R/params.R
In statnet/EpiModelHIVhet: EpiModel for HIV-1 Infection among Heterosexual Couples in Sub-Saharan Africa
Defines functions
param_het
init_het
control_het
Documented in
control_het
init_het
param_het
#' @title Parameters for Stochastic Network Model of HIV-1 Infection in
#' Sub-Saharan Africa
#'
#' @description Sets the simulation parameters for the stochastic
#' network model of HIV-1 Infection among Heterosexuals in
#' Sub-Saharan Africa for the \code{EpiModelHIVhet} package.
#'
#' @param time.unit Unit of time relative to one day.
#'
#' @param acute.stage.mult Acute stage multiplier for increased infectiousness
#' above impact of heightened viral load.
#' @param aids.stage.mult AIDS stage multiplier for increased infectiousness in
#' AIDS above impact of heightened viral load.
#'
#' @param vl.acute.topeak Time in days to peak viremia during acute infection.
#' @param vl.acute.toset Time in days to viral set point following peak viremia.
#' @param vl.acute.peak Log 10 viral load at acute peak.
#' @param vl.setpoint Log 10 viral load at set point.
#' @param vl.aidsmax Maximum log 10 viral load during AIDS.
#'
#' @param cond.prob Probability of condoms per act with partners.
#' @param cond.eff Efficacy of condoms per act in HIV prevention.
#'
#' @param act.rate.early Daily per-partnership act rate in early disease.
#' @param act.rate.late Daily per-partnership act rate in late disease.
#' @param act.rate.cd4 CD4 count at which the \code{act.rate.late} applies.
#' @param acts.rand If \code{TRUE}, will draw number of total and unprotected
#' acts from a binomial distribution parameterized by the \code{act.rate}.
#'
#' @param circ.prob.birth Proportion of men circumcised at birth.
#' @param circ.eff Efficacy of circumcision per act in HIV prevention.
#'
#' @param tx.elig.cd4 CD4 count at which a person becomes eligible for treatment.
#' @param tx.init.cd4.mean Mean CD4 count at which person presents for care.
#' @param tx.init.cd4.sd SD of CD4 count at which person presents for care.
#' @param tx.adhere.full Proportion of people who start treatment who are fully
#' adherent.
#' @param tx.adhere.part Of the not fully adherent proportion, the percent of time
#' they are on medication.
#' @param tx.vlsupp.time Time in days from treatment initiation to viral suppression.
#' @param tx.vlsupp.level Log 10 viral load level at suppression.
#' @param tx.cd4.recrat.feml Rate of CD4 recovery under treatment for males.
#' @param tx.cd4.recrat.male Rate of CD4 recovery under treatment for females.
#' @param tx.cd4.decrat.feml Rate of CD4 decline under periods of non-adherence
#' for females.
#' @param tx.cd4.decrat.male Rate of CD4 decline under periods of non-adherence
#' for males.
#' @param tx.coverage Proportion of treatment-eligible persons who have initiated
#' treatment.
#' @param tx.prev.eff Proportional amount by which treatment reduces infectivity
#' of infected partner.
#'
#' @param b.rate General entry rate per day for males and females specified.
#' @param b.rate.method Method for assigning birth rates, with options of "totpop"
#' for births as a function of the total population size, "fpop" for births
#' as a function of the female population size, and "stgrowth" for a constant
#' stable growth rate.
#' @param b.propmale Proportion of entries assigned as male. If NULL, then set
#' adaptively based on the proportion at time 1.
#'
#' @param ds.exit.age Age at which the age-specific ds.rate is set to 1, with NA
#' value indicating no censoring.
#' @param ds.rate.mult Simple multiplier for background death rates.
#' @param di.cd4.aids CD4 count at which late-stage AIDS occurs and the risk of
#' mortality is governed by \code{di.cd4.rate}.
#' @param di.cd4.rate Mortality in late-stage AIDS after hitting a nadir CD4 of
#' \code{di.cd4.aids}.
#' @param ... additional arguments to be passed into model.
