heatplot.crossnma {crossnma} | R Documentation |
Heat Plot
Description
Produces a heat plot that contain point estimates of relative effects for all possible pairs of treatments along with credible intervals obtained with the quantile method.
Usage
## S3 method for class 'crossnma'
heatplot(
x,
median = TRUE,
backtransf = x$model$backtransf,
seq = NULL,
low.colour = "red",
mid.colour = "white",
high.colour = "springgreen4",
cov1.value = NULL,
cov2.value = NULL,
cov3.value = NULL,
size = 6,
size.trt = 20,
size.axis = 12,
digits = gs("digits.forest"),
exp = backtransf,
...
)
Arguments
x |
An object created with |
median |
A logical indicating whether to use the median (default) or mean to measure relative treatment effects. |
backtransf |
A logical indicating whether results should be
back transformed. If |
seq |
A vector of treatment names (character) representing the order in which to display these treatments. |
low.colour |
A string indicating the colour of low relative treatment effects for the heat plot (e.g odds ratio of ~0.5) |
mid.colour |
A string indicating the colour of null relative treatment effects for the heat plot (e.g odds ratio of ~1.0). |
high.colour |
A string indicating the colour of high relative treatment effects for the heat plot (e.g odds ratio of ~2.0). |
cov1.value |
The participant covariate value of |
cov2.value |
The participant covariate value of |
cov3.value |
The participant covariate value of |
size |
The size of cell entries with the relative treatment effect and 95% credible intervals. |
size.trt |
The size of treatment names placed on the top and left of the plot. |
size.axis |
The size of labels on the top and left of the plot |
digits |
The number of digits to be used when displaying the results. |
exp |
Deprecated argument (replaced by |
... |
Additional arguments (ignored at the moment). |
Value
League heat plot, where a color scale is used to represent the values of relative treatment effects.
Author(s)
Tasnim Hamza tasnim.hamza@ispm.unibe.ch
See Also
Examples
## 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
set.seed(1909)
fit <- crossnma(mod)
# Create a heat plot
heatplot(fit)
## End(Not run)