print.orbbound {metasens} | R Documentation |
Print method for objects of class orbbound
Description
Print method for objects of class orbbound
.
Usage
## S3 method for class 'orbbound'
print(
x,
common = x$x$common,
random = x$x$random,
header = TRUE,
backtransf = x$backtransf,
digits = gs("digits"),
digits.stat = gs("digits.stat"),
digits.pval = max(gs("digits.pval"), 2),
digits.tau2 = gs("digits.tau2"),
scientific.pval = gs("scientific.pval"),
big.mark = gs("big.mark"),
warn.deprecated = gs("warn.deprecated"),
...
)
Arguments
x |
An object of class |
common |
A logical indicating whether sensitivity analysis for common effect model should be printed. |
random |
A logical indicating whether sensitivity analysis for random effects model should be printed. |
header |
A logical indicating whether information on meta-analysis should be printed at top of printout. |
backtransf |
A logical indicating whether printed results
should be back transformed. If |
digits |
Minimal number of significant digits, see
|
digits.stat |
Minimal number of significant digits for z- or
t-value, see |
digits.pval |
Minimal number of significant digits for p-value
of overall treatment effect, see |
digits.tau2 |
Minimal number of significant digits for
between-study variance, see |
scientific.pval |
A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345. |
big.mark |
A character used as thousands separator. |
warn.deprecated |
A logical indicating whether warnings should be printed if deprecated arguments are used. |
... |
Additional arguments to catch deprecated arguments. |
Details
For summary measures 'RR', 'OR', and 'HR' column labeled maxbias
contains the relative bias, e.g. a value of 1.10 means a maximum
overestimation by 10 percent. If logscale=TRUE
for these
summary measures, maximum bias is instead printed as absolute bias.
Author(s)
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
See Also
Examples
data(Fleiss1993bin, package = "meta")
m1 <- metabin(d.asp, n.asp, d.plac, n.plac,
data = Fleiss1993bin, sm = "OR")
orb1 <- orbbound(m1, k.suspect = 1:5)
print(orb1, digits = 2)
# Print log odds ratios instead of odds ratios
#
print(orb1, digits = 2, backtransf = FALSE)
# Assuming that studies are missing on the left side
#
orb1.missleft <- orbbound(m1, k.suspect = 1:5, left = TRUE)
orb1.missleft
m2 <- metabin(d.asp, n.asp, d.plac, n.plac,
data = Fleiss1993bin, sm = "OR", method = "Inverse")
orb2 <- orbbound(m2, k.suspect = 1:5)
print(orb2, digits = 2)