#'
#' @details This function sets the parameters for the models.
#'
#' @export
#'
param_het <- function(time.unit = 7,
acute.stage.mult = 5,
aids.stage.mult = 1,
vl.acute.topeak = 14,
vl.acute.toset = 107,
vl.acute.peak = 6.7,
vl.setpoint = 4.5,
vl.aidsmax = 7,
cond.prob = 0.09,
cond.eff = 0.78,
act.rate.early = 0.362,
act.rate.late = 0.197,
act.rate.cd4 = 50,
acts.rand = TRUE,
circ.prob.birth = 0.9,
circ.eff = 0.53,
tx.elig.cd4 = 350,
tx.init.cd4.mean = 120,
tx.init.cd4.sd = 40,
tx.adhere.full = 0.76,
tx.adhere.part = 0.50,
tx.vlsupp.time = 365/3,
tx.vlsupp.level = 1.5,
tx.cd4.recrat.feml = 11.6/30,
tx.cd4.recrat.male = 9.75/30,
tx.cd4.decrat.feml = 11.6/30,
tx.cd4.decrat.male = 9.75/30,
tx.coverage = 0.3,
tx.prev.eff = 0.96,
b.rate = 0.03/365,
b.rate.method = "totpop",
b.propmale = NULL,
ds.exit.age = 55,
ds.rate.mult = 1,
di.cd4.aids = 50,
di.cd4.rate = 2/365,
...) {
## Process parameters
p <- list()
formal.args <- formals(sys.function())
formal.args[["..."]] <- NULL
for (arg in names(formal.args)) {
p[arg] <- list(get(arg))
}
dot.args <- list(...)
names.dot.args <- names(dot.args)
if (length(dot.args) > 0) {
for (i in 1:length(dot.args)) {
p[[names.dot.args[i]]] <- dot.args[[i]]
}
}
## trans.rate multiplier
p$trans.rate <- p$trans.rate * p$trans.rate.mult
## Death rate transformations
ltGhana <- EpiModelHIVhet::ltGhana
ds.rates <- ltGhana[ltGhana$year == 2011, ]
ds.rates$mrate <- ds.rates$mrate / 365
if (is.numeric(ds.exit.age)) {
ds.rates$mrate[ds.rates$agStart >= ds.exit.age] <- 1
}
ds.rates$reps <- ds.rates$agEnd - ds.rates$agStart + 1
ds.rates$reps[ds.rates$agStart == 100] <- 1
male <- rep(ds.rates$male, ds.rates$reps)
mrate <- rep(ds.rates$mrate, ds.rates$reps)
mrate <- pmin(1, mrate * ds.rate.mult)
age <- rep(0:100, 2)
ds.rates <- data.frame(male = male, age, mrate = mrate)
ds.rates <- ds.rates[ds.rates$age != 0, ]
p$ds.rates <- ds.rates
## Time unit scaling
if (time.unit > 1) {
## Rates multiplied by time unit
p$act.rate.early <- act.rate.early * time.unit
p$act.rate.late <- act.rate.late * time.unit
p$b.rate <- b.rate * time.unit
p$ds.rates$mrate <- ifelse(p$ds.rates$mrate < 1,
p$ds.rates$mrate * time.unit,
p$ds.rates$mrate)
p$dx.prob.feml <- p$dx.prob.feml * time.unit
p$dx.prob.male <- p$dx.prob.male * time.unit
p$tx.cd4.recrat.feml <- tx.cd4.recrat.feml * time.unit
p$tx.cd4.recrat.male <- tx.cd4.recrat.male * time.unit
p$tx.cd4.decrat.feml <- tx.cd4.decrat.feml * time.unit
p$tx.cd4.decrat.male <- tx.cd4.decrat.male * time.unit
p$di.cd4.rate <- di.cd4.rate * time.unit
## Intervals divided by time unit
p$vl.acute.topeak <- vl.acute.topeak / time.unit
p$vl.acute.toset <- vl.acute.toset / time.unit
p$tx.vlsupp.time <- tx.vlsupp.time / time.unit
}
p$model <- "a2"
class(p) <- "param.net"
return(p)
}
#' @title Initial Conditions for Stochastic Network Model of HIV-1 Infection in
#' Sub-Saharan Africa
#'
#' @description This function sets the initial conditions for the stochastic
#' network models in the \code{epimethods} package.
