R/extLASSODC.R
In abnormally-distributed/Bayezilla: Bayesian Models With JAGS and Several Utilities for Data Exploration and Model Evaluation.
Defines functions
extLASSODC
Documented in
extLASSODC
#' Extended Bayesian LASSO with unpenalized design covariates
#'
#' @description This is the extended Bayesian LASSO presented by
#' Crispin M. Mutshinda and Mikko J. Sillanpää (2010) which is an improvement on the Baysian LASSO
#' of Park & Casella (2008). Two variants are provided here. \cr
#' \cr
#' The first version of the model is the original specification in Mutshinda and Sillanpää (2010), labeled
#' "classic" in the options here. This requires you to choose upper limits for the uniform priors on both
#' the top-level shrinkage hyperparameter as well as the local shrinkage parameters. These can be tuned through
#' model comparison if neccessary.
#' \cr
#' \cr
#' Model Specification:
#' \cr
#' \if{html}{\figure{extLASSODC.png}{}}
#' \if{latex}{\figure{extLASSODC.png}{}}
#' \cr
#' \cr
#' The second version is the "gamma" prior. This places a gamma(0.50 , 0.20) prior on the
#' top level shrinkage hyperparameter. The individual shrinkage parameters are still given independent uniform(0, local_u)
#' priors just as in the classic version.
#' \cr
#' \cr
#' Model Specification:
#' \cr
#' \if{html}{\figure{extLASSO2DC.png}{}}
#' \if{latex}{\figure{extLASSO2DC.png}{}}
#' \cr
#' \cr
#' The author of the extended Bayesian Lasso (Sillanpää, personal communication) confirmed that gamma prior on the top
#' level shrinkage parameter
#' does work well in some settings, which is why I opted to include the "gamma" variant.
#' \cr
#' \cr
#' One really nice feature of the extended Bayesian Lasso is that inclusion probabilities and Bayes Factors for the
#' coefficients are easily obtained. The prior inclusion probability is given by 1/local_u, so for example
#' uniform(0, 2) priors on the shrinkage parameters indicate a 50% prior inclusion
#' probability. Common in Bayesian variable selection is to use a 20% probability if
#' dealing with a high dimensional problem, so for this choose local_u = 5. If you have genuine prior
#' information you can and should use this to guide your choice. If you are unsure, use model comparison
#' to select which value of u to choose. Inclusion indicators are given by a step function based on the
#' marginal individual shrinkage parameter, delta = step(1 - eta). Inlcusion probabilities are then given as the number of
#' 1s that appear in the vector of monte carlo samples out of the total number of iterations. This will appear as
#' the mean for each inclusion indicator in the summary. \cr
#' \cr
#' Bayes Factors for each cofficient can then be manually derived using the formula below (Mutshinda, & Sillanpää, 2010; 2012).
#' \cr
#' \cr
#' \if{html}{\figure{extLASSO_BF.png}{}}
#' \if{latex}{\figure{extLASSO_BF.png}{}}
#' \cr
#' \cr
#' @references
#'
#' Li, Z.,and Sillanpää, M. J. (2012) Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection. Theoretical and Applied Genetics 125: 419-435. \cr
#' \cr
#' Mutshinda, C.M., & Sillanpää, M.J. (2010). Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction. Genetics, 186 3, 1067-75 . \cr
#' \cr
#' Mutshinda, C. M., and M. J. Sillanpää (2012) A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model. Genetics 192: 1483-1491. \cr
#' \cr
#'
#' @param formula the model formula
#' @param design.formula formula for the design covariates.
#' @param data a data frame.
#' @param family one of "gaussian", "binomial", or "poisson".
#' @param eta_prior one of "classic (default)", or "gamma".
#' @param local_u This must be assigned a value. Default is 2.
#' @param top_u If using eta_prior = "classic" this must be assigned a value. Default is 25.
#' @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 10000.
#' @param adapt How many adaptation steps? Defaults to 15000.
#' @param chains How many chains? Defaults to 4.
#' @param thin Thinning interval. Defaults to 3.
#' @param method Defaults to "parallel". For an alternative parallel option, choose "rjparallel" 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.
