Column-wise weighted least squares meta analysis {crwbmetareg}

Column-wise weighted least squares meta analysis

Description

Column-wise weighted least squares meta analysis.

Usage

colwlsmeta(yi, vi)

Arguments

yi

A matrix with the observations.

vi

A matrix with the variances of the observations.

Details

The weighted least squares (WLS) meta analysis is performed in a column-wise fashion. This function is suitable for simulation studies, where one can perform multiple WLS meta analyses at once. See references for this.

Value

A vector with many elements. The fixed effects mean estimate, the \bar{v} estimate, the I^2, the H^2, the Q test statistic and it's p-value, the \tau^2 estimate and the random effects mean estimate.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Stanley T. D. and Doucouliagos H. (2015). Neither fixed nor random: weighted least squares meta-analysis. Statistics in Medicine, 34(13), 2116–2127.

Stanley, T. D. and Doucouliagos, H. (2017). Neither fixed nor random: Weighted least squares meta-regression. Research synthesis methods, 8(1): 19–42.

See Also

wlsmeta

Examples

y <- matrix( rnorm(50* 5), ncol = 5)
vi <- matrix( rexp(50* 5), ncol = 5)
colwlsmeta(y, vi)
wlsmeta(y[, 1], vi[, 1])