Description Usage Arguments References Examples

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

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

`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. |

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

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)
``` |

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