mstFREQ | R Documentation |
mstFREQ
performs analysis of multisite randomised education trials using a multilevel model under a frequentist setting.
mstFREQ( formula, random, intervention, baseln, nPerm, data, type, ci, seed, nBoot )
formula |
the model to be analysed is of the form y ~ x1+x2+.... Where y is the outcome variable and Xs are the independent variables. |
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 appearing in the formula and the data. See example below. |
baseln |
A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference. |
nPerm |
number of permutations required to generate permutated p-value. |
data |
data frame containing the data to be analysed. |
type |
method of bootstrapping including case re-sampling at student level "case(1)", case re-sampling at school level "case(2)", case re-sampling at both levels "case(1,2)" and residual bootstrapping using "residual". If not provided, default will be case re-sampling at student level. |
ci |
method for bootstrap confidence interval calculations; options are the Basic (Hall's) confidence interval "basic" or the simple percentile confidence interval "percentile". If not provided default will be percentile. |
seed |
seed required for bootstrapping and permutation procedure, if not provided default seed will be used. |
nBoot |
number of bootstraps required to generate bootstrap confidence intervals. |
S3 object; a list consisting of
Beta
: Estimates and confidence intervals for variables specified in the model.
ES
: Conditional Hedge's g effect size (ES) and its 95% confidence intervals. If nBoot is not specified, 95% confidence intervals are based on standard errors. If nBoot is specified, they are non-parametric bootstrapped confidence intervals.
covParm
: A list of variance decomposition into between cluster variance-covariance matrix (schools and school by intervention) and within cluster variance (Pupils). It also contains intra-cluster correlation (ICC).
SchEffects
: A vector of the estimated deviation of each school from the intercept and intervention slope.
Perm
: A "nPerm x 2w" 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 "nBoot x 2w" matrix containing the bootstrapped effect sizes using residual variance (Within) and total variance (Total). "w" denotes number of intervention. "w=1" for two arm trial and "w=2" for three arm trial excluding the control group. It is only produced when nBoot
is specified.
Unconditional
: A list of unconditional effect sizes, covParm, Perm and Bootstrap obtained based on variances from the unconditional model (model with only the intercept as a fixed effect).
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$covParm covParm ### plot random effects for schools plot(output1) ################################################## ## MLM analysis of multisite trials ## ## with residual bootstrap confidence intervals ## ################################################## output2 <- mstFREQ(Posttest~ Intervention+Prettest,random="School", intervention="Intervention",nBoot=1000,type="residual",data=mstData) tp <- output2$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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