Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
Description
Find target sample sizes (the number of clusters, cluster size, or both) for the accuracy in unstandardized conditions means estimation in CRD. If users wish to seek for both types of sample sizes simultaneously, an additional constraint is required, such as a desired width or a desired budget.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ss.aipe.crd.nclus.fixedwidth(width, nindiv, prtreat, tauy=NULL, sigma2y=NULL,
totalvar=NULL, iccy=NULL, r2between = 0, r2within = 0, numpredictor = 0,
assurance=NULL, conf.level = 0.95, cluscost=NULL, indivcost=NULL, diffsize=NULL)
ss.aipe.crd.nindiv.fixedwidth(width, nclus, prtreat, tauy=NULL, sigma2y=NULL,
totalvar=NULL, iccy=NULL, r2between = 0, r2within = 0, numpredictor = 0,
assurance=NULL, conf.level = 0.95, cluscost=NULL, indivcost=NULL, diffsize=NULL)
ss.aipe.crd.nclus.fixedbudget(budget, nindiv, cluscost = 0, indivcost = 1,
prtreat = NULL, tauy=NULL, sigma2y=NULL, totalvar=NULL, iccy=NULL, r2between = 0,
r2within = 0, numpredictor = 0, assurance=NULL, conf.level = 0.95, diffsize=NULL)
ss.aipe.crd.nindiv.fixedbudget(budget, nclus, cluscost = 0, indivcost = 1,
prtreat = NULL, tauy=NULL, sigma2y=NULL, totalvar=NULL, iccy=NULL, r2between = 0,
r2within = 0, numpredictor = 0, assurance=NULL, conf.level = 0.95, diffsize=NULL)
ss.aipe.crd.both.fixedbudget(budget, cluscost=0, indivcost=1, prtreat, tauy=NULL,
sigma2y=NULL, totalvar=NULL, iccy=NULL, r2between = 0, r2within = 0,
numpredictor = 0, assurance=NULL, conf.level = 0.95, diffsize=NULL)
ss.aipe.crd.both.fixedwidth(width, cluscost=0, indivcost=1, prtreat, tauy=NULL,
sigma2y=NULL, totalvar=NULL, iccy=NULL, r2between = 0, r2within = 0,
numpredictor = 0, assurance=NULL, conf.level = 0.95, diffsize=NULL)

Arguments
width 
The desired width of the confidence interval of the unstandardized means difference 
budget 
The desired amount of budget 
nclus 
The desired number of clusters 
nindiv 
The number of individuals in each cluster (cluster size) 
prtreat 
The proportion of treatment clusters 
cluscost 
The cost of collecting a new cluster regardless of the number of individuals collected in each cluster 
indivcost 
The cost of collecting a new individual 
tauy 
The residual variance in the between level before accounting for the covariate 
sigma2y 
The residual variance in the within level before accounting for the covariate 
totalvar 
The total resiudal variance before accounting for the covariate 
iccy 
The intraclass correlation of the dependent variable 
r2within 
The proportion of variance explained in the within level (used when 
r2between 
The proportion of variance explained in the between level (used when 
numpredictor 
The number of predictors used in the between level 
assurance 
The degree of assurance, which is the value with which confidence can be placed that describes the likelihood of obtaining a confidence interval less than the value specified (e.g, .80, .90, .95) 
conf.level 
The desired level of confidence for the confidence interval 
diffsize 
Difference cluster size specification. The difference in cluster sizes can be specified in two ways. First, users may specify cluster size as integers, which can be negative or positive. The resulting cluster sizes will be based on the estimated cluster size adding by the specified vectors. For example, if the cluster size is 25, the number of clusters is 10, and the specified different cluster size is 
Details
Here are the functions' descriptions:

ss.aipe.crd.nclus.fixedwidth
Find the number of clusters given a specified width of the confidence interval and the cluster size 
ss.aipe.crd.nindiv.fixedwidth
Find the cluster size given a specified width of the confidence interval and the number of clusters 
ss.aipe.crd.nclus.fixedbudget
Find the number of clusters given a budget and the cluster size 
ss.aipe.crd.nindiv.fixedbudget
Find the cluster size given a budget and the number of clusters 
ss.aipe.crd.both.fixedbudget
Find the sample size combinations (the number of clusters and that cluster size) providing the narrowest confidence interval given the fixed budget 
ss.aipe.crd.both.fixedwidth
Find the sample size combinations (the number of clusters and that cluster size) providing the lowest cost given the specified width of the confidence interval
Value
The ss.aipe.crd.nclus.fixedwidth
and ss.aipe.crd.nclus.fixedbudget
functions provide the number of clusters. The ss.aipe.crd.nindiv.fixedwidth
and ss.aipe.crd.nindiv.fixedbudget
functions provide the cluster size. The ss.aipe.crd.both.fixedbudget
and ss.aipe.crd.both.fixedwidth
provide the number of clusters and the cluster size, respectively.
Author(s)
Sunthud Pornprasertmanit (psunthud@gmail.com)
References
Pornprasertmanic, S., & Schneider, W. J. (2014). Accuracy in parameter estimation in cluster randomized designs. Psychological Methods, 19, 356–379.
Examples
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  ## Not run:
# Examples for each function
ss.aipe.crd.nclus.fixedwidth(width=0.3, nindiv=30, prtreat=0.5, tauy=0.25, sigma2y=0.75)
ss.aipe.crd.nindiv.fixedwidth(width=0.3, nclus=250, prtreat=0.5, tauy=0.25, sigma2y=0.75)
ss.aipe.crd.nclus.fixedbudget(budget=10000, nindiv=20, cluscost=20, indivcost=1)
ss.aipe.crd.nindiv.fixedbudget(budget=10000, nclus=30, cluscost=20, indivcost=1,
prtreat=0.5, tauy=0.05, sigma2y=0.95, assurance=0.8)
ss.aipe.crd.both.fixedbudget(budget=10000, cluscost=30, indivcost=1, prtreat=0.5, tauy=0.25,
sigma2y=0.75)
ss.aipe.crd.both.fixedwidth(width=0.3, cluscost=0, indivcost=1, prtreat=0.5, tauy=0.25,
sigma2y=0.75)
# Examples for different cluster size
ss.aipe.crd.nclus.fixedwidth(width=0.3, nindiv=30, prtreat=0.5, tauy=0.25, sigma2y=0.75,
diffsize = c(2, 1, 0, 2, 1, 3, 3, 0, 0))
ss.aipe.crd.nclus.fixedwidth(width=0.3, nindiv=30, prtreat=0.5, tauy=0.25, sigma2y=0.75,
diffsize = c(0.6, 1.2, 0.8, 1.4, 1, 1, 1.1, 0.9))
## End(Not run)
