f.Q {GeneMeta} | R Documentation |
Compute Cochran's Q statistic for testing whether the a fixed effects or a random effects model will be appropriate.
f.Q(dadj, varadj)
dadj |
A matrix, each row is a gene, each column a study, of the estimated t-statistics. |
varadj |
A matrix, each row is a gene, each column a study, of the estimated, adjusted variances of the t-statistics. |
A straightforward computation of Cochran's Q statistic. If the null hypothesis that the data are well modeled by a fixed effects design is true then the estimate Q values will have approximately a chi-squared distribution with degrees of freedom equal to the number of studies minus one.
A vector of length equal to the number of rows of dadj
with the
Q statistics.
L. Lusa and R. Gentleman
Choi et al, Combining multiple microarray studies and modeling interstudy variation. Bioinformatics, 2003, i84-i90.
##none now, this requires a pretty elaborate example