multinma (version 0.1.3)

dic: Deviance Information Criterion (DIC)

Description

Calculate the DIC for a model fitted using the nma() function.

Usage

dic(x, ...)

Arguments

x

A fitted model object, inheriting class stan_nma

...

Other arguments (not used)

Value

A nma_dic object.

See Also

print.nma_dic() for printing details, plot.nma_dic() for producing plots of residual deviance contributions.

Examples

# NOT RUN {
## Smoking cessation
# Set up network of smoking cessation data
head(smoking)

smk_net <- set_agd_arm(smoking,
                       study = studyn,
                       trt = trtc,
                       r = r,
                       n = n,
                       trt_ref = "No intervention")

# Print details
smk_net

# }
# NOT RUN {
# Fitting a fixed effect model
smk_fit_FE <- nma(smk_net,
              trt_effects = "fixed",
              prior_intercept = normal(scale = 100),
              prior_trt = normal(scale = 100))

smk_fit_FE
# }
# NOT RUN {
# }
# NOT RUN {
# Fitting a random effects model
smk_fit_RE <- nma(smk_net,
                  trt_effects = "random",
                  prior_intercept = normal(scale = 100),
                  prior_trt = normal(scale = 100),
                  prior_het = normal(scale = 5))

smk_fit_RE
# }
# NOT RUN {
# }
# NOT RUN {
# Compare DIC of FE and RE models
(smk_dic_FE <- dic(smk_fit_FE))
(smk_dic_RE <- dic(smk_fit_RE))   # substantially better fit

# Plot residual deviance contributions under RE model
plot(smk_dic_RE)

# Check for inconsistency using UME model
# }
# NOT RUN {
# Fitting an unrelated mean effects (inconsistency) model
smk_fit_RE_UME <- nma(smk_net,
                      consistency = "ume",
                      trt_effects = "random",
                      prior_intercept = normal(scale = 100),
                      prior_trt = normal(scale = 100),
                      prior_het = normal(scale = 5))

smk_fit_RE_UME
# }
# NOT RUN {
# }
# NOT RUN {
# Compare DIC
smk_dic_RE
(smk_dic_RE_UME <- dic(smk_fit_RE_UME))  # no difference in fit

# Compare residual deviance contributions
plot(smk_dic_RE, smk_dic_RE_UME, show_uncertainty = FALSE)
# }

Run the code above in your browser using DataCamp Workspace