R/hybrid_Model.R
In fernandosm/hybridModels: An R Package for the Stochastic Simulation of Disease Spreading in Dynamic Networks
Defines functions
hybridModel
Documented in
hybridModel
#' Hybrid model simulation.
#'
#' @description \code{hybridModel} function runs hybrid models simulations.
#'
#' @param network a \code{\link{data.frame}} with variables that describe
#' the donor node, the receiver node, the time when each connection between
#' donor to the receiver happened and the number of individual or weight of
#' these connection.
#'
#' @param var.names a \code{\link{list}} with variable names of the network:
#' the donor node, the receiver node, the time when each connection between
#' donor to the receiver happened and the weight of these connection.
#' The variables names must be "from", "to", "Time" and "arc", respectively.
#'
#' @param link.type a \code{\link{character}} describing the link type between
#' nodes. There are two types: 'migration' and 'influence'. In the migration
#' link type there are actual migration between nodes. In the influence
#' link type individuals does not migrate, just influences another node.
#'
#' @param model a \code{\link{character}} describing model's name.
#'
#' @param init.cond a named \code{\link{vector}} with initial conditions.
#'
#' @param fill.time It indicates whether to return all dates or just the dates
#' when nodes get connected.
#'
#' @param model.parms a named \code{\link{vector}} with model's parameters.
#'
#' @param prop.func a character \code{\link{vector}} with propensity functions
#' of a generic node. See references for more details
#'
#' @param state.change.matrix is a state-change \code{\link{matrix}}. See references
#' for more details
#'
#' @param state.var a character \code{\link{vector}} with the state variables of
#' the propensity functions.
#'
#' @param infl.var a named \code{\link{vector}} mapping state variables to influence
#' variables.
#'
#' @param ssa.method a \code{\link{list}} with SSA parameters. The default method
#' is the direct method. See references for more details
#'
#' @param nodesCensus a \code{\link{data.frame}} with the first column describing
#' nodes' ID, the second column with the number of individuals and the third
#' describing the day of the census.
#'
#' @param sim.number Number of repetitions.The default value is 1
#'
#' @param pop.correc Whether \code{hybridModel} function tries to balance the number
#' of individuals or not. The default value is TRUE.
#'
#' @param num.cores number of threads/cores that the simulation will use. the
#' default value is num.cores = 'max', the Algorithm will use all
#' threads/cores available.
#'
#' @param probWeights a named \code{\link{vector}} (optional and for migration type
#' only) mapping state variables to migration probability weights based on
#' state variables. These argument can be used to give weights for sampling
#' individuals from node. They need not sum to one, they should be
#' non-negative and not zero. For more information on the sampling method
#' \link[base]{sample}.
#'
#' @param emigrRule a string (optional and for migration type only) stating how
#' many individual emigrate based on state variables. It requires that the
#' network have weights instead of number of individuals that migrate.
#'
#' @return Object containing a \code{\link{data.frame}} (results) with the number
#' of individuals through time per node and per state.
#'
#' @references
#' [1] Pineda-krch, M. (2008). GillespieSSA : Implementing the Stochastic
#' Simulation Algorithm in R. Journal of Statistical Software, Volume 25
#' Issue 12 <doi:10.1146/annurev.physchem.58.032806.104637>.
#'
#' [2] Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al.
#' hybridModels: An R Package for the Stochastic Simulation of Disease Spreading
#' in Dynamic Network. In: Jounal of Statistical Software Volume 94, Issue 6
#' <doi:10.18637/jss.v094.i06>.
#'
#' @seealso \link{GillespieSSA}.
