Nothing
## ----setup, echo = FALSE------------------------------------------------------
knitr::opts_knit$set(global.par = TRUE)
knitr::opts_chunk$set(fig.width = 7, fig.height = 4)
## ---- eval=FALSE, echo=TRUE---------------------------------------------------
# y1 + y2 | sd1 + sd2 ~ x1 + x2 + x3 + ns(n) | z1 + z2 + z3 | treat + trial (+ groups)
## ---- eval=FALSE, echo=TRUE---------------------------------------------------
# f <- "y1 + y2 | sd1 + sd2 ~ x1 + x2 + x3 + ns(n) | z1 + z2 + z3 | treat + trial + groups"
# out <- bmeta_analyze(formula(f), data = df,
# mcmc = list(ndiscard = 20000, nskip = 5, nkeep = 10000),
# prior = list(model = "NoRecovery"))
## ---- eval=FALSE, echo=TRUE---------------------------------------------------
# Rho_init <- diag(3) # assume 3 by 3
# Rho_init[upper.tri(Rho_init)] <-
# Rho_init[lower.tri(Rho_init)] <- 0.2
# out <- bmeta_analyze(formula(f), data = df,
# prior = list(model = "EquiCorr"), # abbreviation allowed
# control = list(sample_Rho = FALSE),
# init = list(Rho = Rho_init))
## ---- eval=FALSE, echo=TRUE---------------------------------------------------
# f <- "y | sd ~ x1 + x2 + x3 + ns(n) | z1 + z2 + z3 | treat + trial"
# out <- bmeta_analyze(formula(f), data = df,
# mcmc = list(discard = 20000, nskip = 5, nkeep = 10000),
# prior = list(df = 3)) # heavy-tailed random effects
## ---- eval=FALSE, echo=TRUE---------------------------------------------------
# Rho_init <- diag(nT) # nT = the number of treatments
# Rho_init[upper.tri(Rho_init)] <-
# Rho_init[lower.tri(Rho_init)] <- 0.2
# out <- bmeta_analyze(formula(f), data = df,
# control = list(sample_df = TRUE, sample_Rho = FALSE),
# init = list(Rho = Rho_init))
## ---- eval=FALSE, echo=TRUE---------------------------------------------------
# f <- "y | sd ~ x1 + x2 + x3 + ns(n) | treat + trial"
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