sumz | R Documentation |
Combine \mjseqnp-values using the sum z method\loadmathjax
sumz(p, weights = NULL, data = NULL, subset = NULL, na.action = na.fail, log.p = FALSE, log.input = FALSE) ## S3 method for class 'sumz' print(x, ...)
p |
\sigvec |
weights |
A vector of weights |
data |
Optional data frame containing variables |
subset |
Optional vector of logicals to specify a subset of the \mjseqnp-values |
na.action |
A function indicating what should happen when data contains NAs |
log.p |
\logp |
log.input |
\loginput |
x |
An object of class ‘ |
... |
Other arguments to be passed through |
Defined as \mjdeqn\frac\sum_i=1^k w_i z(p_i)\sqrt \sum_i=1^k w_i ^ 2sum (w * z(p)) / sqrt(sum (w * w)) is a \mjseqnz where \mjseqnk is the number of studies and \mjseqnw are the weights \insertCitestouffer49metap. By default the weights are equal. In the absence of effect sizes (in which case a method for combining effect sizes would be more appropriate anyway) best results are believed to be obtained with weights proportional to the square root of the sample sizes \insertCitezaykin11metap
\insertNoCitebecker94metap \insertNoCiterosenthal78metap
\ltlttwo If the omitted \mjseqnp values had supplied weights a further warning is issued.
\plotmethodAn object of class ‘sumz
’ and
‘metap
’, a list with entries
z |
Transformed sum of \mjseqnz values |
p |
Associated \mjseqnp-value |
validp |
The input vector with illegal values removed |
weights |
The weight vector corresponding to |
Michael Dewey
See also plotp
data(dat.metap) teachexpect <- dat.metap$teachexpect sumz(teachexpect) # z = 2.435, p = 0.0074, from Becker beckerp <- dat.metap$beckerp sumz(beckerp) # z = 1.53, NS, from Beckerp rosenthal <- dat.metap$rosenthal sumz(rosenthal$p) # 2.39, p = 0.009 sumz(p, df, rosenthal) # 3.01, p = 0.0013 validity <- dat.metap$validity$p sumz(validity) # z = 8.191, p = 1.25 * 10^{-16} all.equal(exp(sumz(validity, log.p = TRUE)$p), sumz(validity)$p) all.equal(sumz(log(validity), log.input = TRUE)$p, sumz(validity)$p)
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