R/bayesEnet.R
In abnormally-distributed/Bayezilla: Bayesian Models With JAGS and Several Utilities for Data Exploration and Model Evaluation.
Defines functions
bayesEnet
Documented in
bayesEnet
#' Bayesian Elastic Net
#'
#' @description The Bayesian elastic net described by Li and Lin (2010). For the binomial and poisson likelihoods
#' plug-in pseudo-variances are used.
#'
#' \cr
#' The model structure is given below: \cr
#' \cr
#' \cr
#' \if{html}{\figure{elasticNet.png}{}}
#' \if{latex}{\figure{elasticNet.png}{}}
#' \cr
#' \cr
#' Plugin Pseudo-Variances: \cr
#' \if{html}{\figure{pseudovar.png}{}}
#' \if{latex}{\figure{pseudovar.png}{}}
#' \cr
#' @param formula the model formula
#' @param data a data frame.
#' @param family one of "gaussian", "st" (Student-t with nu = 3), "binomial", or "poisson".
#' @param lambda.prior either "dmouch" (the default) or "gamma"
#' @param log_lik Should the log likelihood be monitored? The default is FALSE.
#' @param iter How many post-warmup samples? Defaults to 10000.
#' @param warmup How many warmup samples? Defaults to 1000.
#' @param adapt How many adaptation steps? Defaults to 2000.
#' @param chains How many chains? Defaults to 4.
#' @param thin Thinning interval. Defaults to 1.
#' @param method Defaults to "rjparallel". For an alternative parallel option, choose "parallel" or. Otherwise, "rjags" (single core run).
#' @param cl Use parallel::makeCluster(# clusters) to specify clusters for the parallel methods. Defaults to two cores.
#' @param ... Other arguments to run.jags.
#'
#' @references Li, Qing; Lin, Nan. The Bayesian elastic net. Bayesian Anal. 5 (2010), no. 1, 151--170. doi:10.1214/10-BA506. https://projecteuclid.org/euclid.ba/1340369796
#'
#' @return A run.jags object
#' @export
#'
#' @examples
#' bayesEnet()
#'
bayesEnet = function(formula, data, family = "gaussian", lambda.prior = "dmouch", log_lik = FALSE, iter=10000, warmup=1000, adapt=2000, chains=4, thin=1, method = "rjparallel", cl = makeCluster(2), ...){
X = model.matrix(formula, data)[,-1]
y = model.frame(formula, data)[,1]
if (lambda.prior == "dmouch"){
if (method == "parallel"){
message("method switching to rjparallel to enable use of DuMouchley's prior")
method <- "rjparallel"
}
if (family == "gaussian"){
jags_elastic_net = "model{
tau ~ dgamma(.01, .01)
sigma <- sqrt(1/tau)
lambdaL1 ~ dmouch(1)
lambdaL2 ~ dmouch(1)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * pow(sigma,2)) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/pow(sigma,2)) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
y[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]), tau)
log_lik[i] <- logdensity.norm(y[i], Intercept + sum(beta[1:P] * X[i,1:P]), tau)
ySim[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]), tau)
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X))
monitor <- c("Intercept", "beta", "sigma", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = 0,
"beta" = lmSolve(formula, data)[-1],
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"tau" = 1,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "st"){
jags_elastic_net = "model{
tau ~ dgamma(.01, .01)
sigma <- sqrt(1/tau)
lambdaL1 ~ dmouch(1)
lambdaL2 ~ dmouch(1)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * pow(sigma,2)) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/pow(sigma,2)) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
mu[i] <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dt(mu[i], tau, 3)
log_lik[i] <- logdensity.t(y[i], mu[i], tau, 3)
ySim[i] ~ dt(mu[i], tau, 3)
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X))
monitor <- c("Intercept", "beta", "sigma", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = 0,
"beta" = lmSolve(formula, data)[-1],
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"tau" = 1,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "binomial"){
jags_elastic_net = "model{
lambdaL1 ~ dmouch(1)
lambdaL2 ~ dmouch(1)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * sigma2) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/sigma2) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
logit(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dbern(psi[i])
log_lik[i] <- logdensity.bern(y[i], psi[i])
ySim[i] ~ dbern(psi[i])
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X), sigma2 =pow(mean(y), -1) * pow(1 - mean(y), -1))
monitor <- c("Intercept", "beta", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "binomial", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[1,1]),
"beta" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "binomial", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[-1,1]),
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "poisson"){
jags_elastic_net = "model{
lambdaL1 ~ dmouch(1)