#'
#' @param i.prev.male Prevalence of initially infected males.
#' @param i.prev.feml Prevalence of initially infected females.
#' @param ages.male initial ages of males in the population.
#' @param ages.feml initial ages of females in the population.
#' @param inf.time.dist Probability distribution for setting time of infection
#' for nodes infected at T1, with options of \code{"geometric"} for randomly
#' distributed on a geometric distribution with a probability of the
#' reciprocal of the average length of infection, \code{"uniform"} for a
#' uniformly distributed time over that same interval, or \code{"allacute"} for
#' placing all infections in the acute stage at the start.
#' @param max.inf.time Maximum infection time in days for infection at initialization,
#' used when \code{inf.time.dist} is \code{"geometric"} or \code{"uniform"}.
#' @param ... additional arguments to be passed into model.
#'
#' @details This function sets the initial conditions for the models.
#'
#' @export
#'
init_het <- function(i.prev.male = 0.05,
i.prev.feml = 0.05,
ages.male = seq(18, 55, 7/365),
ages.feml = seq(18, 55, 7/365),
inf.time.dist = "geometric",
max.inf.time = 5 * 365,
...) {
## Process parameters
p <- list()
formal.args <- formals(sys.function())
formal.args[["..."]] <- NULL
for (arg in names(formal.args)) {
p[arg] <- list(get(arg))
}
dot.args <- list(...)
names.dot.args <- names(dot.args)
if (length(dot.args) > 0) {
for (i in 1:length(dot.args)) {
p[[names.dot.args[i]]] <- dot.args[[i]]
}
}
## Parameter checks
if (!(inf.time.dist %in% c("uniform", "geometric", "allacute"))) {
stop("inf.time.dist must be \"uniform\" or \"geometric\" or \"allacute\" ")
}
class(p) <- "init.net"
return(p)
}
#' @title Control Settings for Stochastic Network Model of HIV-1 Infection in
#' Sub-Saharan Africa
#'
#' @description This function sets the control settings for the stochastic
#' network models in the \code{epimethods} package.
#'
#' @param simno Simulation ID number.
#' @param nsteps Number of time steps to simulate the model over in whatever unit
#' implied by \code{time.unit}.
#' @param start Starting time step for simulation
#' @param nsims Number of simulations.
#' @param ncores Number of parallel cores to use for simulation jobs, if using
#' the \code{EpiModel.hpc} package.
#' @param par.type Parallelization type, either of \code{"single"} for multi-core
#' or \code{"mpi"} for multi-node MPI threads.
#' @param initialize.FUN Module to initialize the model at time 1.
#' @param aging.FUN Module to age active nodes.
#' @param cd4.FUN CD4 progression module.
#' @param vl.FUN HIV viral load progression module.
#' @param dx.FUN HIV diagnosis module.
#' @param tx.FUN HIV treatment module.
#' @param deaths.FUN Module to simulate death or exit.
#' @param births.FUN Module to simulate births or entries.
#' @param resim_nets.FUN Module to resimulate the network at each time step.
#' @param infection.FUN Module to simulate disease infection.
#' @param prev.FUN Module to calculate disease prevalence at each time step,
#' with the default function of \code{\link{prevalence_het}}.
#' @param verbose.FUN Module to print simulation progress to screen, with the
#' default function of \code{\link{verbose_het}}.
#' @param module.order A character vector of module names that lists modules the
#' order in which they should be evaluated within each time step. If
#' \code{NULL}, the modules will be evaluated as follows: first any
#' new modules supplied through \code{...} in the order in which they are
#' listed, then the built-in modules in their order of the function listing.
#' The \code{initialize.FUN} will always be run first and the
#' \code{verbose.FUN} always last.
