caceMSTBoot: CACE Analysis of Multisite Randomised Trials.

Description Usage Arguments Value Examples

View source: R/eefAnalyticPerm_modified_05_02_2016.R

Description

caceMSTBoot performs CACE analysis of multisite randomised trials.

Usage

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caceMSTBoot(formula, random, intervention, compliance, nBoot, data)

Arguments

formula

model specification of the form posttest ~ pretests+Intervention+.... 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.

random

a string variable specifying the "clustering" variable as contained in the data. This must be put between quotes. For example, "school".

intervention

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

compliance

percentages of sessions attended by pupils.

nBoot

number of bootstrap required to generate bootstrap confidence interval. This must be specified.

data

data frame containing the data to be analysed.

Value

S3 object; a list consisting of

Examples

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data(catcht)

########################################################
## MLM analysis of multisite trials + 1.96SE ##
########################################################

output1 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
		intervention="Intervention",data=catcht)


### Fixed effects
beta <- output1$Beta
beta

### Effect size
ES1 <- output1$ES$Intervention1
ES1

## Covariance matrix
covParm <- output1$covParm
covParm

### random effects for schools

randOut <- output1$"SchEffects"
randOut <- randOut[order(randOut$Estimate),]
barplot(randOut$Estimate,ylab="Deviations from Overall Average",
		names.arg=randOut$Schools,las=2)

###############################################
## MLM analysis of multisite trials          ##	 
## with bootstrap confidence intervals       ##
###############################################

output2 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
		intervention="Intervention",nBoot=1000,data=catcht)


### Effect size

ES2 <- output2$ES
ES2


#######################################################################
## MLM analysis of mutltisite trials with permutation p-value##
#######################################################################

output3 <- mstFREQ(Posttest~ Intervention+Prettest,random="School",
		intervention="Intervention",nPerm=1000,data=catcht)


#### Distribution under the null

perm <- output3$Perm
str(perm )

#### Permutation P-value using total variance


obsg <- output3$ES$Intervention1[2,1]
obsg


p_value <- ifelse(mean(perm$"Intervention1Total" > obsg)==0,"<0.001",
		mean(perm$"Intervention1Total" > obsg) )
p_value

hist(perm$"Intervention1Total", breaks=40, col="white", 
		border="blueviolet",
		xlab="Distribution Under Null Hypothesis", 
		main=paste("P(X|NULL)= ",p_value,sep=""), 
		xlim=range(c(perm$"Intervention1Total",obsg),
		na.rm=TRUE))
abline(v=obsg,lwd=2,col=4)

eefMLM documentation built on May 31, 2017, 3:50 a.m.

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