Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval = FALSE------------------------------------------------------------
# install.packages("bama")
## ---- eval = FALSE------------------------------------------------------------
# # install.packages(devtools)
# devtools::install_github("umich-cphds/bama", built_opts = c())
## ---- eval = FALSE------------------------------------------------------------
# library(bama)
# # print just the first 10 columns
# head(bama.data[,1:10])
## ---- eval = FALSE------------------------------------------------------------
# Y <- bama.data$y
# A <- bama.data$a
#
# # grab the mediators from the example data.frame
# M <- as.matrix(bama.data[, paste0("m", 1:100)], nrow(bama.data))
#
# # We just include the intercept term in this example as we have no covariates
# C1 <- matrix(1, 1000, 1)
# C2 <- matrix(1, 1000, 1)
# beta.m <- rep(0, 100)
# alpha.a <- rep(0, 100)
#
# out <- bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "BSLMM", seed = 1234,
# burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL,
# control = list(k = 2, lm0 = 1e-04, lm1 = 1, l = 1))
#
# # The package includes a function to summarise output from 'bama'
# summary <- summary(out)
# head(summary)
#
# # Product Threshold Gaussian
# ptgmod = bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "PTG", seed = 1234,
# burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL,
# control = list(lambda0 = 0.04, lambda1 = 0.2, lambda2 = 0.2))
#
# mean(ptgmod$beta.a)
# apply(ptgmod$beta.m, 2, mean)
# apply(ptgmod$alpha.a, 2, mean)
# apply(ptgmod$betam_member, 2, mean)
# apply(ptgmod$alphaa_member, 2, mean)
#
# # Gaussian Mixture Model
# gmmmod = bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "GMM", seed = 1234,
# burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL,
# control = list(phi0 = 0.01, phi1 = 0.01))
#
# mean(gmmmod$beta.a)
# apply(gmmmod$beta.m, 2, mean)
# apply(gmmmod$alpha.a, 2, mean)
#
# mean(gmmmod$sigma.sq.a)
# mean(gmmmod$sigma.sq.e)
# mean(gmmmod$sigma.sq.g)
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