#' @param save.nwstats Save out network statistics.
#' @param save.other Other list elements of dat to save out.
#' @param verbose If \code{TRUE}, print progress to console.
#' @param verbose.int Interval for printing progress to console.
#' @param skip.check If \code{TRUE}, skips the error check for parameter values,
#' initial conditions, and control settings before running the models.
#' @param ... Additional arguments passed to the function.
#'
#' @details This function sets the parameters for the models.
#'
#' @export
#'
control_het <- function(simno = 1,
nsteps = 100,
start = 1,
nsims = 1,
ncores = 1,
par.type = "single",
initialize.FUN = initialize_het,
aging.FUN = aging_het,
cd4.FUN = cd4_het,
vl.FUN = vl_het,
dx.FUN = dx_het,
tx.FUN = tx_het,
deaths.FUN = deaths_het,
births.FUN = births_het,
resim_nets.FUN = simnet_het,
infection.FUN = infect_het,
prev.FUN = prevalence_het,
verbose.FUN = verbose_het,
module.order = NULL,
save.nwstats = FALSE,
save.other = c("el", "attr"),
verbose = TRUE,
verbose.int = 1,
skip.check = TRUE,
...) {
p <- list()
formal.args <- formals(sys.function())
formal.args[["..."]] <- NULL
for (arg in names(formal.args)) {
p[arg] <- list(get(arg))
}
dot.args <- list(...)
names.dot.args <- names(dot.args)
if (length(dot.args) > 0) {
for (i in 1:length(dot.args)) {
p[[names.dot.args[i]]] <- dot.args[[i]]
}
}
bi.mods <- grep(".FUN", names(formal.args), value = TRUE)
bi.mods <- bi.mods[which(sapply(bi.mods, function(x) !is.null(eval(parse(text = x))),
USE.NAMES = FALSE) == TRUE)]
p$bi.mods <- bi.mods
p$user.mods <- grep(".FUN", names.dot.args, value = TRUE)
p$save.transmat <- FALSE
p$save.network <- FALSE
class(p) <- "control.net"
return(p)
}
statnet/EpiModelHIVhet documentation built on May 30, 2019, 9:47 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 param_het init_het control_het
Documented in control_het init_het param_het
#' @title Parameters for Stochastic Network Model of HIV-1 Infection in
#' Sub-Saharan Africa
#'
#' @description Sets the simulation parameters for the stochastic
#' network model of HIV-1 Infection among Heterosexuals in
#' Sub-Saharan Africa for the \code{EpiModelHIVhet} package.
#'
#' @param time.unit Unit of time relative to one day.
#'
#' @param acute.stage.mult Acute stage multiplier for increased infectiousness
#' above impact of heightened viral load.
#' @param aids.stage.mult AIDS stage multiplier for increased infectiousness in
#' AIDS above impact of heightened viral load.
#'
#' @param vl.acute.topeak Time in days to peak viremia during acute infection.
#' @param vl.acute.toset Time in days to viral set point following peak viremia.
#' @param vl.acute.peak Log 10 viral load at acute peak.
#' @param vl.setpoint Log 10 viral load at set point.
#' @param vl.aidsmax Maximum log 10 viral load during AIDS.
#'
#' @param cond.prob Probability of condoms per act with partners.
#' @param cond.eff Efficacy of condoms per act in HIV prevention.
#'
#' @param act.rate.early Daily per-partnership act rate in early disease.
#' @param act.rate.late Daily per-partnership act rate in late disease.
#' @param act.rate.cd4 CD4 count at which the \code{act.rate.late} applies.
#' @param acts.rand If \code{TRUE}, will draw number of total and unprotected
#' acts from a binomial distribution parameterized by the \code{act.rate}.
#'
#' @param circ.prob.birth Proportion of men circumcised at birth.
#' @param circ.eff Efficacy of circumcision per act in HIV prevention.
#'
#' @param tx.elig.cd4 CD4 count at which a person becomes eligible for treatment.
#' @param tx.init.cd4.mean Mean CD4 count at which person presents for care.
