cumul.rma.mh {metafor} | R Documentation |
The functions leave1out.rma.mh
and leave1out.rma.peto
repeatedly fit the specified model, adding one observation (i.e., 2x2 table) at a time to the model.
## S3 method for class 'rma.mh': cumul(x, order=NULL, digits=x$digits, transf=FALSE, ...) ## S3 method for class 'rma.peto': cumul(x, order=NULL, digits=x$digits, transf=FALSE, ...)
x |
an object of class "rma.mh" or "rma.peto" . |
order |
an optional vector with indices giving the desired order for the cumulative meta-analysis. |
digits |
an integer specifying the number of decimal places to which the printed results should be rounded (the default is to take the value from the object). |
transf |
logical indicating whether odds ratios or risk ratios (and the corresponding confidence interval bounds) should be given in the transformed (meaning: raw) units or in terms of log units (the default). |
... |
other arguments. |
An object of class c("list.rma", "cumul.rma")
. The object is a list containing the following components:
estimate |
estimated coefficients of the model. |
se |
standard errors of the coefficients. NA if transf=TRUE . |
zval |
test statistics of the coefficients. |
pval |
p-values for the test statistics. |
ci.lb |
lower bounds of the confidence intervals for the coefficients. |
ci.ub |
upper bounds of the confidence intervals for the coefficients. |
Q |
test statistics for the tests of heterogeneity. |
Qp |
p-values for the tests of heterogeneity. |
The object is formated and printed with print.list.rma
. A forest plot showing the results from the cumulative meta-analysis can be obtained with forest.cumul.rma
.
Wolfgang Viechtbauer; wvb@www.wvbauer.com; http://www.wvbauer.com/
### load BCG vaccine data data(dat.bcg) ### meta-analysis of the (log) risk ratios using the Mantel-Haenszel method res <- rma.mh(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg, measure="RR") cumul(res, order=order(dat.bcg$year)) cumul(res, order=order(dat.bcg$year), transf=TRUE) ### meta-analysis of the (log) odds ratios using Peto's method res <- rma.mh(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg) cumul(res, order=order(dat.bcg$year)) cumul(res, order=order(dat.bcg$year), transf=TRUE)