R/bhlm.R
In arcuo/bachelors_meta: Bayesian Hierarchical Latent Mixture model meta analysis and plots.
Defines functions
bhlm
Documented in
bhlm
#' @include bhlm_classes.R
#' @include bhlm_helpers.R
#'
#' @import tidyverse
#' @import plyr
#' @import R2jags
NULL
#'@title Bayesian Hierarchical Latent Mixture Model
#'@description Run a BHLM model with custom number of latent variables according to specified number of outcome options.
#'The model sets priors for each outcome, group mean (theta) and estimate variance.
#'
#'Run following for model specification (Graphical notation):
#'
#' \code{plot(0:1,0:1,type="n",ann=F,axes=F)}
#' \code{rasterImage(bhlm_model_spec,0,0,1,1)}
#'
#' @author Hugh Benjamin Zachariae
#' @export
#' @param dataframe A data frame with studies, two grouping factor cols (e.g. studies*outcomes),
#' estimates, and optional ID column.
#' @param grouping_factor_cols A data vector of two grouping factor columns of the data frame
#' (e.g. \code{c("Study", "Outcome")}). These columns must be integer vectors.
#' @param estimate_col Column of estimates in the data frame.
#' @param outcome_options_col Column containing the different outcome names
#'
#' \code{Outcomes : Factor w/ 3 levels "Physical","Psychological",..: 2 2 2 2 2 2 2 2 1 1 ...}
#'
#' @param outcome_options Chosen outcomes. You can choose a subset of outcomes.
#' @param outcome_priors Priors for outcomes. Takes character vector or \code{matrix}.
#'
#' Define all outcome priors to one distribution set prior (throws Warning) as \code{character}:
#'
#' \code{"dnorm(0, 1)"}
#'
#' \emph{Priors defined as character strings are checked with regex:}
#'
#' \code{grepl("^[a-zA-Z]+\\(((\\d*\\.)*\\d+,\\s?)*(\\d*\\.)*\\d+\\)", .)}
#'
#' or as data vector
#'
#' \code{c("dnorm", 0, 1)}
#'
#' Define all outcome priors manually with \code{matrix}. \strong{Notice}: byrow = T
#'
#' \code{matrix(c("dnorm", 0, 1, "dnorm", 0, 1.1),
#' nrow = 'number of outcomes',
#' ncol = 'maximum amount of dist parameters',
#' byrow = T)}
#'
#' or as character vector of \code{characters}
#'
#' \code{c("dnorm(0,1)", "dnorm(0,1.1)")}
#'
#' \strong{Notice}: priors set manually, are set in the same order as \code{outcome_options}
#' in character vector or in matrix row order (top to bottom).
#'
#' @param lambda_prior Prior for Lambda as \code{character} or data vector.
#' @param theta_prior Prior for Theta as \code{character} or data vector.
#' @param field_theta Optional: Include a another hierarchical level in the form of a field-wide mean distribution.
#' Use a string vector with the same distribution as \code{theta_prior} and precision (no mean). The field-wide mean prior,
#' is set to \code{theta_prior}. This solution is currently a fix-up, and should only be used on a case by case basis.
#' @param identifier_col ID column. Chosen column is added to the used data for easier overview.
#' @param bayes_method Use JAGS or STAN (currently only JAGS is implemented).
#' @param jags Parameters for \code{R2jags::\link[R2jags]{jags}}:
#'
#' \code{init} (\code{NULL} set as default), \code{chains},
#' \code{iter} (iterations per chain), \code{burning} (length of burn), \code{thin} (sim thinning rate), and
#' \code{DIC} (compute deviance, pD and DIC).
#' @param save_model Input F to not save the model file,
#' NULL to save as 'model_file.txt' in working directory, or
#' filepath location with name to save the model file as .txt (Separated with '\\' or '/').
