R/Param_ave_dens_2.R
In zy-wang1/calm: What the Package Does in One 'Title Case' Line
#' average density, or integral of squared desnity function
#'
#' Parameter definition for longitudinal mediation
#' @importFrom R6 R6Class
#' @importFrom uuid UUIDgenerate
#' @importFrom methods is
#' @family Parameters
#' @keywords data
#'
#' @return \code{Param_base} object
#'
#' @format \code{\link{R6Class}} object.
#'
#' @section Constructor:
#' \code{define_param(Param_middle, observed_likelihood, intervention_list, ..., outcome_node)}
#'
#' \describe{
#' \item{\code{observed_likelihood}}{A \code{\link{Likelihood}} corresponding to the observed likelihood
#' }
#' \item{\code{...}}{Not currently used.
#' }
#' }
#'
#' @section Fields:
#' \describe{
#' \item{\code{...}}{... from \code{\link{LF_base}} ...
#' }
#' }
#' @export
Param_ave_dens_2 <- R6Class(
classname = "Param_ave_dens_2",
portable = TRUE,
class = TRUE,
inherit = Param_base,
public = list(
initialize = function(observed_likelihood, targeted_likelihood_1, updater_1, tilde_likelihood, choose_grid = seq(-20, 20, by = 0.01)) {
super$initialize(observed_likelihood, list())
private$.choose_grid <- choose_grid
private$.targeted_likelihood_1 <- targeted_likelihood_1
private$.updater_1 <- updater_1
private$.tilde_likelihood <- tilde_likelihood
# observed_likelihood$get_likelihoods(observed_likelihood$training_task)
},
clever_covariates = function(tmle_task = NULL, fold_number = "full", node = NULL, submodel_type = "EIC") {
if (is.null(tmle_task)) { # calculate for obs data task if not specified
tmle_task <- self$observed_likelihood$training_task
}
temp_node_names <- names(tmle_task$npsem)
# prepare pmf
support_points <- self$choose_grid
new_data <- data.table(one = 0,
outcome = support_points)
new_task <- tmle3_Task$new(new_data, tmle_task$npsem)
tilde_dens <- data.frame(outcome = new_data$outcome,
cont_dens = self$tilde_likelihood$get_likelihood(new_task, "outcome", fold_number))
temp <- c(1)
for (i in 2:nrow(new_data)) {
if (tilde_dens$cont_dens[i] != tilde_dens$cont_dens[i-1]) temp <- c(temp, last(temp)+1) else temp <- c(temp, last(temp))
}
tilde_dens[["group"]] <- temp
ini_dens <- data.frame(outcome = new_data$outcome,
cont_dens = self$observed_likelihood$get_likelihood(new_task, "outcome", fold_number))
temp <- c(1)
for (i in 2:nrow(new_data)) {
if (ini_dens$cont_dens[i] != ini_dens$cont_dens[i-1]) temp <- c(temp, last(temp)+1) else temp <- c(temp, last(temp))
}
ini_dens[["group"]] <- temp
temp <- c(1)
for (i in 2:length(ini_dens$group)) {
if (ini_dens$group[i] != ini_dens$group[i-1] | tilde_dens$group[i] != tilde_dens$group[i-1]) temp <- c(temp, last(temp)+1) else temp <- c(temp, last(temp))
}
tilde_dens$group <- ini_dens$group <- temp
# tilde_pmf <- sapply(tilde_dens$group %>% unique, function(which_group) {
# temp <- which(tilde_dens$group == which_group)
# a1 <- first(temp)
# a2 <- last(temp)
# c(tilde_dens$outcome[a1],
# (tilde_dens$outcome[a2] - tilde_dens$outcome[a1]) *
# tilde_dens$cont_dens[a1],
# tilde_dens$cont_dens[a1]
# )
# }) %>% t %>% as.data.frame
# colnames(tilde_pmf) <- c("outcome", "prob", "dens")
# # est_tilde <- sum(tilde_pmf$prob^2)
# est_tilde <- sum(tilde_pmf$prob * tilde_pmf$dens)
est_tilde <- sum((tilde_dens$cont_dens^2)*(diff(self$choose_grid))[1])