#'
#' @return A run.jags object
#' @export
#'
#'
#' @examples
#' extLASSODC()
#'
extLASSODC = function(formula, design.formula, data, family = "gaussian", eta_prior = "classic", local_u = 2, top_u = 25, log_lik = FALSE, iter=10000, warmup = 10000, adapt=15000, chains=4, thin = 3, method = "parallel", cl = makeCluster(2), ...){
FX <- as.matrix(model.matrix(design.formula, data)[, -1])
X = model.matrix(formula, data)[,-1]
y = model.frame(formula, data)[,1]
if (family == "gaussian" || family == "normal") {
if (eta_prior == "gamma"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
jags_extended_LASSO = "model{
# Precision
tau ~ dgamma(.01, .01)
# Shrinkage top-level-hyperparameter
Omega ~ dgamma(0.50 , 0.20)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
y[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
log_lik[i] <- logdensity.norm(y[i], Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
ySim[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
}
sigma <- sqrt(1/tau)
Deviance <- -2 * sum(log_lik[1:N])
}"
P = ncol(X)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "sigma", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u)
inits = lapply(1:chains, function(z) list("Intercept" = lmSolve(design.formula, data)[1],
"design_beta" = lmSolve(design.formula, data)[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = lmSolve(formula, data)[-1],
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
"tau" = 1,
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
if (eta_prior == "classic"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
if (is.na(top_u)) stop("Please select an upper limit for the uniform prior on Omega.")
jags_extended_LASSO = "model{
# Precision
tau ~ dgamma(.01, .01)
# Shrinkage top-level-hyperparameter
Omega ~ dunif(0, top_u)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
y[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
log_lik[i] <- logdensity.norm(y[i], Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
ySim[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
}
sigma <- sqrt(1/tau)
Deviance <- -2 * sum(log_lik[1:N])
}"
P = ncol(X)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "sigma", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u, top_u = top_u)
inits = lapply(1:chains, function(z) list("Intercept" = lmSolve(design.formula, data)[1],
"design_beta" = lmSolve(design.formula, data)[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = lmSolve(formula, data)[-1],
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
"tau" = 1,
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
}
if (family == "binomial" || family == "logistic") {
if (eta_prior == "gamma"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
jags_extended_LASSO = "model{
# Shrinkage top-level-hyperparameter
Omega ~ dgamma(0.50 , 0.20)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
logit(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP])
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)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u)
inits = lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glm(design.formula, data, family = "binomial")))[1],
"design_beta" = as.vector(coef(glm(design.formula, data, family = "binomial")))[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = jitter(rep(0, P), amount = .25),
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
else if (eta_prior == "classic"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
if (is.na(top_u)) stop("Please select an upper limit for the uniform prior on Omega.")
jags_extended_LASSO = "model{
# Shrinkage top-level-hyperparameter
Omega ~ dunif(0, top_u)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
logit(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP])
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)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u, top_u = top_u)
inits = lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glm(design.formula, data, family = "binomial")))[1],
"design_beta" = as.vector(coef(glm(design.formula, data, family = "binomial")))[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = jitter(rep(0, P), amount = .25),
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
}
else if (family == "poisson") {
if (eta_prior == "gamma"){
if (is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
jags_extended_LASSO = "model{
# Shrinkage top-level-hyperparameter
Omega ~ dgamma(0.50 , 0.20)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
log(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP])
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)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u)
inits = lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glm(design.formula, data, family = "poisson")))[1],
"design_beta" = as.vector(coef(glm(design.formula, data, family = "poisson")))[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = jitter(rep(0, P), amount = .25),
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
if (eta_prior == "classic"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
if (is.na(top_u)) stop("Please select an upper limit for the uniform prior on Omega.")
jags_extended_LASSO = "model{
# Shrinkage top-level-hyperparameter
Omega ~ dunif(0, top_u)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
log(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP])
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)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u, top_u = top_u)
inits = lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glm(design.formula, data, family = "poisson")))[1],
"design_beta" = as.vector(coef(glm(design.formula, data, family = "poisson")))[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = rep(0, P),
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
}
out = run.jags(model = "jags_extended_LASSO.txt", modules = c("bugs on", "glm on", "dic off"), monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, n.chains = chains, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_extended_LASSO.txt")
if (is.null(cl) == FALSE){
parallel::stopCluster(cl = cl)
}
return(out)
}
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Defines functions extLASSODC
Documented in extLASSODC
#' Extended Bayesian LASSO with unpenalized design covariates
#'
#' @description This is the extended Bayesian LASSO presented by
#' Crispin M. Mutshinda and Mikko J. Sillanpää (2010) which is an improvement on the Baysian LASSO
#' of Park & Casella (2008). Two variants are provided here. \cr
#' \cr
#' The first version of the model is the original specification in Mutshinda and Sillanpää (2010), labeled
#' "classic" in the options here. This requires you to choose upper limits for the uniform priors on both
#' the top-level shrinkage hyperparameter as well as the local shrinkage parameters. These can be tuned through
#' model comparison if neccessary.