lambdaL2 ~ dmouch(1)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * sigma2) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/sigma2) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
log(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dpois(psi[i])
log_lik[i] <- logdensity.pois(y[i], psi[i])
ySim[i] ~ dpois(psi[i])
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X), sigma2 = pow(mean(y), -1))
monitor <- c("Intercept", "beta", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "poisson", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[1,1]),
"beta" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "poisson", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[-1,1]),
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
}
if (lambda.prior == "gamma"){
if (family == "gaussian"){
jags_elastic_net = "model{
tau ~ dgamma(.01, .01)
sigma <- sqrt(1/tau)
lambdaL1 ~ dgamma(0.50, 0.2)
lambdaL2 ~ dgamma(0.50, 0.2)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * pow(sigma,2)) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/pow(sigma,2)) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
y[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]), tau)
log_lik[i] <- logdensity.norm(y[i], Intercept + sum(beta[1:P] * X[i,1:P]), tau)
ySim[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]), tau)
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X))
monitor <- c("Intercept", "beta", "sigma", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = 0,
"beta" = lmSolve(formula, data)[-1],
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"tau" = 1,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "st"){
jags_elastic_net = "model{
tau ~ dgamma(.01, .01)
sigma <- sqrt(1/tau)
lambdaL1 ~ dgamma(0.50, 0.2)
lambdaL2 ~ dgamma(0.50, 0.2)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * pow(sigma,2)) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/pow(sigma,2)) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
mu[i] <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dt(mu[i], tau, 3)
log_lik[i] <- logdensity.t(y[i], mu[i], tau, 3)
ySim[i] ~ dt(mu[i], tau, 3)
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X))
monitor <- c("Intercept", "beta", "sigma", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = 0,
"beta" = lmSolve(formula, data)[-1],
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"tau" = 1,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "binomial"){
jags_elastic_net = "model{
lambdaL1 ~ dgamma(0.50, 0.2)
lambdaL2 ~ dgamma(0.50, 0.2)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * sigma2) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/sigma2) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
logit(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dbern(psi[i])
log_lik[i] <- logdensity.bern(y[i], psi[i])
ySim[i] ~ dbern(psi[i])
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X), sigma2 =pow(mean(y), -1) * pow(1 - mean(y), -1))
monitor <- c("Intercept", "beta", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "binomial", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[1,1]),
"beta" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "binomial", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[-1,1]),
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "poisson"){
jags_elastic_net = "model{
lambdaL1 ~ dgamma(0.50, 0.2)
lambdaL2 ~ dgamma(0.50, 0.2)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * sigma2) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/sigma2) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
log(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dpois(psi[i])
log_lik[i] <- logdensity.pois(y[i], psi[i])
ySim[i] ~ dpois(psi[i])
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X), sigma2 = pow(mean(y), -1))
monitor <- c("Intercept", "beta", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "poisson", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[1,1]),
"beta" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "poisson", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[-1,1]),
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
}
}
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Defines functions bayesEnet
Documented in bayesEnet
#' Bayesian Elastic Net
#'
#' @description The Bayesian elastic net described by Li and Lin (2010). For the binomial and poisson likelihoods
#' plug-in pseudo-variances are used.
#'
#' \cr
#' The model structure is given below: \cr
#' \cr
#' \cr
#' \if{html}{\figure{elasticNet.png}{}}
#' \if{latex}{\figure{elasticNet.png}{}}
#' \cr
#' \cr
#' Plugin Pseudo-Variances: \cr
#' \if{html}{\figure{pseudovar.png}{}}
#' \if{latex}{\figure{pseudovar.png}{}}
#' \cr
#' @param formula the model formula
#' @param data a data frame.