# ini_pmf <- sapply(ini_dens$group %>% unique, function(which_group) {
# temp <- which(ini_dens$group == which_group)
# a1 <- first(temp)
# a2 <- last(temp)
# c(ini_dens$outcome[a1],
# (ini_dens$outcome[a2] - ini_dens$outcome[a1]) *
# ini_dens$cont_dens[a1],
# ini_dens$cont_dens[a1]
# )
# }) %>% t %>% as.data.frame
# colnames(ini_pmf) <- c("outcome", "prob", "dens")
# # est_1 <- sum(ini_pmf$prob^2)
# est_1 <- sum(ini_pmf$prob * ini_pmf$dens)
est_1 <- sum((ini_dens$cont_dens^2)*(diff(self$choose_grid))[1])
# param_1 <- self$param_1
# if (is.null(param_1)) param_1 <- Param_ave_dens$new(observed_likelihood = self$observed_likelihood, choose_grid = self$choose_grid)
# get cnP
epsilon_1 <- self$updater_1$epsilons %>% unlist %>% sum
dat_int_tilde <- left_join(tilde_dens,
# tilde_pmf,
data.frame(outcome = support_points,
D1P = self$observed_likelihood$get_likelihood(new_task, "outcome", fold_number)*2 - est_1*2
# param_1$clever_covariates(tmle_task = new_task, fold_number = fold_number, node = "outcome")$outcome
,
prob_p = self$observed_likelihood$get_likelihood(tmle_task = new_task, fold_number = fold_number, node = "outcome"))
) %>% mutate(value = (D1P)^2 / (1 + epsilon_1 * D1P)^2) %>% mutate(to_sum = cont_dens*value*(diff(self$choose_grid))[1])
cnP <- sum(dat_int_tilde$to_sum)
# get dP
# param_tilde <- self$param_tilde # the param at the updated likelihood
# if (is.null(param_tilde)) param_tilde <- Param_ave_dens$new(observed_likelihood = self$targeted_likelihood_1, choose_grid = self$choose_grid)
dat_int_P <- ini_dens %>%
# ini_pmf %>%
left_join(
data.frame(outcome = new_data$outcome,
D1P =
self$observed_likelihood$get_likelihood(new_task, "outcome", fold_number)*2 - est_1*2
# param_1$clever_covariates(tmle_task = new_task, fold_number = fold_number, node = "outcome")$outcome
,
D1P1 =
self$targeted_likelihood_1$get_likelihood(new_task, "outcome", fold_number)*2 - est_tilde*2
# param_tilde$clever_covariates(tmle_task = new_task, fold_number = fold_number, node = "outcome")$outcome
,
prob_p_tilde = self$tilde_likelihood$get_likelihood(tmle_task = new_task, fold_number = fold_number, node = "outcome"))
) %>% mutate(to_sum = cont_dens*D1P*D1P1*(diff(self$choose_grid))[1])
dP <- sum(dat_int_P$to_sum) / cnP
D1P1 <-
# param_tilde$clever_covariates(tmle_task = tmle_task, fold_number = fold_number, node = "outcome")$outcome
self$targeted_likelihood_1$get_likelihood(tmle_task, "outcome", fold_number)*2 - est_tilde*2
D1P <-
# param_1$clever_covariates(tmle_task = tmle_task, fold_number = fold_number, node = "outcome")$outcome
self$observed_likelihood$get_likelihood(tmle_task, "outcome", fold_number)*2 - est_1*2
line1 <- D1P1 * (1 + epsilon_1 * D1P)
tilde_p <- self$tilde_likelihood$get_likelihood(tmle_task = tmle_task, node = "outcome", fold_number = fold_number)
p <- self$observed_likelihood$get_likelihood(tmle_task = tmle_task, node = "outcome", fold_number = fold_number)
dat_int_tilde <- dat_int_tilde %>% mutate(denominator = 1 / (1 + epsilon_1 * D1P)^2) %>% mutate(to_sum_denominator = denominator * cont_dens * (diff(self$choose_grid))[1])
tilde_Pn_denominator <- sum(dat_int_tilde$to_sum_denominator)
line2 <- 2 * dP * (tilde_p / (1 + epsilon_1 * D1P)^2 - 2*p*tilde_Pn_denominator)
dat_int_P <- dat_int_P %>% mutate(value_line3 = prob_p_tilde / (1 + epsilon_1 * D1P)^2) %>% mutate(to_sum_line3 = cont_dens * value_line3 * (diff(self$choose_grid))[1])
line3 <- -2 * dP * sum(dat_int_P$to_sum_line3)
line4 <- 4 * dP *
# param_1$estimates(fold_number = fold_number)$psi
est_1 * tilde_Pn_denominator