#' \cr
#' \cr
#' Model Specification:
#' \cr
#' \if{html}{\figure{extLASSODC.png}{}}
#' \if{latex}{\figure{extLASSODC.png}{}}
#' \cr
#' \cr
#' The second version is the "gamma" prior. This places a gamma(0.50 , 0.20) prior on the
#' top level shrinkage hyperparameter. The individual shrinkage parameters are still given independent uniform(0, local_u)
#' priors just as in the classic version.
#' \cr
#' \cr
#' Model Specification:
#' \cr
#' \if{html}{\figure{extLASSO2DC.png}{}}
#' \if{latex}{\figure{extLASSO2DC.png}{}}
#' \cr
#' \cr
#' The author of the extended Bayesian Lasso (Sillanpää, personal communication) confirmed that gamma prior on the top
#' level shrinkage parameter
#' does work well in some settings, which is why I opted to include the "gamma" variant.
#' \cr
#' \cr
#' One really nice feature of the extended Bayesian Lasso is that inclusion probabilities and Bayes Factors for the
#' coefficients are easily obtained. The prior inclusion probability is given by 1/local_u, so for example
#' uniform(0, 2) priors on the shrinkage parameters indicate a 50% prior inclusion
#' probability. Common in Bayesian variable selection is to use a 20% probability if
#' dealing with a high dimensional problem, so for this choose local_u = 5. If you have genuine prior
#' information you can and should use this to guide your choice. If you are unsure, use model comparison
#' to select which value of u to choose. Inclusion indicators are given by a step function based on the
#' marginal individual shrinkage parameter, delta = step(1 - eta). Inlcusion probabilities are then given as the number of
#' 1s that appear in the vector of monte carlo samples out of the total number of iterations. This will appear as
#' the mean for each inclusion indicator in the summary. \cr
#' \cr
#' Bayes Factors for each cofficient can then be manually derived using the formula below (Mutshinda, & Sillanpää, 2010; 2012).
#' \cr
#' \cr
#' \if{html}{\figure{extLASSO_BF.png}{}}
#' \if{latex}{\figure{extLASSO_BF.png}{}}
#' \cr
#' \cr
#' @references
#'
#' Li, Z.,and Sillanpää, M. J. (2012) Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection. Theoretical and Applied Genetics 125: 419-435. \cr
#' \cr
#' Mutshinda, C.M., & Sillanpää, M.J. (2010). Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction. Genetics, 186 3, 1067-75 . \cr
#' \cr
#' Mutshinda, C. M., and M. J. Sillanpää (2012) A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model. Genetics 192: 1483-1491. \cr
#' \cr
#'
#' @param formula the model formula
#' @param design.formula formula for the design covariates.
#' @param data a data frame.
#' @param family one of "gaussian", "binomial", or "poisson".
#' @param eta_prior one of "classic (default)", or "gamma".
#' @param local_u This must be assigned a value. Default is 2.
#' @param top_u If using eta_prior = "classic" this must be assigned a value. Default is 25.
#' @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 10000.
#' @param adapt How many adaptation steps? Defaults to 15000.
#' @param chains How many chains? Defaults to 4.
#' @param thin Thinning interval. Defaults to 3.
#' @param method Defaults to "parallel". For an alternative parallel option, choose "rjparallel" 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.
#'
#' @return A run.jags object
#' @export
#'
#'
#' @examples
#' extLASSODC()
#'
extLASSODC = function(formula, design.formula, data, family = "gaussian", eta_prior = "classic", local_u = 2, top_u = 25, log_lik = FALSE, iter=10000, warmup = 10000, adapt=15000, chains=4, thin = 3, method = "parallel", cl = makeCluster(2), ...){
FX <- as.matrix(model.matrix(design.formula, data)[, -1])
X = model.matrix(formula, data)[,-1]
y = model.frame(formula, data)[,1]
if (family == "gaussian" || family == "normal") {
if (eta_prior == "gamma"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
jags_extended_LASSO = "model{
# Precision
tau ~ dgamma(.01, .01)
# Shrinkage top-level-hyperparameter
Omega ~ dgamma(0.50 , 0.20)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
y[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
log_lik[i] <- logdensity.norm(y[i], Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
ySim[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
}
sigma <- sqrt(1/tau)
Deviance <- -2 * sum(log_lik[1:N])
}"
P = ncol(X)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "sigma", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u)
inits = lapply(1:chains, function(z) list("Intercept" = lmSolve(design.formula, data)[1],
"design_beta" = lmSolve(design.formula, data)[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = lmSolve(formula, data)[-1],
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
"tau" = 1,
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
if (eta_prior == "classic"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
if (is.na(top_u)) stop("Please select an upper limit for the uniform prior on Omega.")
