View source: R/rank.probability.R

rank.probability | R Documentation |

Rank probabilities indicate the probability for each treatment to be best, second best, etc.

```
rank.probability(result, preferredDirection=1, covariate=NA)
## S3 method for class 'mtc.rank.probability'
print(x, ...)
## S3 method for class 'mtc.rank.probability'
plot(x, ...)
sucra(ranks)
rank.quantiles(ranks, probs=c("2.5%"=0.025, "50%"=0.5, "97.5%"=0.975))
```

`result` |
Object of S3 class |

`preferredDirection` |
Preferential direction of the outcome. Set 1 if higher values are preferred, -1 if lower values are preferred. |

`covariate` |
(Regression analyses only) Value of the covariate at which to compute rank probabilities. |

`x` |
An object of S3 class |

`...` |
Additional arguments. |

`ranks` |
A ranking matrix where the treatments are the rows (e.g. the result of rank.probability). |

`probs` |
Probabilities at which to give quantiles. |

For each MCMC iteration, the treatments are ranked by their effect relative to an arbitrary baseline. A frequency table is constructed from these rankings and normalized by the number of iterations to give the rank probabilities.

`rank.probability`

: A matrix (with class `mtc.rank.probability`

) with the treatments as rows and the ranks as columns.
`sucra`

: A vector of SUCRA values.
`rank.quantiles`

: A matrix with treatments as rows and quantiles as columns, giving the quantile ranks (by default, the median and 2.5% and 97.5% ranks).

Gert van Valkenhoef, Joël Kuiper

`relative.effect`

```
model <- mtc.model(smoking)
# To save computation time we load the samples instead of running the model
## Not run: results <- mtc.run(model)
results <- readRDS(system.file("extdata/luades-smoking-samples.rds", package="gemtc"))
ranks <- rank.probability(results)
print(ranks)
## Rank probability; preferred direction = 1
## [,1] [,2] [,3] [,4]
## A 0.000000 0.003000 0.105125 0.891875
## B 0.057875 0.175875 0.661500 0.104750
## C 0.228250 0.600500 0.170875 0.000375
## D 0.713875 0.220625 0.062500 0.003000
print(sucra(ranks))
## A B C D
## 0.03670833 0.39591667 0.68562500 0.88175000
print(rank.quantiles(ranks))
## 2.5% 50% 97.5%
## A 3 4 4
## B 1 3 4
## C 1 2 3
## D 1 1 3
plot(ranks) # plot a cumulative rank plot
plot(ranks, beside=TRUE) # plot a 'rankogram'
```

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