dat_int_P <- dat_int_P %>% mutate(to_sum_line5 = cont_dens * D1P1 * (diff(self$choose_grid))[1])
line5 <- 2 * epsilon_1 * p * (D1P1 - 2 * sum(dat_int_P$to_sum_line5))
dat_int_P <- dat_int_P %>% mutate(value_line6 = cont_dens * (D1P1 - 2 * sum(dat_int_P$to_sum_line5))) %>% mutate(to_sum_line6 = cont_dens * value_line6 * (diff(self$choose_grid))[1])
line6 <- -2 * epsilon_1 * sum(dat_int_P$to_sum_line6)
list_D <- lapply(temp_node_names, function(name) {
if (length(unique(tmle_task$get_tmle_node(name))) == 1) return(NULL) else {
IC <- line1 + line2 + line3 + line4 + line5 + line6
return(IC)
}
})
names(list_D) <- temp_node_names
if (!is.null(node)) return(list_D[node]) else return(list_D)
},
estimates = function(tmle_task = NULL, fold_number = "full") {
if (is.null(tmle_task)) {
tmle_task <- self$observed_likelihood$training_task
}
obs_data <- tmle_task$data %>% dplyr::select(-c(id, t))
# new_dens <- data.frame(obs_data,
# cont_dens = initial_likelihood$get_likelihood(tmle_task, "outcome")) %>% arrange(outcome)
# descritize;
{
support_points <- self$choose_grid
new_data <- data.table(one = 0,
outcome = support_points)
new_task <- tmle3_Task$new(new_data, npsem)
new_dens <- data.frame(outcome = new_data$outcome,
cont_dens = self$observed_likelihood$get_likelihood(new_task, "outcome", fold_number))
temp <- c(1)
for (i in 2:nrow(new_data)) {
if (new_dens$cont_dens[i] != new_dens$cont_dens[i-1]) temp <- c(temp, last(temp)+1) else temp <- c(temp, last(temp))
}
new_dens[["group"]] <- temp
# new_pmf <- sapply(new_dens$group %>% unique, function(which_group) {
# temp <- which(new_dens$group == which_group)
# a1 <- first(temp)
# a2 <- last(temp)
# c(new_dens$outcome[a1],
# (new_dens$outcome[a2] - new_dens$outcome[a1]) *
# new_dens$cont_dens[a1],
# new_dens$cont_dens[a1]
# )
# }) %>% t
# temp_loc <- last(which(new_pmf[, 2] !=0))
# new_pmf[temp_loc, 2] <- 1-sum(new_pmf[-temp_loc, 2])
# new_pmf[, 2] %>% sum
# psi <- sum(new_pmf[, 2]^2)
# psi <- sum(new_pmf[, 2] * new_pmf[, 3])
psi <- sum((new_dens$cont_dens^2)*(diff(self$choose_grid))[1])
}
# {
# # new_data <- sample_all(tmle_task, initial_likelihood, 10, "one", "outcome")
# psi <- self$observed_likelihood$get_likelihood(tmle_task, "outcome", fold_number) %>% mean
#
# }
IC <- self$clever_covariates(tmle_task = tmle_task, fold_number = fold_number, node = "outcome")$outcome
result <- list(psi = psi, IC = IC)
return(result)
}
),
active = list(
name = function() {
param_form <- sprintf("Expectation of p(O) = Int p(o)^2 do")
return(param_form)
},
update_nodes = function() {
tmle_task <- self$observed_likelihood$training_task
temp_node_names <- names(tmle_task$npsem)
if (length(unique(tmle_task$data[[1]])) == 1) nodes_to_update <- temp_node_names[-1] else nodes_to_update <- temp_node_names
return(nodes_to_update)
},
choose_grid = function() {
return(private$.choose_grid)
},
targeted_likelihood_1 = function() return(private$.targeted_likelihood_1),
updater_1 = function() return(private$.updater_1),
tilde_likelihood = function() return(private$.tilde_likelihood),
param_1 = function() return(private$.param_1),
param_tilde = function() return(private$.param_tilde)
),
private = list(
.type = "ave_dens_2",
.submodel_type_supported = c("EIC"),
.choose_grid = NULL,
.targeted_likelihood_1 = NULL,
.updater_1 = NULL,
.tilde_likelihood = NULL,
.param_1 = NULL,
.param_tilde = NULL
)
)
zy-wang1/calm documentation built on July 30, 2024, 10:51 a.m.
