model <- mtc.model(smoking)
# 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
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