#'
#' @examples
#'
#' # INCOMING
#'
#'
bhlm <- function(dataframe,
grouping_factor_cols,
estimate_col,
outcome_options_col,
outcome_options,
outcome_priors,
lambda_prior,
theta_prior,
field_theta_precision = NULL,
identifier_col = NULL,
bayes_method = "jags",
jags_init = NULL, jags_chains=3, jags_iter = 2000,
jags_burnin = floor(jags_iter/2),
jags_thin = max(1, floor(jags_chains * (jags_iter-jags_burnin) / 1000)),
jags_DIC = T,
save_model = NULL
) {
if (length(outcome_options) <= 1) {
stop(paste("Not enough outcomes, found ", length(outcome_options),
", required at least two.", sep = ""), call. = F)
}
if (!all(outcome_options %in% levels(dataframe[,outcome_options_col]))) {
stop("One or more outcome names are not in outcome_option_col0", call. = F)
}
if(bayes_method == "jags") {
tryCatch({
data <- bhlm.preprocessing(dataframe,
grouping_factor_cols,
estimate_col,
outcome_options_col,
outcome_options,
identifier_col = identifier_col)
}, error = function(e) {
stop(paste("Preprocessing:", e), call. = F)
})
# Create objects needed for model and jags ----------------------------------------------
outcomes = as.vector(data@used_data$outcomes)
outcomes_numeric = as.vector(data@used_data$outcomes_numeric)
start_bounds = data@start_bounds
upper_group = length(start_bounds) - 1
jags_data <- c("outcomes", "outcomes_numeric", "start_bounds", "upper_group")
result_parameters <- c(c("lambda", "theta"), outcome_options)
# Convert outcome priors ------------------------------------------------------
dist_check <- "^[a-zA-Z]+\\(((\\d*\\.)*\\d+,\\s?)*(\\d*\\.)*\\d+\\)"
outcome_priors_c <- "NULL"
outcome_priors_m <- as.matrix("NULL")
# Matrix (all outcome prior defined by list c("dist", n, n))
if (class(outcome_priors) == "matrix") {
if (length(outcome_priors[,1]) != length(outcome_options)) {
stop(paste("Wrong number of priors, found ",
length(outcome_priors[,1]),
", requires ", length(outcome_options),
" priors defined.\n For full outcome prior defined, use: \n",
" matrix(outcome_priors, nrow = length(outcome_options), ",
"ncol = 'distribution + max(parameters)', byrow = T)",
sep = ""),
call. = F)
} else {
outcome_priors_new <- outcome_priors
outcome_priors_m <- cbind(outcome_options, outcome_priors_new)
}
# c(...) or single string/character
} else if (class(outcome_priors) == "character" || class(outcome_priors) == "string" ) {
# If c(...)
if (length(outcome_priors) > 1) {
# If c("dist", n, n)
if (!grepl("[^0-9]", outcome_priors[2])) {
warning(paste("Only one outcome prior defined as vector, c(",
paste(outcome_priors, collapse = ", "),
"). All outcomes are set to have this prior.",
sep = "")
)
outcome_priors_new <- matrix(outcome_priors,
length(outcome_options),
ncol = 3, byrow = T)
outcome_priors_m <- cbind(outcome_options, outcome_priors_new)
# Else c("dnorm(0, 1)", ...) (all outcome priors defined)
} else {
# Check that all prior distributions are well defined
if (!all(grepl(dist_check, outcome_priors))) {
stop(paste("Error in one of the defined outcome prior distributions.\n ",
paste(outcome_priors, collapse = "\n"),
sep = ""), call. = F)
}
if (length(outcome_priors) != length(outcome_options)) {
stop(paste("You have defined more than one outcome prior, but not all. Found ",
length(outcome_priors), " priors defined, requires ",
length(outcome_options), ".", sep = ""))
} else {
outcome_priors_new <- outcome_priors
outcome_priors_c <- paste(outcome_options, outcome_priors_new, sep = "#")