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#' average density, or integral of squared desnity function
#'
#' Parameter definition for longitudinal mediation
#' @importFrom R6 R6Class
#' @importFrom uuid UUIDgenerate
#' @importFrom methods is
#' @family Parameters
#' @keywords data
#'
#' @return \code{Param_base} object
#'
#' @format \code{\link{R6Class}} object.
#'
#' @section Constructor:
#' \code{define_param(Param_middle, observed_likelihood, intervention_list, ..., outcome_node)}
#'
#' \describe{
#' \item{\code{observed_likelihood}}{A \code{\link{Likelihood}} corresponding to the observed likelihood
#' }
#' \item{\code{...}}{Not currently used.
#' }
#' }
#'
#' @section Fields:
#' \describe{
#' \item{\code{...}}{... from \code{\link{LF_base}} ...
#' }
#' }
#' @export
Param_ave_dens_2 <- R6Class(
classname = "Param_ave_dens_2",
portable = TRUE,
class = TRUE,
inherit = Param_base,
public = list(
initialize = function(observed_likelihood, targeted_likelihood_1, updater_1, tilde_likelihood, choose_grid = seq(-20, 20, by = 0.01)) {
super$initialize(observed_likelihood, list())
private$.choose_grid <- choose_grid
private$.targeted_likelihood_1 <- targeted_likelihood_1
private$.updater_1 <- updater_1
private$.tilde_likelihood <- tilde_likelihood
# observed_likelihood$get_likelihoods(observed_likelihood$training_task)
},
clever_covariates = function(tmle_task = NULL, fold_number = "full", node = NULL, submodel_type = "EIC") {
if (is.null(tmle_task)) { # calculate for obs data task if not specified
tmle_task <- self$observed_likelihood$training_task
}
temp_node_names <- names(tmle_task$npsem)
# prepare pmf
support_points <- self$choose_grid
new_data <- data.table(one = 0,
outcome = support_points)
new_task <- tmle3_Task$new(new_data, tmle_task$npsem)
tilde_dens <- data.frame(outcome = new_data$outcome,
cont_dens = self$tilde_likelihood$get_likelihood(new_task, "outcome", fold_number))
temp <- c(1)
for (i in 2:nrow(new_data)) {
if (tilde_dens$cont_dens[i] != tilde_dens$cont_dens[i-1]) temp <- c(temp, last(temp)+1) else temp <- c(temp, last(temp))
}
tilde_dens[["group"]] <- temp
ini_dens <- data.frame(outcome = new_data$outcome,
cont_dens = self$observed_likelihood$get_likelihood(new_task, "outcome", fold_number))
temp <- c(1)
for (i in 2:nrow(new_data)) {
if (ini_dens$cont_dens[i] != ini_dens$cont_dens[i-1]) temp <- c(temp, last(temp)+1) else temp <- c(temp, last(temp))
}
ini_dens[["group"]] <- temp
temp <- c(1)
for (i in 2:length(ini_dens$group)) {
if (ini_dens$group[i] != ini_dens$group[i-1] | tilde_dens$group[i] != tilde_dens$group[i-1]) temp <- c(temp, last(temp)+1) else temp <- c(temp, last(temp))
}
tilde_dens$group <- ini_dens$group <- temp
# tilde_pmf <- sapply(tilde_dens$group %>% unique, function(which_group) {
# temp <- which(tilde_dens$group == which_group)
# a1 <- first(temp)
# a2 <- last(temp)
# c(tilde_dens$outcome[a1],
# (tilde_dens$outcome[a2] - tilde_dens$outcome[a1]) *
# tilde_dens$cont_dens[a1],
# tilde_dens$cont_dens[a1]
# )
# }) %>% t %>% as.data.frame
# colnames(tilde_pmf) <- c("outcome", "prob", "dens")
# # est_tilde <- sum(tilde_pmf$prob^2)
# est_tilde <- sum(tilde_pmf$prob * tilde_pmf$dens)
est_tilde <- sum((tilde_dens$cont_dens^2)*(diff(self$choose_grid))[1])
