Hedges Effect Size ##
###################################################################

output1 <- srtFREQ(Posttest~ Intervention+Prettest,
		intervention="Intervention",data=catcht )
ES1 <- output1$ES

###################################################################
## Analysis of simple randomised trials using Hedges Effect Size ## 
## with Permutation p-value                                      ##
###################################################################

output2 <- srtFREQ(Posttest~ Intervention+Prettest,
		intervention="Intervention",nPerm=1000,data=catcht )
ES2 <- output2$ES




#### Distribution under the null

perm <- output2$Perm


#### Permutation P-value using total variance


obsg <- output2$ES[1]


permPvalue <- ifelse(mean(perm$permES> obsg[1])==0,"<0.001",
		mean(perm$permES > obsg[1]) )
permPvalue 

## Distribution of ES under H0
hist(perm$permES, breaks=40, col="white", border="blueviolet", 
		xlab="Distribution Under Null Hypothesis", 
		main=paste("P(X|NULL)= ",permPvalue,sep=""),
		xlim=range(c(perm[,1],obsg),na.rm=TRUE));
abline(v=obsg[1],lwd=2,col=4)



###################################################################
## Analysis of simple randomised trials using Hedges Effect Size ##
## with non-parametric bootstrap CI                              ##
###################################################################

output3 <- srtFREQ(Posttest~ Intervention+Prettest,
		intervention="Intervention",nBoot=1000,data=catcht)
ES3 <- output3$ES


####################################################################
## Analysis of simple randomised trials using Hedges Effect Size. ##
## Schools as fixed effects                                       ##
####################################################################

output4 <- srtFREQ(Posttest~ Intervention+Prettest+as.factor(School),
		intervention="Intervention",data=catcht )
ES4 <- output4$ES

###################################################################
## Analysis of simple randomised trials using Hedges Effect Size ##
## with Permutation p-value. Schools as fixed effects            ##
###################################################################

output5 <- srtFREQ(Posttest~ Intervention+Prettest,
		intervention="Intervention",nPerm=1000,data=catcht )

ES5 <- output5$ES
ES5

####################################################################
## Analysis of simple randomised trials using Hedges Effect Size  ##
## with non-parametric bootstrap CI. Schools as fixed effects     ##
####################################################################

output6 <- srtFREQ(Posttest~ Intervention+Prettest,
		intervention="Intervention",nBoot=1000,data=catcht)
ES6 <- output6$ES
ES6 

eefMLM documentation built on May 21, 2017, 1:37 a.m.

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