}
}
# Else only one string (all outcomes get same prior.)
} else {
if (!grepl(dist_check, outcome_priors)) {
stop(paste("Error in the defined outcome prior distribution.\n",
outcome_priors,
sep = ""), call. = F)
} else {
warning(paste("Only one outcome prior defined as string, '",
outcome_priors,
"'. All outcomes are set to have this prior.",
sep = ""))
outcome_priors_new <- rep(outcome_priors, length(outcome_options))
outcome_priors_c <- paste(outcome_options, outcome_priors_new, sep = "#")
}
}
} else {
stop(paste("Unknown outcome priors setup, found ",
class(outcome_priors),
".\n oucome_priors has following setups: \n ",
"One prior for all",
sep = ""), call. = F)
}
# Create model file -----------------------------------------------------------
tryCatch({
modelfilepath = bhlm.write.model(
bhlm.create.model.list(outcomes_list = outcome_options,
theta_prior = theta_prior,
lambda_prior = lambda_prior,
outcome_priors = outcome_priors_new,
field_theta = field_theta_precision),
path = save_model)
}, error = function(e) {
stop(paste("Model creation:", e), call. = F)
})
tryCatch({
samples = R2jags::jags(jags_data, inits = jags_init, result_parameters,
model.file = modelfilepath, n.chains=jags_chains, n.iter=jags_iter,
n.burnin=jags_burnin, n.thin=jags_thin, DIC=jags_DIC)
}, error = function(e) {
stop(paste("JAGS model sampling: ",
"\n For Syntax error, consider saving model",
" file by setting a path for 'save_model'.\n ", e,
sep = ""), call. = F)
})
}
return(new("bhlm_object",
used_data = data@used_data,
start_bounds = data@start_bounds,
jags_samples = samples,
outcome_options = outcome_options,
outcome_priors_m = outcome_priors_m,
outcome_priors_c = outcome_priors_c,
estimate_name = estimate_col))
}
#'@title Maximum a posteriori
#'@description Create a dataframe with outcome names, and maximum posteriori for each outcome.
#'You can choose the density estimation method (\code{polspline::logspline} or \code{base::density}).
#'
#' @author Hugh Benjamin Zachariae
#' @param bhlm_object Object returned from \code{bhlm}, of class \code{bhlm_object}.
#' @param outcome_options Character vector specifying which outcomes should be plotted.
#' Defaults to all outcome options from \code{bhlm_object@outcome_options}.
#' @param density.estimate Choose between \code{polspline::logspline} or \code{base::density} for density estimation method.
#' @export
bhlm.MAP <- function (bhlm_object, outcome_options = NULL, density_estimate = "logspline") {
if (class(bhlm_object) != "bhlm_object") {
stop(paste("Not bhlm_object. Found ", class(bhlm_object), ".", sep = ""), call. = F)
}
if (is.null(outcome_options)) {
outcome_options <- bhlm_object@outcome_options
}
data <- bhlm_object@jags_samples$BUGSoutput$sims.list
table <- plyr::ldply(1:length(outcome_options), function(p) {
data.frame('Outcome' = outcome_options[p],
"MAP" = density(data[[outcome_options[p]]])$x[which(density(data[[outcome_options[p]]])$y==max(density(data[[outcome_options[p]]])$y))])
})
if (density_estimate == "logspline") {
table <- plyr::ldply(outcome_options, function(o) {
log.est <- logspline(data[o])
u1 <- qlogspline(0.01, log.est)
u2 <- qlogspline(0.99, log.est)
u3 <- 1.1 * u1 - 0.1 * u2
u4 <- 1.1 * u2 - 0.1 * u1
d <- data.frame(x = (0:(1000 - 1))/(1000 - 1) * (u4 - u3) + u3) %>%
mutate(y = dlogspline(x, log.est))
return(data.frame('Outcome' = o,
'MAP' = d$x[which.max(d$y)]))
})
} else if (density_estimate == "density") {
table <- plyr::ldply(1:length(outcome_options), function(o) {
data.frame('Outcome' = outcome_options[o],
"MAP" = density(data[[outcome_options[o]]])$x[which(density(data[[outcome_options[o]]])$y==max(density(data[[outcome_options[o]]])$y))]) %>%
as_tibble()
})
} else {
stop("incorrect density estimation method", call. = F)
}
return(table)
}
arcuo/bachelors_meta documentation built on May 5, 2019, 2:41 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions bhlm
Documented in bhlm
#' @include bhlm_classes.R
#' @include bhlm_helpers.R
#'
#' @import tidyverse
#' @import plyr
#' @import R2jags
NULL
#'@title Bayesian Hierarchical Latent Mixture Model
#'@description Run a BHLM model with custom number of latent variables according to specified number of outcome options.