# ini_pmf <- sapply(ini_dens$group %>% unique, function(which_group) {
# temp <- which(ini_dens$group == which_group)
# a1 <- first(temp)
# a2 <- last(temp)
# c(ini_dens$outcome[a1],
# (ini_dens$outcome[a2] - ini_dens$outcome[a1]) *
# ini_dens$cont_dens[a1],
# ini_dens$cont_dens[a1]
# )
# }) %>% t %>% as.data.frame
# colnames(ini_pmf) <- c("outcome", "prob", "dens")
# # est_1 <- sum(ini_pmf$prob^2)
# est_1 <- sum(ini_pmf$prob * ini_pmf$dens)
est_1 <- sum((ini_dens$cont_dens^2)*(diff(self$choose_grid))[1])
# param_1 <- self$param_1
# if (is.null(param_1)) param_1 <- Param_ave_dens$new(observed_likelihood = self$observed_likelihood, choose_grid = self$choose_grid)
# get cnP
epsilon_1 <- self$updater_1$epsilons %>% unlist %>% sum
dat_int_tilde <- left_join(tilde_dens,
# tilde_pmf,
data.frame(outcome = support_points,
D1P = self$observed_likelihood$get_likelihood(new_task, "outcome", fold_number)*2 - est_1*2
# param_1$clever_covariates(tmle_task = new_task, fold_number = fold_number, node = "outcome")$outcome
,
prob_p = self$observed_likelihood$get_likelihood(tmle_task = new_task, fold_number = fold_number, node = "outcome"))
) %>% mutate(value = (D1P)^2 / (1 + epsilon_1 * D1P)^2) %>% mutate(to_sum = cont_dens*value*(diff(self$choose_grid))[1])
cnP <- sum(dat_int_tilde$to_sum)
# get dP
# param_tilde <- self$param_tilde # the param at the updated likelihood
# if (is.null(param_tilde)) param_tilde <- Param_ave_dens$new(observed_likelihood = self$targeted_likelihood_1, choose_grid = self$choose_grid)
dat_int_P <- ini_dens %>%
# ini_pmf %>%
left_join(
data.frame(outcome = new_data$outcome,
D1P =
self$observed_likelihood$get_likelihood(new_task, "outcome", fold_number)*2 - est_1*2
# param_1$clever_covariates(tmle_task = new_task, fold_number = fold_number, node = "outcome")$outcome
,
D1P1 =
self$targeted_likelihood_1$get_likelihood(new_task, "outcome", fold_number)*2 - est_tilde*2
# param_tilde$clever_covariates(tmle_task = new_task, fold_number = fold_number, node = "outcome")$outcome
,
prob_p_tilde = self$tilde_likelihood$get_likelihood(tmle_task = new_task, fold_number = fold_number, node = "outcome"))
) %>% mutate(to_sum = cont_dens*D1P*D1P1*(diff(self$choose_grid))[1])
dP <- sum(dat_int_P$to_sum) / cnP
D1P1 <-
# param_tilde$clever_covariates(tmle_task = tmle_task, fold_number = fold_number, node = "outcome")$outcome
self$targeted_likelihood_1$get_likelihood(tmle_task, "outcome", fold_number)*2 - est_tilde*2
D1P <-
# param_1$clever_covariates(tmle_task = tmle_task, fold_number = fold_number, node = "outcome")$outcome
self$observed_likelihood$get_likelihood(tmle_task, "outcome", fold_number)*2 - est_1*2
line1 <- D1P1 * (1 + epsilon_1 * D1P)
tilde_p <- self$tilde_likelihood$get_likelihood(tmle_task = tmle_task, node = "outcome", fold_number = fold_number)
p <- self$observed_likelihood$get_likelihood(tmle_task = tmle_task, node = "outcome", fold_number = fold_number)
dat_int_tilde <- dat_int_tilde %>% mutate(denominator = 1 / (1 + epsilon_1 * D1P)^2) %>% mutate(to_sum_denominator = denominator * cont_dens * (diff(self$choose_grid))[1])
tilde_Pn_denominator <- sum(dat_int_tilde$to_sum_denominator)
line2 <- 2 * dP * (tilde_p / (1 + epsilon_1 * D1P)^2 - 2*p*tilde_Pn_denominator)
dat_int_P <- dat_int_P %>% mutate(value_line3 = prob_p_tilde / (1 + epsilon_1 * D1P)^2) %>% mutate(to_sum_line3 = cont_dens * value_line3 * (diff(self$choose_grid))[1])
line3 <- -2 * dP * sum(dat_int_P$to_sum_line3)
line4 <- 4 * dP *
# param_1$estimates(fold_number = fold_number)$psi
est_1 * tilde_Pn_denominator