#' @param family one of "gaussian", "st" (Student-t with nu = 3), "binomial", or "poisson".
#' @param lambda.prior either "dmouch" (the default) or "gamma"
#' @param log_lik Should the log likelihood be monitored? The default is FALSE.
#' @param iter How many post-warmup samples? Defaults to 10000.
#' @param warmup How many warmup samples? Defaults to 1000.
#' @param adapt How many adaptation steps? Defaults to 2000.
#' @param chains How many chains? Defaults to 4.
#' @param thin Thinning interval. Defaults to 1.
#' @param method Defaults to "rjparallel". For an alternative parallel option, choose "parallel" or. Otherwise, "rjags" (single core run).
#' @param cl Use parallel::makeCluster(# clusters) to specify clusters for the parallel methods. Defaults to two cores.
#' @param ... Other arguments to run.jags.
#'
#' @references Li, Qing; Lin, Nan. The Bayesian elastic net. Bayesian Anal. 5 (2010), no. 1, 151--170. doi:10.1214/10-BA506. https://projecteuclid.org/euclid.ba/1340369796
#'
#' @return A run.jags object
#' @export
#'
#' @examples
#' bayesEnet()
#'
bayesEnet = function(formula, data, family = "gaussian", lambda.prior = "dmouch", log_lik = FALSE, iter=10000, warmup=1000, adapt=2000, chains=4, thin=1, method = "rjparallel", cl = makeCluster(2), ...){
X = model.matrix(formula, data)[,-1]
y = model.frame(formula, data)[,1]
if (lambda.prior == "dmouch"){
if (method == "parallel"){
message("method switching to rjparallel to enable use of DuMouchley's prior")
method <- "rjparallel"
}
if (family == "gaussian"){
jags_elastic_net = "model{
tau ~ dgamma(.01, .01)
sigma <- sqrt(1/tau)
lambdaL1 ~ dmouch(1)
lambdaL2 ~ dmouch(1)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * pow(sigma,2)) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/pow(sigma,2)) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
y[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]), tau)
log_lik[i] <- logdensity.norm(y[i], Intercept + sum(beta[1:P] * X[i,1:P]), tau)
ySim[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]), tau)
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X))
monitor <- c("Intercept", "beta", "sigma", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = 0,
"beta" = lmSolve(formula, data)[-1],
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"tau" = 1,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "st"){
jags_elastic_net = "model{
tau ~ dgamma(.01, .01)
sigma <- sqrt(1/tau)
lambdaL1 ~ dmouch(1)
lambdaL2 ~ dmouch(1)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * pow(sigma,2)) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/pow(sigma,2)) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
mu[i] <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dt(mu[i], tau, 3)
log_lik[i] <- logdensity.t(y[i], mu[i], tau, 3)
ySim[i] ~ dt(mu[i], tau, 3)
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X))
monitor <- c("Intercept", "beta", "sigma", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = 0,
"beta" = lmSolve(formula, data)[-1],
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"tau" = 1,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "binomial"){
jags_elastic_net = "model{
lambdaL1 ~ dmouch(1)
lambdaL2 ~ dmouch(1)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * sigma2) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/sigma2) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
logit(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dbern(psi[i])
log_lik[i] <- logdensity.bern(y[i], psi[i])
ySim[i] ~ dbern(psi[i])
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X), sigma2 =pow(mean(y), -1) * pow(1 - mean(y), -1))
monitor <- c("Intercept", "beta", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "binomial", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[1,1]),
"beta" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "binomial", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[-1,1]),
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "poisson"){
jags_elastic_net = "model{
lambdaL1 ~ dmouch(1)
lambdaL2 ~ dmouch(1)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * sigma2) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/sigma2) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
log(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dpois(psi[i])
log_lik[i] <- logdensity.pois(y[i], psi[i])
ySim[i] ~ dpois(psi[i])
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X), sigma2 = pow(mean(y), -1))
