rank.probability: Calculating rank-probabilities

Description

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

Usage

rank.probability(result, preferredDirection=1, covariate=NA)
# S3 method for mtc.rank.probability
print(x, ...)
# S3 method for 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.

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.

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).

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.

Examples

Run this code

# NOT RUN {
model <- mtc.model(smoking)
# To save computation time we load the samples instead of running the model
# }
# NOT RUN {
results <- mtc.run(model)
# }
# NOT RUN {
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'
# }