gap (version 1.5-1)

mvmeta: Multivariate meta-analysis based on generalized least squares

Description

This function accepts a data matrix of parameter estimates and their variance-covariance matrix from individual studies and obtain a generalized least squares (GLS) estimate and heterogeneity statistic.

Usage

mvmeta(b, V)

Value

The returned value is a list containing:

d

the compact parameter estimates

Psi

the compact covariance-covariance matrix

X

the design matrix

beta

the pooled parameter estimates

cov.beta

the pooled variance-covariance matrix

X2

the Chi-squared statistic for heterogeneity

df

the degrees(s) of freedom

p

the p value

Arguments

b

the parameter estimates.

V

the triangular variance-covariance matrix.

Author

Jing Hua Zhao

Details

For instance, this would be appropriate for combining linear correlation coefficients of single nucleotide polymorphisms (SNPs) for a given region.

References

Hartung J, Knapp G, Sinha BK. Statistical Meta-analysis with Applications, Wiley 2008.

See Also

metareg

Examples

if (FALSE) {
# example 11.3 from Hartung et al.
#
b <- matrix(c(
0.808, 1.308, 1.379, NA, NA,
NA, 1.266, 1.828, 1.962, NA,
NA, 1.835, NA, 2.568, NA,
NA, 1.272, NA, NA, 2.038,
1.171, 2.024, 2.423, 3.159, NA,
0.681, NA, NA, NA, NA),ncol=5, byrow=TRUE)

psi1 <- psi2 <- psi3 <- psi4 <- psi5 <- psi6 <- matrix(0,5,5)

psi1[1,1] <- 0.0985
psi1[1,2] <- 0.0611
psi1[1,3] <- 0.0623
psi1[2,2] <- 0.1142
psi1[2,3] <- 0.0761
psi1[3,3] <- 0.1215

psi2[2,2] <- 0.0713
psi2[2,3] <- 0.0539
psi2[2,4] <- 0.0561
psi2[3,3] <- 0.0938
psi2[3,4] <- 0.0698
psi2[4,4] <- 0.0981

psi3[2,2] <- 0.1228
psi3[2,4] <- 0.1119
psi3[4,4] <- 0.1790

psi4[2,2] <- 0.0562
psi4[2,5] <- 0.0459
psi4[5,5] <- 0.0815

psi5[1,1] <- 0.0895
psi5[1,2] <- 0.0729
psi5[1,3] <- 0.0806
psi5[1,4] <- 0.0950
psi5[2,2] <- 0.1350
psi5[2,3] <- 0.1151
psi5[2,4] <- 0.1394
psi5[3,3] <- 0.1669
psi5[3,4] <- 0.1609
psi5[4,4] <- 0.2381

psi6[1,1] <- 0.0223

V <- rbind(psi1[upper.tri(psi1,diag=TRUE)],psi2[upper.tri(psi2,diag=TRUE)],
psi3[upper.tri(psi3,diag=TRUE)],psi4[upper.tri(psi4,diag=TRUE)],
psi5[upper.tri(psi5,diag=TRUE)],psi6[upper.tri(psi6,diag=TRUE)])

mvmeta(b,V)
}