#'The model sets priors for each outcome, group mean (theta) and estimate variance.
#'
#'Run following for model specification (Graphical notation):
#'
#' \code{plot(0:1,0:1,type="n",ann=F,axes=F)}
#' \code{rasterImage(bhlm_model_spec,0,0,1,1)}
#'
#' @author Hugh Benjamin Zachariae
#' @export
#' @param dataframe A data frame with studies, two grouping factor cols (e.g. studies*outcomes),
#' estimates, and optional ID column.
#' @param grouping_factor_cols A data vector of two grouping factor columns of the data frame
#' (e.g. \code{c("Study", "Outcome")}). These columns must be integer vectors.
#' @param estimate_col Column of estimates in the data frame.
#' @param outcome_options_col Column containing the different outcome names
#'
#' \code{Outcomes : Factor w/ 3 levels "Physical","Psychological",..: 2 2 2 2 2 2 2 2 1 1 ...}
#'
#' @param outcome_options Chosen outcomes. You can choose a subset of outcomes.
#' @param outcome_priors Priors for outcomes. Takes character vector or \code{matrix}.
#'
#' Define all outcome priors to one distribution set prior (throws Warning) as \code{character}:
#'
#' \code{"dnorm(0, 1)"}
#'
#' \emph{Priors defined as character strings are checked with regex:}
#'
#' \code{grepl("^[a-zA-Z]+\\(((\\d*\\.)*\\d+,\\s?)*(\\d*\\.)*\\d+\\)", .)}
#'
#' or as data vector
#'
#' \code{c("dnorm", 0, 1)}
#'
#' Define all outcome priors manually with \code{matrix}. \strong{Notice}: byrow = T
#'
#' \code{matrix(c("dnorm", 0, 1, "dnorm", 0, 1.1),
#' nrow = 'number of outcomes',
#' ncol = 'maximum amount of dist parameters',
#' byrow = T)}
#'
#' or as character vector of \code{characters}
#'
#' \code{c("dnorm(0,1)", "dnorm(0,1.1)")}
#'
#' \strong{Notice}: priors set manually, are set in the same order as \code{outcome_options}
#' in character vector or in matrix row order (top to bottom).
#'
#' @param lambda_prior Prior for Lambda as \code{character} or data vector.
#' @param theta_prior Prior for Theta as \code{character} or data vector.
#' @param field_theta Optional: Include a another hierarchical level in the form of a field-wide mean distribution.
#' Use a string vector with the same distribution as \code{theta_prior} and precision (no mean). The field-wide mean prior,
#' is set to \code{theta_prior}. This solution is currently a fix-up, and should only be used on a case by case basis.
#' @param identifier_col ID column. Chosen column is added to the used data for easier overview.
#' @param bayes_method Use JAGS or STAN (currently only JAGS is implemented).
#' @param jags Parameters for \code{R2jags::\link[R2jags]{jags}}:
#'
#' \code{init} (\code{NULL} set as default), \code{chains},
#' \code{iter} (iterations per chain), \code{burning} (length of burn), \code{thin} (sim thinning rate), and
#' \code{DIC} (compute deviance, pD and DIC).
#' @param save_model Input F to not save the model file,
#' NULL to save as 'model_file.txt' in working directory, or
#' filepath location with name to save the model file as .txt (Separated with '\\' or '/').