monitor <- c("Intercept", "beta", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "poisson", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[1,1]),
"beta" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "poisson", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[-1,1]),
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
}
if (lambda.prior == "gamma"){
if (family == "gaussian"){
jags_elastic_net = "model{
tau ~ dgamma(.01, .01)
sigma <- sqrt(1/tau)
lambdaL1 ~ dgamma(0.50, 0.2)
lambdaL2 ~ dgamma(0.50, 0.2)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * pow(sigma,2)) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/pow(sigma,2)) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
y[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]), tau)
log_lik[i] <- logdensity.norm(y[i], Intercept + sum(beta[1:P] * X[i,1:P]), tau)
ySim[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]), tau)
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X))
monitor <- c("Intercept", "beta", "sigma", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = 0,
"beta" = lmSolve(formula, data)[-1],
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"tau" = 1,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "st"){
jags_elastic_net = "model{
tau ~ dgamma(.01, .01)
sigma <- sqrt(1/tau)
lambdaL1 ~ dgamma(0.50, 0.2)
lambdaL2 ~ dgamma(0.50, 0.2)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * pow(sigma,2)) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/pow(sigma,2)) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
mu[i] <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dt(mu[i], tau, 3)
log_lik[i] <- logdensity.t(y[i], mu[i], tau, 3)
ySim[i] ~ dt(mu[i], tau, 3)
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X))
monitor <- c("Intercept", "beta", "sigma", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = 0,
"beta" = lmSolve(formula, data)[-1],
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"tau" = 1,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "binomial"){
jags_elastic_net = "model{
lambdaL1 ~ dgamma(0.50, 0.2)
lambdaL2 ~ dgamma(0.50, 0.2)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * sigma2) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/sigma2) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
logit(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dbern(psi[i])
log_lik[i] <- logdensity.bern(y[i], psi[i])
ySim[i] ~ dbern(psi[i])
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X), sigma2 =pow(mean(y), -1) * pow(1 - mean(y), -1))
monitor <- c("Intercept", "beta", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "binomial", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[1,1]),
"beta" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "binomial", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[-1,1]),
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
if (family == "poisson"){
jags_elastic_net = "model{
lambdaL1 ~ dgamma(0.50, 0.2)
lambdaL2 ~ dgamma(0.50, 0.2)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
eta[p] ~ dgamma(.5, (8 * lambdaL2 * sigma2) / pow(lambdaL1, 2)) T(1,)
beta_prec[p] <- (lambdaL2/sigma2) * (eta[p]/(eta[p]-1))
beta[p] ~ dnorm(0, beta_prec[p])
}
for (i in 1:N){
log(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P])
y[i] ~ dpois(psi[i])
log_lik[i] <- logdensity.pois(y[i], psi[i])
ySim[i] ~ dpois(psi[i])
}
Deviance <- -2 * sum(log_lik[1:N])
}"
P <- ncol(X)
write_lines(jags_elastic_net, "jags_elastic_net.txt")
jagsdata <- list(X = X, y = y, N = length(y), P = ncol(X), sigma2 = pow(mean(y), -1))
monitor <- c("Intercept", "beta", "lambdaL1", "lambdaL2", "Deviance", "ySim", "log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
inits <- lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "poisson", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[1,1]),
"beta" = as.vector(coef(glmnet::glmnet(x = X, y = y, family = "poisson", lambda = runif(1, .01, .15), alpha = runif(1, .01, .5), standardize = FALSE))[-1,1]),
"eta" = 1 + abs(jitter(rep(1, P), amount = .25)),
"lambdaL1" = 5,
"lambdaL2" = 15,
"ySim" = sample(y, length(y)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
out = run.jags(model = "jags_elastic_net.txt", modules = c("bugs on", "glm on", "dic off"), n.chains = chains, monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_elastic_net.txt")
if (!is.null(cl)) {
parallel::stopCluster(cl = cl)
}
return(out)
}
}
}
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