print.crossnma {crossnma} | R Documentation |
Print call used to create JAGS model for cross-design & -format network meta-analysis or regression
## S3 method for class 'crossnma'
print(x, backtransf = x$model$backtransf, digits = gs("digits"), ...)
x |
An object of class |
backtransf |
A logical indicating whether results should be
back transformed. If |
digits |
The number of significant digits printed. |
... |
Additional arguments. |
No return value (print function).
Tasnim Hamza tasnim.hamza@ispm.unibe.ch, Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
## Not run:
# We conduct a network meta-analysis assuming a random-effects
# model.
# The data comes from randomized-controlled trials and
# non-randomized studies (combined naively)
head(ipddata) # participant-level data
stddata # study-level data
# Create a JAGS model
mod <- crossnma.model(treat, id, relapse, n, design,
prt.data = ipddata, std.data = stddata,
reference = "A", trt.effect = "random", method.bias = "naive")
# Fit JAGS model
# (suppress warning 'Adaptation incomplete' due to n.adapt = 20)
fit <-
suppressWarnings(crossnma(mod))
fit
## End(Not run)