#'
#' @examples
#'
#' # INCOMING
#'
#'
bhlm <- function(dataframe,
grouping_factor_cols,
estimate_col,
outcome_options_col,
outcome_options,
outcome_priors,
lambda_prior,
theta_prior,
field_theta_precision = NULL,
identifier_col = NULL,
bayes_method = "jags",
jags_init = NULL, jags_chains=3, jags_iter = 2000,
jags_burnin = floor(jags_iter/2),
jags_thin = max(1, floor(jags_chains * (jags_iter-jags_burnin) / 1000)),
jags_DIC = T,
save_model = NULL
) {
if (length(outcome_options) <= 1) {
stop(paste("Not enough outcomes, found ", length(outcome_options),
", required at least two.", sep = ""), call. = F)
}
if (!all(outcome_options %in% levels(dataframe[,outcome_options_col]))) {
stop("One or more outcome names are not in outcome_option_col0", call. = F)
}
if(bayes_method == "jags") {
tryCatch({
data <- bhlm.preprocessing(dataframe,
grouping_factor_cols,
estimate_col,
outcome_options_col,
outcome_options,
identifier_col = identifier_col)
}, error = function(e) {
stop(paste("Preprocessing:", e), call. = F)
})
# Create objects needed for model and jags ----------------------------------------------
outcomes = as.vector(data@used_data$outcomes)
outcomes_numeric = as.vector(data@used_data$outcomes_numeric)
start_bounds = data@start_bounds
upper_group = length(start_bounds) - 1
jags_data <- c("outcomes", "outcomes_numeric", "start_bounds", "upper_group")
result_parameters <- c(c("lambda", "theta"), outcome_options)
# Convert outcome priors ------------------------------------------------------
dist_check <- "^[a-zA-Z]+\\(((\\d*\\.)*\\d+,\\s?)*(\\d*\\.)*\\d+\\)"
outcome_priors_c <- "NULL"
outcome_priors_m <- as.matrix("NULL")
# Matrix (all outcome prior defined by list c("dist", n, n))
if (class(outcome_priors) == "matrix") {
if (length(outcome_priors[,1]) != length(outcome_options)) {
stop(paste("Wrong number of priors, found ",
length(outcome_priors[,1]),
", requires ", length(outcome_options),
" priors defined.\n For full outcome prior defined, use: \n",
" matrix(outcome_priors, nrow = length(outcome_options), ",
"ncol = 'distribution + max(parameters)', byrow = T)",
sep = ""),
call. = F)
} else {
outcome_priors_new <- outcome_priors
outcome_priors_m <- cbind(outcome_options, outcome_priors_new)
}
# c(...) or single string/character
} else if (class(outcome_priors) == "character" || class(outcome_priors) == "string" ) {
# If c(...)
if (length(outcome_priors) > 1) {
# If c("dist", n, n)
if (!grepl("[^0-9]", outcome_priors[2])) {
warning(paste("Only one outcome prior defined as vector, c(",
paste(outcome_priors, collapse = ", "),
"). All outcomes are set to have this prior.",
sep = "")
)
outcome_priors_new <- matrix(outcome_priors,
length(outcome_options),
ncol = 3, byrow = T)
outcome_priors_m <- cbind(outcome_options, outcome_priors_new)
# Else c("dnorm(0, 1)", ...) (all outcome priors defined)
} else {
# Check that all prior distributions are well defined
if (!all(grepl(dist_check, outcome_priors))) {
stop(paste("Error in one of the defined outcome prior distributions.\n ",
paste(outcome_priors, collapse = "\n"),
sep = ""), call. = F)
}
if (length(outcome_priors) != length(outcome_options)) {
stop(paste("You have defined more than one outcome prior, but not all. Found ",
length(outcome_priors), " priors defined, requires ",
length(outcome_options), ".", sep = ""))
} else {
outcome_priors_new <- outcome_priors
outcome_priors_c <- paste(outcome_options, outcome_priors_new, sep = "#")