dat_int_P <- dat_int_P %>% mutate(to_sum_line5 = cont_dens * D1P1 * (diff(self$choose_grid))[1])
line5 <- 2 * epsilon_1 * p * (D1P1 - 2 * sum(dat_int_P$to_sum_line5))
dat_int_P <- dat_int_P %>% mutate(value_line6 = cont_dens * (D1P1 - 2 * sum(dat_int_P$to_sum_line5))) %>% mutate(to_sum_line6 = cont_dens * value_line6 * (diff(self$choose_grid))[1])
line6 <- -2 * epsilon_1 * sum(dat_int_P$to_sum_line6)
list_D <- lapply(temp_node_names, function(name) {
if (length(unique(tmle_task$get_tmle_node(name))) == 1) return(NULL) else {
IC <- line1 + line2 + line3 + line4 + line5 + line6
return(IC)
}
})
names(list_D) <- temp_node_names
if (!is.null(node)) return(list_D[node]) else return(list_D)
},
estimates = function(tmle_task = NULL, fold_number = "full") {
if (is.null(tmle_task)) {
tmle_task <- self$observed_likelihood$training_task
}
obs_data <- tmle_task$data %>% dplyr::select(-c(id, t))
# new_dens <- data.frame(obs_data,
# cont_dens = initial_likelihood$get_likelihood(tmle_task, "outcome")) %>% arrange(outcome)
# descritize;
{
support_points <- self$choose_grid
new_data <- data.table(one = 0,
outcome = support_points)
new_task <- tmle3_Task$new(new_data, npsem)
new_dens <- data.frame(outcome = new_data$outcome,
cont_dens = self$observed_likelihood$get_likelihood(new_task, "outcome", fold_number))
temp <- c(1)
for (i in 2:nrow(new_data)) {
if (new_dens$cont_dens[i] != new_dens$cont_dens[i-1]) temp <- c(temp, last(temp)+1) else temp <- c(temp, last(temp))
}
new_dens[["group"]] <- temp
# new_pmf <- sapply(new_dens$group %>% unique, function(which_group) {
# temp <- which(new_dens$group == which_group)
# a1 <- first(temp)
# a2 <- last(temp)
# c(new_dens$outcome[a1],
# (new_dens$outcome[a2] - new_dens$outcome[a1]) *
# new_dens$cont_dens[a1],
# new_dens$cont_dens[a1]
# )
# }) %>% t
# temp_loc <- last(which(new_pmf[, 2] !=0))
# new_pmf[temp_loc, 2] <- 1-sum(new_pmf[-temp_loc, 2])
# new_pmf[, 2] %>% sum
# psi <- sum(new_pmf[, 2]^2)
# psi <- sum(new_pmf[, 2] * new_pmf[, 3])
psi <- sum((new_dens$cont_dens^2)*(diff(self$choose_grid))[1])
}
# {
# # new_data <- sample_all(tmle_task, initial_likelihood, 10, "one", "outcome")
# psi <- self$observed_likelihood$get_likelihood(tmle_task, "outcome", fold_number) %>% mean
#
# }
IC <- self$clever_covariates(tmle_task = tmle_task, fold_number = fold_number, node = "outcome")$outcome
result <- list(psi = psi, IC = IC)
return(result)
}
),
active = list(
name = function() {
param_form <- sprintf("Expectation of p(O) = Int p(o)^2 do")
return(param_form)
},
update_nodes = function() {
tmle_task <- self$observed_likelihood$training_task
temp_node_names <- names(tmle_task$npsem)
if (length(unique(tmle_task$data[[1]])) == 1) nodes_to_update <- temp_node_names[-1] else nodes_to_update <- temp_node_names
return(nodes_to_update)
},
choose_grid = function() {
return(private$.choose_grid)
},
targeted_likelihood_1 = function() return(private$.targeted_likelihood_1),
updater_1 = function() return(private$.updater_1),
tilde_likelihood = function() return(private$.tilde_likelihood),
param_1 = function() return(private$.param_1),
param_tilde = function() return(private$.param_tilde)
),
private = list(
.type = "ave_dens_2",
.submodel_type_supported = c("EIC"),
.choose_grid = NULL,
.targeted_likelihood_1 = NULL,
.updater_1 = NULL,
.tilde_likelihood = NULL,
.param_1 = NULL,
.param_tilde = NULL
)
)
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