| coefa_tflm {coefa} | R Documentation |
Trimming factor loading matrices
Description
The original factor loading matrices after removing nulls will be trimmed through this function.At the end all the matrices only have two elements,0 or 1.
Usage
coefa_tflm(x,methodE=c("s","ls"),cutoff=c(0.3,0.4,0.5))
Arguments
x |
A list of multiple original factor loading matrices included without NA. |
methodE |
The methods for trimming multiple factor loading matrices from the original study."s" and "ls" are two methods of trimming the original factor loading matrix, which are widely used in coefa studies.The s = Shafer’s(2005) method; ls = the Loeber and Schmaling’s method(1985). |
cutoff |
The Criteria for trimming factor loadings matrices. When greater than the cutoff value, a factor loading is assigned as 1; otherwise, it assigned as 0. And the cutoff value can be given as 0.3, or 0.4, or 0.5. |
Details
This function is mainly used to discard the intensity information of the original factor loading matrices and to retain the direction information which make it into binary data.
During the coefa trimming process,the strength information of the factor loading matrices are given up,and the direction information is preserved. "s" and "ls" are two different methods in the coefa study.
If method="s", the factor loading matrices will be trimmed using the Shafer's(2005)method, that is, only the highest salient loadings of each variable in each factor are retained. If method="ls", the factor loading matrices will be trimmed using the Loeber and Schmaling's (1985) method,all insignificant factor loadings will be valued as 0.
Cutoff is just some fixed criterion for trimming the factor loading matrix. For example:if cutoff=0.3,elements in the factor loading matrix greater than 0.3 will be given the value of 1,and elements less than 0.3 will be replaced with 0.
Value
A list contained those trimmed factor loading matrices.
References
Loeber,R., & Schmaling, K. B. (1985). Empirical evidence for overt and covert patterns of antisocial conduct problems: a metaanalysis. Journal of abnormal child psychology, 13(2), 337–353.
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
mx1<-matrix(c(0.1,0.2,0.3,0.4),nrow=2)
mx2<-matrix(c(0.4,0.3,0.2,0.1),nrow=2)
list1<-list(mx1,mx2)
#Trim matrices using the Shafer's method ,cutoff is 0.3.
matrices.tflm<-coefa_tflm(list1,methodE="s",cutoff=0.3)