View source: R/print.decomp.design.R
print.decomp.design | R Documentation |
Print method for objects of class decomp.design
.
## S3 method for class 'decomp.design'
print(
x,
digits.Q = gs("digits.Q"),
showall = FALSE,
digits.pval.Q = gs("digits.pval.Q"),
digits.tau2 = gs("digits.tau2"),
scientific.pval = gs("scientific.pval"),
big.mark = gs("big.mark"),
nchar.trts = x$nchar.trts,
sort = TRUE,
legend = TRUE,
...
)
x |
An object of class |
digits.Q |
Minimal number of significant digits for Q
statistics, see |
showall |
A logical indicating whether results should be shown for all designs or only designs contributing to chi-squared statistics (default). |
digits.pval.Q |
Minimal number of significant digits for
p-value of heterogeneity tests, see |
digits.tau2 |
Minimal number of significant digits for
between-study variance, see |
scientific.pval |
A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345. |
big.mark |
A character used as thousands separator. |
nchar.trts |
A numeric defining the minimum number of characters used to create unique treatment names. |
sort |
A logical indicating whether to sort results by p-values. |
legend |
A logical indicating whether a legend should be printed. |
... |
Additional arguments (ignored at the moment). |
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de, Ulrike Krahn ulrike.krahn@bayer.com
decomp.design
data(Senn2013)
# Only consider first five studies (to reduce runtime of example)
#
studies <- unique(Senn2013$studlab)
Senn2013.5 <- subset(Senn2013, studlab %in% studies[1:5])
# Conduct network meta-analysis with placebo as reference treatment
#
net1 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013.5, sm = "MD", reference = "plac")
# Decomposition of Cochran's Q
#
decomp.design(net1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.