ssAIPECRDES: Find target sample sizes for the accuracy in standardized...

In MBESS: The MBESS R Package

Find target sample sizes for the accuracy in standardized conditions means estimation in CRD

Description

Find target sample sizes (the number of clusters, cluster size, or both) for the accuracy in standardized 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. This function uses the likelihood-based confidence interval (Cheung, 2009) by the OpenMx package (Boker et al., 2011). See further details at Pornprasertmanit and Schneider (2010, submitted).

Usage

ss.aipe.crd.es.nclus.fixedwidth(width, nindiv, es, estype = 1, iccy, prtreat, 
	r2between = 0, r2within = 0, numpredictor = 0, assurance=NULL, 
	conf.level = 0.95, nrep = 1000, iccz = NULL, seed = 123321, multicore = FALSE, 
	numProc=NULL, cluscost=NULL, indivcost=NULL, diffsize=NULL)
ss.aipe.crd.es.nindiv.fixedwidth(width, nclus, es, estype = 1, iccy, prtreat, 
	r2between = 0, r2within = 0, numpredictor = 0, assurance=NULL, 
	conf.level = 0.95, nrep = 1000, iccz = NULL, seed = 123321, multicore = FALSE, 
	numProc=NULL, cluscost=NULL, indivcost=NULL, diffsize=NULL) 
ss.aipe.crd.es.nclus.fixedbudget(budget, nindiv, cluscost, indivcost, nrep=NULL, 
	prtreat=NULL, iccy=NULL, es=NULL, estype = 1, numpredictor = 0, 
	iccz=NULL, r2within=NULL, r2between=NULL, assurance=NULL, 
	seed=123321, multicore=FALSE, numProc=NULL, conf.level=0.95, diffsize=NULL)
ss.aipe.crd.es.nindiv.fixedbudget(budget, nclus, cluscost, indivcost, nrep=NULL, 
	prtreat=NULL, iccy=NULL, es=NULL, estype = 1, numpredictor = 0, 
	iccz=NULL, r2within=NULL, r2between=NULL, assurance=NULL, 
	seed=123321, multicore=FALSE, numProc=NULL, conf.level=0.95, diffsize=NULL) 
ss.aipe.crd.es.both.fixedbudget(budget, cluscost=0, indivcost=1, es, estype = 1, 
	iccy, prtreat, r2between = 0, r2within = 0, numpredictor = 0, assurance=NULL, 
	conf.level = 0.95, nrep = 1000, iccz = NULL, seed = 123321, multicore = FALSE, 
	numProc=NULL, diffsize=NULL)
ss.aipe.crd.es.both.fixedwidth(width, cluscost=0, indivcost=1, es, estype = 1, 
	iccy, prtreat, r2between = 0, r2within = 0, numpredictor = 0, assurance=NULL, 
	conf.level = 0.95, nrep = 1000, iccz = NULL, seed = 123321, multicore = FALSE, 
	numProc=NULL, 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
iccy	The intraclass correlation of the dependent variable
es	The amount of effect size
estype	The type of effect size. There are only three possible options: 0 = the effect size using total standard deviation, 1 = the effect size using the individual-level standard deviation (level 1), 2 = the effect size using the cluster-level standard deviation (level 2)
numpredictor	If 1, a single covariate is included into the model. If 0, the no-covariate model is used. This function cannot handle multiple covariates. Therefore, only the values of 0 and 1 are allowed.
iccz	The intraclass correlation of the covariate (used when covariate = TRUE). If iccz = 0, the within-level covariate will be only used. If iccz = 1, the between-level covariate will be only used.
r2within	The proportion of variance explained in the within level (used when covariate = TRUE)
r2between	The proportion of variance explained in the between level (used when covariate = TRUE)
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)
nrep	The number of replications used in a priori Monte Carlo simulation
seed	A desired seed number
multicore	Use multiple processors within a computer. Specify as TRUE to use it.
numProc	The number of processors to be used when multicore=TRUE. If it is not specified, the package will use the maximum number of processors in a machine.
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 c(-1, 0, 1), the cluster sizes will be 24, 25, 26, 24, 25, 26, 24, 25, 26, and 24. Second, users may specify cluster size as positive decimals. The resulting cluster size will be based on the estimated cluster size multiplied 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 c(-1, 0, 1), the cluster sizes will be 24, 25, 26, 24, 25, 26, 24, 25, 26, and 24. If NULL, the cluster size is equal across clusters.

