# rank.probability: Calculating rank-probabilities In gemtc: Network Meta-Analysis Using Bayesian Methods

## Description

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

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```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)) ```

## Arguments

 `result` Object of S3 class `mtc.result` to be used in creation of the rank probability table `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 `rank.probability`. `...` 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.

## Details

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.

## Value

`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).

## Author(s)

Gert van Valkenhoef, Joël Kuiper

`relative.effect`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```model <- mtc.model(smoking) # To save computation time we load the samples instead of running the model ## Not run: results <- mtc.run(model) results <- dget(system.file("extdata/luades-smoking.samples.gz", 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' ```