coefa_acm {coefa}

Aggregate co-occurrence matrices

Description

This function is used to aggregate those co-occurrence matrices to form an overall(pooled) co-occurrence matrix.The users can get two kinds of aggregated (i.e. pooled) co-occurrence matrices: the non-weighted aggregated co-occurrence matrix, and the weighted aggregated co-occurrence matrix. And the users can make their choices through the setting of corresponding parameters.

Usage

coefa_acm(x,sz,samplesized =c(TRUE,FALSE),raw=FALSE)

Arguments

x

A list containing multiple co-occurrence matrices.

sz

A vector containing multiple sample sized of original study. The elements of the vector should be in the same order as the original study, that is, the first element is the sample size of the first original study, and the second element is the sample size of the second original study....

samplesized

A logical value is used to select the weighting method. If "samplesized"=TRUE, the final aggregated co-occurrence matrix will be obtained by weighting the sample size. If "samplesized"=FALSE, the size of the sample size is not considered in the process of summarizing the co-occurrence matrix, that is, the co-occurrence matrix is added and divided by the number of matrices.

raw

A logical value.If raw=TRUE,Raw co-occurrence matrix and similarity matrix will be obtained. If raw=FALSE(The default value is FALSE),only similarity matrix will be obtained.

Value

A matrix formed by adding all the co-occurrence matrices,or adding them after weighting by sample size.

References

Shafer,A. B.(2005). Meta-analysis of the Brief Psychiatric Rating Scale factor structure. Psychological Assessment, 17(3),324–335.

Shafer,A. B. (2006). Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. Journal of clinical psychology, 62(1), 123–146.

Examples

#Suppose that matrices.gcm is the co-ocurrence matrices.
##Note:This is just an example.
##The real co-occurrence matrix should be generated from actual studies.
mx1.tflm<-matrix(c(1,0,0,1,1,0),nrow=2,byrow=2)
mx2.tflm<-matrix(c(1,0,0,1),nrow=2,byrow=2)
matrices.tflm<-list(mx1.tflm,mx2.tflm)
matrices.gcm<-coefa_gcm(matrices.tflm)
#Import a sample size collection containing the original study.
sz<-c(100,200)
#Aggregate multiple co-occurrence matrices.
matrices.acm<-coefa_acm(matrices.gcm,sz,samplesized = TRUE)
matrices.acm