Details

Here are the functions' descriptions:

• ss.aipe.crd.es.nclus.fixedwidth Find the number of clusters given a specified width of the confidence interval and the cluster size

• ss.aipe.crd.es.nindiv.fixedwidth Find the cluster size given a specified width of the confidence interval and the number of clusters

• ss.aipe.crd.es.nclus.fixedbudget Find the number of clusters given a budget and the cluster size

• ss.aipe.crd.es.nindiv.fixedbudget Find the cluster size given a budget and the number of clusters

• ss.aipe.crd.es.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.es.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.es.nclus.fixedwidth and ss.aipe.crd.es.nclus.fixedbudget functions provide the number of clusters. The ss.aipe.crd.es.nindiv.fixedwidth and ss.aipe.crd.es.nindiv.fixedbudget functions provide the cluster size. The ss.aipe.crd.es.both.fixedbudget and ss.aipe.crd.es.both.fixedwidth provide the number of clusters and the cluster size, respectively.

Author(s)

Sunthud Pornprasertmanit ([e-mail])

References

Boker, S., M., N., Maes, H., Wilde, M., Spiegel, M., Brick, T., et al. (2011). OpenMx: An open source extended structural equation modeling framework. Psychometrika, 76, 306-317.

Cheung, M. W.-L. (2009). Constructing approximate confidence intervals for parameters with structural constructing approximate confidence intervals for parameters with structural equation models. Structural Equation Modeling, 16, 267-294.

Pornprasertmanit, S., & Schneider, W. J. (2010). Efficient sample size for power and desired accuracy in Cohen's d estimation in two-group cluster randomized design (Master Thesis). Illinois State University, Normal, IL.

Pornprasertmanic, S., & Schneider, W. J. (2014). Accuracy in parameter estimation in cluster randomized designs. Psychological Methods, 19, 356–379.

Examples

## Not run: 
# Examples for each function
ss.aipe.crd.es.nclus.fixedwidth(width=0.3, nindiv=20, es=0.5, estype=1, iccy=0.25, prtreat=0.5, 
	nrep=20)

ss.aipe.crd.es.nindiv.fixedwidth(width=0.3, 250, es=0.5, estype=1, iccy=0.25, prtreat=0.5, 
	nrep=20)

ss.aipe.crd.es.nclus.fixedbudget(budget=1000, nindiv=20, cluscost=0, indivcost=1, nrep=20, 
	prtreat=0.5, iccy=0.25, es=0.5)

ss.aipe.crd.es.nindiv.fixedbudget(budget=1000, nclus=200, cluscost=0, indivcost=1, nrep=20, 
	prtreat=0.5, iccy=0.25, es=0.5)

ss.aipe.crd.es.both.fixedbudget(budget=1000, cluscost=5, indivcost=1, es=0.5, estype=1, 
	iccy=0.25, prtreat=0.5, nrep=20)

ss.aipe.crd.es.both.fixedwidth(width=0.5, cluscost=5, indivcost=1, es=0.5, estype=1, iccy=0.25, 
	prtreat=0.5, nrep=20)

# Examples for different cluster size
ss.aipe.crd.es.nclus.fixedwidth(width=0.3, nindiv=20, es=0.5, estype=1, iccy=0.25, prtreat=0.5, 
nrep=20, diffsize = c(-2, 1, 0, 2, -1, 3, -3, 0, 0))

ss.aipe.crd.es.nclus.fixedwidth(width=0.3, nindiv=20, es=0.5, estype=1, iccy=0.25, prtreat=0.5, 
nrep=20, diffsize = c(0.6, 1.2, 0.8, 1.4, 1, 1, 1.1, 0.9))

## End(Not run)

MBESS documentation built on Oct. 26, 2023, 9:07 a.m.