jags_extended_LASSO = "model{
# Precision
tau ~ dgamma(.01, .01)
# Shrinkage top-level-hyperparameter
Omega ~ dunif(0, top_u)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
y[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
log_lik[i] <- logdensity.norm(y[i], Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
ySim[i] ~ dnorm(Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP]), tau)
}
sigma <- sqrt(1/tau)
Deviance <- -2 * sum(log_lik[1:N])
}"
P = ncol(X)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "sigma", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u, top_u = top_u)
inits = lapply(1:chains, function(z) list("Intercept" = lmSolve(design.formula, data)[1],
"design_beta" = lmSolve(design.formula, data)[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = lmSolve(formula, data)[-1],
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
"tau" = 1,
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
}
if (family == "binomial" || family == "logistic") {
if (eta_prior == "gamma"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
jags_extended_LASSO = "model{
# Shrinkage top-level-hyperparameter
Omega ~ dgamma(0.50 , 0.20)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
logit(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP])
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)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u)
inits = lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glm(design.formula, data, family = "binomial")))[1],
"design_beta" = as.vector(coef(glm(design.formula, data, family = "binomial")))[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = jitter(rep(0, P), amount = .25),
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
else if (eta_prior == "classic"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
if (is.na(top_u)) stop("Please select an upper limit for the uniform prior on Omega.")
jags_extended_LASSO = "model{
# Shrinkage top-level-hyperparameter
Omega ~ dunif(0, top_u)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
logit(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP])
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)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u, top_u = top_u)
inits = lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glm(design.formula, data, family = "binomial")))[1],
"design_beta" = as.vector(coef(glm(design.formula, data, family = "binomial")))[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = jitter(rep(0, P), amount = .25),
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
.RNG.name= "lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
}
else if (family == "poisson") {
if (eta_prior == "gamma"){
if (is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
jags_extended_LASSO = "model{
# Shrinkage top-level-hyperparameter
Omega ~ dgamma(0.50 , 0.20)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
log(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP])
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)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u)
inits = lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glm(design.formula, data, family = "poisson")))[1],
"design_beta" = as.vector(coef(glm(design.formula, data, family = "poisson")))[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = jitter(rep(0, P), amount = .25),
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
if (eta_prior == "classic"){
if(is.na(local_u)) stop("Please select an upper limit for the uniform prior on eta.")
if (is.na(top_u)) stop("Please select an upper limit for the uniform prior on Omega.")
jags_extended_LASSO = "model{
# Shrinkage top-level-hyperparameter
Omega ~ dunif(0, top_u)
Intercept ~ dnorm(0, 1e-10)
for (p in 1:P){
# Individual Level shrinkage hyperparameter
eta[p] ~ dunif(0, local_u)
lambda[p] <- Omega * eta[p]
# Beta Precision
w[p]<- pow(lambda[p],2)/2
beta_var[p] ~ dexp(w[p])
beta_prec[p] <- 1 / beta_var[p]
# Coefficient
beta[p] ~ dnorm(0, beta_prec[p])
# Indicator Function
delta[p] <- step(1-eta[p])
}
# Design Variable Coefficients
for (f in 1:FP){
design_beta[f] ~ dnorm(0, 1e-200)
}
for (i in 1:N){
log(psi[i]) <- Intercept + sum(beta[1:P] * X[i,1:P]) + sum(design_beta[1:FP] * FX[i,1:FP])
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)
FP <- ncol(FX)
monitor = c("Intercept", "beta", "design_beta", "Omega", "Deviance", "lambda", "delta", "ySim" ,"log_lik")
if (log_lik == FALSE){
monitor = monitor[-(length(monitor))]
}
jagsdata = list(X = X, y = y, N = length(y), P = ncol(X), FP = FP, FX = FX, local_u = local_u, top_u = top_u)
inits = lapply(1:chains, function(z) list("Intercept" = as.vector(coef(glm(design.formula, data, family = "poisson")))[1],
"design_beta" = as.vector(coef(glm(design.formula, data, family = "poisson")))[-1],
"ySim" = sample(y, length(y)),
"Omega" = 2,
"beta" = rep(0, P),
"eta" = rep(1, P),
"beta_var" = abs(jitter(rep(.5, P), amount = .25)),
.RNG.name="lecuyer::RngStream",
.RNG.seed= sample(1:10000, 1)))
write_lines(jags_extended_LASSO, "jags_extended_LASSO.txt")
}
}
out = run.jags(model = "jags_extended_LASSO.txt", modules = c("bugs on", "glm on", "dic off"), monitor = monitor, data = jagsdata, inits = inits, burnin = warmup, sample = iter, thin = thin, adapt = adapt, n.chains = chains, method = method, cl = cl, summarise = FALSE, ...)
file.remove("jags_extended_LASSO.txt")
if (is.null(cl) == FALSE){
parallel::stopCluster(cl = cl)
}
return(out)
}
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