#' @param tx.init.cd4.sd SD of CD4 count at which person presents for care.
#' @param tx.adhere.full Proportion of people who start treatment who are fully
#' adherent.
#' @param tx.adhere.part Of the not fully adherent proportion, the percent of time
#' they are on medication.
#' @param tx.vlsupp.time Time in days from treatment initiation to viral suppression.
#' @param tx.vlsupp.level Log 10 viral load level at suppression.
#' @param tx.cd4.recrat.feml Rate of CD4 recovery under treatment for males.
#' @param tx.cd4.recrat.male Rate of CD4 recovery under treatment for females.
#' @param tx.cd4.decrat.feml Rate of CD4 decline under periods of non-adherence
#' for females.
#' @param tx.cd4.decrat.male Rate of CD4 decline under periods of non-adherence
#' for males.
#' @param tx.coverage Proportion of treatment-eligible persons who have initiated
#' treatment.
#' @param tx.prev.eff Proportional amount by which treatment reduces infectivity
#' of infected partner.
#'
#' @param b.rate General entry rate per day for males and females specified.
#' @param b.rate.method Method for assigning birth rates, with options of "totpop"
#' for births as a function of the total population size, "fpop" for births
#' as a function of the female population size, and "stgrowth" for a constant
#' stable growth rate.
#' @param b.propmale Proportion of entries assigned as male. If NULL, then set
#' adaptively based on the proportion at time 1.
#'
#' @param ds.exit.age Age at which the age-specific ds.rate is set to 1, with NA
#' value indicating no censoring.
#' @param ds.rate.mult Simple multiplier for background death rates.
#' @param di.cd4.aids CD4 count at which late-stage AIDS occurs and the risk of
#' mortality is governed by \code{di.cd4.rate}.
#' @param di.cd4.rate Mortality in late-stage AIDS after hitting a nadir CD4 of
#' \code{di.cd4.aids}.
#' @param ... additional arguments to be passed into model.
#'
#' @details This function sets the parameters for the models.
#'
#' @export
#'
param_het <- function(time.unit = 7,
acute.stage.mult = 5,
aids.stage.mult = 1,
vl.acute.topeak = 14,
vl.acute.toset = 107,
vl.acute.peak = 6.7,
vl.setpoint = 4.5,
vl.aidsmax = 7,
cond.prob = 0.09,
cond.eff = 0.78,
act.rate.early = 0.362,
act.rate.late = 0.197,
act.rate.cd4 = 50,
acts.rand = TRUE,
circ.prob.birth = 0.9,
circ.eff = 0.53,
tx.elig.cd4 = 350,
tx.init.cd4.mean = 120,
tx.init.cd4.sd = 40,
tx.adhere.full = 0.76,
tx.adhere.part = 0.50,
tx.vlsupp.time = 365/3,
tx.vlsupp.level = 1.5,
tx.cd4.recrat.feml = 11.6/30,
tx.cd4.recrat.male = 9.75/30,
tx.cd4.decrat.feml = 11.6/30,
tx.cd4.decrat.male = 9.75/30,
tx.coverage = 0.3,
tx.prev.eff = 0.96,
b.rate = 0.03/365,
b.rate.method = "totpop",
b.propmale = NULL,
ds.exit.age = 55,
ds.rate.mult = 1,
di.cd4.aids = 50,
di.cd4.rate = 2/365,
...) {
## Process parameters
p <- list()
formal.args <- formals(sys.function())
formal.args[["..."]] <- NULL
for (arg in names(formal.args)) {
p[arg] <- list(get(arg))
}
dot.args <- list(...)
