heatplot.crossnma {crossnma} | R Documentation |

Produces a heat plot that contain point estimates of relative effects for all possible pairs of treatments along with 95% credible intervals obtained with the quantile method.

```
## S3 method for class 'crossnma'
heatplot(
x,
median = TRUE,
exp = TRUE,
order = 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 = 2,
...
)
heatplot(x, ...)
```

`x` |
An object created with |

`median` |
A logical indicating whether to use the median (default) or mean to measure relative treatment effects. |

`exp` |
If TRUE (default), odds ratios are displayed. If FALSE, log odds ratios will be presented. |

`order` |
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. |

`...` |
Additional arguments (ignored at the moment). |

League heat plot, where a color scale is used to represent the relative treatment effects.

Tasnim Hamza tasnim.hamza@ispm.unibe.ch

```
# 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
head(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, n.adapt = 20,
n.iter = 50, thin = 1, n.chains = 3))
# Create a heat plot
heatplot(fit)
```

[Package *crossnma* version 1.0.1 Index]