Description Usage Arguments Value Examples
View source: R/eefAnalytics_03_2017.r
caceMSTBoot
performs exploratory CACE analysis of multisite randomised education trials.
1 | caceMSTBoot(formula, random, intervention, compliance, nBoot, 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 |
compliance |
a string variable specifying the "compliance variable" as contained in the data. The data must be in percentages ranging from 0 - 100. |
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
CACE
. Estimates of CACE adjusted effect sizes based on pre-specified thresholds. Only produced for threshold with at least 50
Compliers
. Percentage of pupils that achieved a pre-specified threshold of compliance.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | 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)
}
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