cumul.rma.uni {metafor} | R Documentation |
The function cumul.rma.uni
repeatedly fits the specified model, adding one observation at a time to the model.
## S3 method for class 'rma.uni': cumul(x, order=NULL, digits=x$digits, transf=FALSE, targs=NULL, ...)
x |
an object of class "rma.uni" . |
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 |
an optional argument specifying the name of a function that should be used to transform the model coefficients and interval bounds (e.g., transf=exp ). Defaults to FALSE , which means that no transformation is used. |
targs |
optional arguments needed by the function specified under transf . |
... |
other arguments. |
The model specified by x
must be a model without moderators (i.e., either a fixed- or a random-effects model).
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 is used to transform the coefficients. |
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. |
tau2 |
estimated amounts of (residual) heterogeneity (only for random-effects models). |
I2 |
values of I^2 (only for random-effects models). |
H2 |
values of H^2 (only for random-effects models). |
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) ### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg) dat <- cbind(dat.bcg, dat) ### random-effects model res <- rma(yi, vi, data=dat, method="REML") cumul(res, transf=exp, order=order(dat$year))