names.dot.args <- names(dot.args)
if (length(dot.args) > 0) {
for (i in 1:length(dot.args)) {
p[[names.dot.args[i]]] <- dot.args[[i]]
}
}
## trans.rate multiplier
p$trans.rate <- p$trans.rate * p$trans.rate.mult
## Death rate transformations
ltGhana <- EpiModelHIVhet::ltGhana
ds.rates <- ltGhana[ltGhana$year == 2011, ]
ds.rates$mrate <- ds.rates$mrate / 365
if (is.numeric(ds.exit.age)) {
ds.rates$mrate[ds.rates$agStart >= ds.exit.age] <- 1
}
ds.rates$reps <- ds.rates$agEnd - ds.rates$agStart + 1
ds.rates$reps[ds.rates$agStart == 100] <- 1
male <- rep(ds.rates$male, ds.rates$reps)
mrate <- rep(ds.rates$mrate, ds.rates$reps)
mrate <- pmin(1, mrate * ds.rate.mult)
age <- rep(0:100, 2)
ds.rates <- data.frame(male = male, age, mrate = mrate)
ds.rates <- ds.rates[ds.rates$age != 0, ]
p$ds.rates <- ds.rates
## Time unit scaling
if (time.unit > 1) {
## Rates multiplied by time unit
p$act.rate.early <- act.rate.early * time.unit
p$act.rate.late <- act.rate.late * time.unit
p$b.rate <- b.rate * time.unit
p$ds.rates$mrate <- ifelse(p$ds.rates$mrate < 1,
p$ds.rates$mrate * time.unit,
p$ds.rates$mrate)
p$dx.prob.feml <- p$dx.prob.feml * time.unit
p$dx.prob.male <- p$dx.prob.male * time.unit
p$tx.cd4.recrat.feml <- tx.cd4.recrat.feml * time.unit
p$tx.cd4.recrat.male <- tx.cd4.recrat.male * time.unit
p$tx.cd4.decrat.feml <- tx.cd4.decrat.feml * time.unit
p$tx.cd4.decrat.male <- tx.cd4.decrat.male * time.unit
p$di.cd4.rate <- di.cd4.rate * time.unit
## Intervals divided by time unit
p$vl.acute.topeak <- vl.acute.topeak / time.unit
p$vl.acute.toset <- vl.acute.toset / time.unit
p$tx.vlsupp.time <- tx.vlsupp.time / time.unit
}
p$model <- "a2"
class(p) <- "param.net"
return(p)
}
#' @title Initial Conditions for Stochastic Network Model of HIV-1 Infection in
#' Sub-Saharan Africa
#'
#' @description This function sets the initial conditions for the stochastic
#' network models in the \code{epimethods} package.
#'
#' @param i.prev.male Prevalence of initially infected males.
#' @param i.prev.feml Prevalence of initially infected females.
#' @param ages.male initial ages of males in the population.
#' @param ages.feml initial ages of females in the population.
#' @param inf.time.dist Probability distribution for setting time of infection
#' for nodes infected at T1, with options of \code{"geometric"} for randomly
#' distributed on a geometric distribution with a probability of the
#' reciprocal of the average length of infection, \code{"uniform"} for a
#' uniformly distributed time over that same interval, or \code{"allacute"} for
#' placing all infections in the acute stage at the start.
#' @param max.inf.time Maximum infection time in days for infection at initialization,
#' used when \code{inf.time.dist} is \code{"geometric"} or \code{"uniform"}.
#' @param ... additional arguments to be passed into model.
#'
#' @details This function sets the initial conditions for the models.
#'
#' @export
#'
init_het <- function(i.prev.male = 0.05,
i.prev.feml = 0.05,
ages.male = seq(18, 55, 7/365),
ages.feml = seq(18, 55, 7/365),
inf.time.dist = "geometric",
max.inf.time = 5 * 365,
...) {
## Process parameters
p <- list()
formal.args <- formals(sys.function())
formal.args[["..."]] <- NULL
for (arg in names(formal.args)) {
p[arg] <- list(get(arg))
}
dot.args <- list(...)
names.dot.args <- names(dot.args)
if (length(dot.args) > 0) {
for (i in 1:length(dot.args)) {
p[[names.dot.args[i]]] <- dot.args[[i]]
}
}
## Parameter checks
if (!(inf.time.dist %in% c("uniform", "geometric", "allacute"))) {
stop("inf.time.dist must be \"uniform\" or \"geometric\" or \"allacute\" ")
}
class(p) <- "init.net"
return(p)
}
#' @title Control Settings for Stochastic Network Model of HIV-1 Infection in
#' Sub-Saharan Africa
#'
#' @description This function sets the control settings for the stochastic
#' network models in the \code{epimethods} package.
