relative.effect: Calculating relative effects

Description

Calculates the relative effects of pairs of treatments.

Usage

relative.effect(result, t1, t2 = c(), preserve.extra = TRUE)

Arguments

result

An object of S3 class mtc.result to derive the relative effects from.

A list of baselines to calculate a relative effects against. Will be extended to match the length of t2.

A list of treatments to calculate the relative effects for. Will be extended to match the length of t1. If left empty and t1 is a single treatment, relative effects of all treatments except t1 will be calculated.

preserve.extra

Indicates whether to preserve extra parameters such as the sd.w and sd.d.

Value

Returns an mtc.results object containing the calculated relative effects.

encoding

utf8

Examples

Run this code

file <- system.file("extdata/luades-smoking.gemtc", package="gemtc")
network <- read.mtc.network(file)
model <- mtc.model(network)
# To save computation time we load the samples instead of running the model
results <- mtc.run(model)
results <- dget(system.file("extdata/luades-smoking.samples.gz", package="gemtc"))

# Creates a forest plot of the relative effects
forest(relative.effect(results, "A"))

summary(relative.effect(results, "B", c("A", "C", "D")))
## Iterations = 5010:25000
## Thinning interval = 10 
## Number of chains = 4 
## Sample size per chain = 2000 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##          Mean     SD Naive SE Time-series SE
## d.B.A -0.4965 0.4081 0.004563       0.004989
## d.B.C  0.3394 0.4144 0.004634       0.004859
## d.B.D  0.6123 0.4789 0.005354       0.005297
## sd.d   0.8465 0.1913 0.002139       0.002965
## 
## 2. Quantiles for each variable:
## 
##          2.5%     25%     50%     75%  97.5%
## d.B.A -1.3407 -0.7530 -0.4910 -0.2312 0.2985
## d.B.C -0.4809  0.0744  0.3411  0.5977 1.1702
## d.B.D -0.3083  0.3005  0.6044  0.9152 1.5790
## sd.d   0.5509  0.7119  0.8180  0.9542 1.2827