}
}
# Else only one string (all outcomes get same prior.)
} else {
if (!grepl(dist_check, outcome_priors)) {
stop(paste("Error in the defined outcome prior distribution.\n",
outcome_priors,
sep = ""), call. = F)
} else {
warning(paste("Only one outcome prior defined as string, '",
outcome_priors,
"'. All outcomes are set to have this prior.",
sep = ""))
outcome_priors_new <- rep(outcome_priors, length(outcome_options))
outcome_priors_c <- paste(outcome_options, outcome_priors_new, sep = "#")
}
}
} else {
stop(paste("Unknown outcome priors setup, found ",
class(outcome_priors),
".\n oucome_priors has following setups: \n ",
"One prior for all",
sep = ""), call. = F)
}
# Create model file -----------------------------------------------------------
tryCatch({
modelfilepath = bhlm.write.model(
bhlm.create.model.list(outcomes_list = outcome_options,
theta_prior = theta_prior,
lambda_prior = lambda_prior,
outcome_priors = outcome_priors_new,
field_theta = field_theta_precision),
path = save_model)
}, error = function(e) {
stop(paste("Model creation:", e), call. = F)
})
tryCatch({
samples = R2jags::jags(jags_data, inits = jags_init, result_parameters,
model.file = modelfilepath, n.chains=jags_chains, n.iter=jags_iter,
n.burnin=jags_burnin, n.thin=jags_thin, DIC=jags_DIC)
}, error = function(e) {
stop(paste("JAGS model sampling: ",
"\n For Syntax error, consider saving model",
" file by setting a path for 'save_model'.\n ", e,
sep = ""), call. = F)
})
}
return(new("bhlm_object",
used_data = data@used_data,
start_bounds = data@start_bounds,
jags_samples = samples,
outcome_options = outcome_options,
outcome_priors_m = outcome_priors_m,
outcome_priors_c = outcome_priors_c,
estimate_name = estimate_col))
}
#'@title Maximum a posteriori
#'@description Create a dataframe with outcome names, and maximum posteriori for each outcome.
#'You can choose the density estimation method (\code{polspline::logspline} or \code{base::density}).
#'
#' @author Hugh Benjamin Zachariae
#' @param bhlm_object Object returned from \code{bhlm}, of class \code{bhlm_object}.
#' @param outcome_options Character vector specifying which outcomes should be plotted.
#' Defaults to all outcome options from \code{bhlm_object@outcome_options}.
#' @param density.estimate Choose between \code{polspline::logspline} or \code{base::density} for density estimation method.
#' @export
bhlm.MAP <- function (bhlm_object, outcome_options = NULL, density_estimate = "logspline") {
if (class(bhlm_object) != "bhlm_object") {
stop(paste("Not bhlm_object. Found ", class(bhlm_object), ".", sep = ""), call. = F)
}
if (is.null(outcome_options)) {
outcome_options <- bhlm_object@outcome_options
}
data <- bhlm_object@jags_samples$BUGSoutput$sims.list
table <- plyr::ldply(1:length(outcome_options), function(p) {
data.frame('Outcome' = outcome_options[p],
"MAP" = density(data[[outcome_options[p]]])$x[which(density(data[[outcome_options[p]]])$y==max(density(data[[outcome_options[p]]])$y))])
})
if (density_estimate == "logspline") {
table <- plyr::ldply(outcome_options, function(o) {
log.est <- logspline(data[o])
u1 <- qlogspline(0.01, log.est)
u2 <- qlogspline(0.99, log.est)
u3 <- 1.1 * u1 - 0.1 * u2
u4 <- 1.1 * u2 - 0.1 * u1
d <- data.frame(x = (0:(1000 - 1))/(1000 - 1) * (u4 - u3) + u3) %>%
mutate(y = dlogspline(x, log.est))
return(data.frame('Outcome' = o,
'MAP' = d$x[which.max(d$y)]))
})
} else if (density_estimate == "density") {
table <- plyr::ldply(1:length(outcome_options), function(o) {
data.frame('Outcome' = outcome_options[o],
"MAP" = density(data[[outcome_options[o]]])$x[which(density(data[[outcome_options[o]]])$y==max(density(data[[outcome_options[o]]])$y))]) %>%
as_tibble()
})
} else {
stop("incorrect density estimation method", call. = F)
}
return(table)
}
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