#'
#' @param simno Simulation ID number.
#' @param nsteps Number of time steps to simulate the model over in whatever unit
#' implied by \code{time.unit}.
#' @param start Starting time step for simulation
#' @param nsims Number of simulations.
#' @param ncores Number of parallel cores to use for simulation jobs, if using
#' the \code{EpiModel.hpc} package.
#' @param par.type Parallelization type, either of \code{"single"} for multi-core
#' or \code{"mpi"} for multi-node MPI threads.
#' @param initialize.FUN Module to initialize the model at time 1.
#' @param aging.FUN Module to age active nodes.
#' @param cd4.FUN CD4 progression module.
#' @param vl.FUN HIV viral load progression module.
#' @param dx.FUN HIV diagnosis module.
#' @param tx.FUN HIV treatment module.
#' @param deaths.FUN Module to simulate death or exit.
#' @param births.FUN Module to simulate births or entries.
#' @param resim_nets.FUN Module to resimulate the network at each time step.
#' @param infection.FUN Module to simulate disease infection.
#' @param prev.FUN Module to calculate disease prevalence at each time step,
#' with the default function of \code{\link{prevalence_het}}.
#' @param verbose.FUN Module to print simulation progress to screen, with the
#' default function of \code{\link{verbose_het}}.
#' @param module.order A character vector of module names that lists modules the
#' order in which they should be evaluated within each time step. If
#' \code{NULL}, the modules will be evaluated as follows: first any
#' new modules supplied through \code{...} in the order in which they are
#' listed, then the built-in modules in their order of the function listing.
#' The \code{initialize.FUN} will always be run first and the
#' \code{verbose.FUN} always last.
#' @param save.nwstats Save out network statistics.
#' @param save.other Other list elements of dat to save out.
#' @param verbose If \code{TRUE}, print progress to console.
#' @param verbose.int Interval for printing progress to console.
#' @param skip.check If \code{TRUE}, skips the error check for parameter values,
#' initial conditions, and control settings before running the models.
#' @param ... Additional arguments passed to the function.
#'
#' @details This function sets the parameters for the models.
#'
#' @export
#'
control_het <- function(simno = 1,
nsteps = 100,
start = 1,
nsims = 1,
ncores = 1,
par.type = "single",
initialize.FUN = initialize_het,
aging.FUN = aging_het,
cd4.FUN = cd4_het,
vl.FUN = vl_het,
dx.FUN = dx_het,
tx.FUN = tx_het,
deaths.FUN = deaths_het,
births.FUN = births_het,
resim_nets.FUN = simnet_het,
infection.FUN = infect_het,
prev.FUN = prevalence_het,
verbose.FUN = verbose_het,
module.order = NULL,
save.nwstats = FALSE,
save.other = c("el", "attr"),
verbose = TRUE,
verbose.int = 1,
skip.check = TRUE,
...) {
p <- list()
formal.args <- formals(sys.function())
formal.args[["..."]] <- NULL
for (arg in names(formal.args)) {
p[arg] <- list(get(arg))
}
dot.args <- list(...)
names.dot.args <- names(dot.args)
if (length(dot.args) > 0) {
for (i in 1:length(dot.args)) {
p[[names.dot.args[i]]] <- dot.args[[i]]
}
}
bi.mods <- grep(".FUN", names(formal.args), value = TRUE)
bi.mods <- bi.mods[which(sapply(bi.mods, function(x) !is.null(eval(parse(text = x))),
USE.NAMES = FALSE) == TRUE)]
p$bi.mods <- bi.mods
p$user.mods <- grep(".FUN", names.dot.args, value = TRUE)
p$save.transmat <- FALSE
p$save.network <- FALSE
class(p) <- "control.net"
return(p)
}
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.