BLUE_c: BLUEs of global location and scale parameters In metaBLUE: BLUE for Combining Location and Scale Information in a Meta-Analysis

Description

To obtain the global or overall best linear unbiased estimator (BLUE) of location and scale parameters (Yang et al., 2018).

Usage

 `1` ```BLUE_c(alpha_c, B_c, X_c) ```

Arguments

 `alpha_c` the expectation of a combined standardized vector of ordered summary statistics, i.e. equation (3.21) in Yang et al. (2018). `B_c` the variance-covariance matrix of a combined standardized vector of ordered summary statistics, i.e. equation (3.22) in Yang et al. (2018). `X_c` a combined vector of ordered summary statistics.

References

Yang X, Hutson AD, and Wang D. (2018). A generalized BLUE approach for combining location and scale information in a meta-analysis (Submitted).

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```n1<-30 # sample sizes of three included studies n2<-45 n3<-67 X1<-c(3,1.2) # the mean and standard deviation X2<-c(1,4,10) # the sample mean, minimum and maximum values X3<-c(1.5,3,5.5,8,12) # the sample mean, first and third quartiles, and minimum and maximum values X_c<-c(X1[1],X2,X3) alpha1<-0 #Approximate by the CLT. B1<-1/sqrt(n1) alpha2<-BLUE_s(X2,n2,"S1")\$alpha B2<-BLUE_s(X2,n2,"S1")\$B alpha3<-BLUE_s(X3,n3,"S3")\$alpha B3<-BLUE_s(X3,n3,"S3")\$B alpha_c<-c(alpha1,alpha2,alpha3) B_c<-Matrix::bdiag(B1,B2,B3) BLUE_c(alpha_c,B_c,X_c) ```

metaBLUE documentation built on May 29, 2018, 9:04 a.m.