Description Usage Arguments Details Value Author(s) References See Also Examples
Compute Cochran's Q statistic for testing whether the a fixed effects or a random effects model will be appropriate.
1 | 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.
1 | ##none now, this requires a pretty elaborate example
|
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Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
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as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
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