Print method for objects of class `meta`

.

```
# S3 method for meta
print(x, sortvar, comb.fixed = x$comb.fixed,
comb.random = x$comb.random, prediction = x$prediction,
details = FALSE, ma = TRUE, backtransf = x$backtransf,
pscale = x$pscale, irscale = x$irscale, irunit = x$irunit,
digits = gs("digits"), digits.se = gs("digits.se"),
digits.tau2 = gs("digits.tau2"), digits.I2 = gs("digits.I2"),
digits.prop = gs("digits.prop"), digits.weight = gs("digits.weight"),
big.mark = gs("big.mark"), warn.backtransf = FALSE, ...)
```cilayout(bracket = "[", separator = "; ")

x

An object of class `meta`

sortvar

An optional vector used to sort the individual
studies (must be of same length as `x$TE`

).

comb.fixed

A logical indicating whether a fixed effect meta-analysis should be conducted.

comb.random

A logical indicating whether a random effects meta-analysis should be conducted.

prediction

A logical indicating whether a prediction interval should be printed.

details

A logical indicating whether further details of individual studies should be printed.

ma

A logical indicating whether the summary results of the meta-analysis should be printed.

backtransf

A logical indicating whether printed results
should be back transformed. If `backtransf = TRUE`

, results
for `sm = "OR"`

are printed as odds ratios rather than log
odds ratios and results for `sm = "ZCOR"`

are printed as
correlations rather than Fisher's z transformed correlations, for
example.

pscale

A numeric giving scaling factor for printing of
single event probabilities or risk differences, i.e. if argument
`sm`

is equal to `"PLOGIT"`

, `"PLN"`

,
`"PRAW"`

, `"PAS"`

, `"PFT"`

, or `"RD"`

.

irscale

A numeric defining a scaling factor for printing of
single incidence rates or incidence rate differences, i.e. if
argument `sm`

is equal to `"IR"`

, `"IRLN"`

,
`"IRS"`

, `"IRFT"`

, or `"IRD"`

.

irunit

A character specifying the time unit used to calculate rates, e.g. person-years.

digits

Minimal number of significant digits, see
`print.default`

.

digits.se

Minimal number of significant digits for standard
deviations and standard errors, see `print.default`

.

digits.tau2

Minimal number of significant digits for
between-study variance, see `print.default`

.

digits.I2

Minimal number of significant digits for I-squared
and Rb statistic, see `print.default`

.

digits.prop

Minimal number of significant digits for
proportions, see `print.default`

.

digits.weight

Minimal number of significant digits for
weights, see `print.default`

.

big.mark

A character used as thousands separator.

warn.backtransf

A logical indicating whether a warning should be printed if backtransformed proportions and rates are below 0 and backtransformed proportions are above 1.

…

Additional arguments (passed on to
`print.summary.meta`

called internally).

bracket

A character with bracket symbol to print lower confidence interval: "[", "(", "{", "".

separator

A character string with information on separator between lower and upper confidence interval.

R function cilayout can be utilised to change the layout to print
confidence intervals (both in printout from print.meta and
print.summary.meta function as well as in forest plots). The
default layout is "[lower; upper]". Another popular layout is
"(lower - upper)" which is used throughout an R session by using R
command `cilayout("(", " - ")`

.

Argument `pscale`

can be used to rescale single proportions or
risk differences, e.g. `pscale = 1000`

means that proportions
are expressed as events per 1000 observations. This is useful in
situations with (very) low event probabilities.

Argument `irscale`

can be used to rescale single rates or rate
differences, e.g. `irscale = 1000`

means that rates are
expressed as events per 1000 time units, e.g. person-years. This is
useful in situations with (very) low rates. Argument `irunit`

can be used to specify the time unit used in individual studies
(default: "person-years"). This information is printed in summaries
and forest plots if argument `irscale`

is not equal to 1.

Cooper H & Hedges LV (1994), *The Handbook of
Research Synthesis*. Newbury Park, CA: Russell Sage Foundation.

Crippa A, Khudyakov P, Wang M, Orsini N, Spiegelman D (2016), A new measure
of between-studies heterogeneity in meta-analysis. *Statistics in
Medicine*, **35**, 3661--75.

Higgins JPT & Thompson SG (2002), Quantifying heterogeneity in a
meta-analysis. *Statistics in Medicine*, **21**, 1539--58.

```
# NOT RUN {
data(Fleiss93cont)
m1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c,
data = Fleiss93cont, sm = "SMD",
studlab = paste(study, year))
m1
print(m1, digits = 2)
# }
# NOT RUN {
# Use unicode characters to print tau^2 and I^2
print(m1, text.tau2 = "\u03c4\u00b2", text.I2 = "I\u00b2")
# }
# NOT RUN {
# }
```

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