#' @export
#' @examples
#' # Migration model
#' # Parameters and initial conditions for an SIS model
#' # loading the data set
#' data(networkSample) # help("networkSample"), for more info
#' networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]
#'
#' var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
#' arc = 'num.animals')
#'
#' prop.func <- c('beta * S * I / (S + I)', 'gamma * I')
#' state.var <- c('S', 'I')
#' state.change.matrix <- matrix(c(-1, 1, # S
#' 1, -1), # I
#' nrow = 2, ncol = 2, byrow = TRUE)
#'
#' model.parms <- c(beta = 0.1, gamma = 0.01)
#'
#' init.cond <- rep(100, length(unique(c(networkSample$originID,
#' networkSample$destinationID))))
#' names(init.cond) <- paste('S', unique(c(networkSample$originID,
#' networkSample$destinationID)), sep = '')
#' init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
#'
#' # running simulations, check the number of cores available (num.cores)
#' sim.results <- hybridModel(network = networkSample, var.names = var.names,
#' model.parms = model.parms, state.var = state.var,
#' prop.func = prop.func, init.cond = init.cond,
#' state.change.matrix = state.change.matrix,
#' sim.number = 2, num.cores = 2)
#'
#' # default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
#' plot(sim.results, plot.type = 'subpop.mean')
#'
#' # changing plot layout with ggplot2 (example)
#' # uncomment the lines below to test new layout exemple
#' #library(ggplot2)
#' #plot(sim.results, plot.type = 'subpop') + ggtitle('New Layout') +
#' # theme_bw() + theme(axis.title = element_text(size = 14, face = "italic"))
#'
#' # Influence model
#' # Parameters and initial conditions for an SIS model
#' # loading the data set
#' data(networkSample) # help("networkSample"), for more info
#' networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]
#'
#' var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
#' arc = 'num.animals')
#'
#' prop.func <- c('beta * S * (I + i) / (S + I + s + i)', 'gamma * I')
#' state.var <- c('S', 'I')
#' infl.var <- c(S = "s", I = "i") # mapping influence
#' state.change.matrix <- matrix(c(-1, 1, # S
#' 1, -1), # I
#' nrow = 2, ncol = 2, byrow = TRUE)
#'
#' model.parms <- c(beta = 0.1, gamma = 0.01)
#'
#' init.cond <- rep(100, length(unique(c(networkSample$originID,
#' networkSample$destinationID))))
#' names(init.cond) <- paste('S', unique(c(networkSample$originID,
#' networkSample$destinationID)), sep = '')
#' init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
#'
#' # running simulations, check num of cores available (num.cores)
#' # Uncomment to run
#' # sim.results <- hybridModel(network = networkSample, var.names = var.names,
#' # model.parms = model.parms, state.var = state.var,
#' # infl.var = infl.var, prop.func = prop.func,
#' # init.cond = init.cond,
#' # state.change.matrix = state.change.matrix,
#' # sim.number = 2, num.cores = 2)
#'
#' # default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
#' # plot(sim.results, plot.type = 'subpop.mean')
#'
hybridModel <- function(network = stop("undefined 'network'"), var.names = NULL,
link.type = 'migration', model = 'custom', probWeights = NULL,
emigrRule = NULL, init.cond = stop("undefined 'initial conditions'"),
fill.time = F, model.parms = stop("undefined 'model parmeters'"),
prop.func = NULL, state.var = NULL, infl.var = NULL,
state.change.matrix = NULL, ssa.method = NULL,
nodesCensus = NULL, sim.number = 1, pop.correc = TRUE,
num.cores = 'max'){
#### setting dynamic type #####
if (!is.null(var.names)){
network <- network[, c(var.names$from, var.names$to, var.names$Time,
var.names$arc)]
} else{
network <- network[, c("from", "to", "Time",
"arc")]
}
if (!is.null(infl.var)){
link.type = 'influence'
}
#### building classes ####
if(model == 'SI model without demographics' & link.type == 'migration'){
model1 <- 'siWoDemogrMigr'
} else if(model == 'SI model without demographics' & link.type == 'influence'){
model1 <- 'siWoDemogrInfl'
} else if(model == 'custom' & link.type == 'migration'){
model1 <- 'customMigr'
} else if(model == 'custom' & link.type == 'influence'){
model1 <- 'customInfl'
}
model2simulate <- buildModelClass(structure(list(network = network,
ssa.method = ssa.method,
pop.correc = pop.correc,
nodes.info = nodesCensus),
class = c(model1, 'HM')), var.names,
init.cond, model.parms, probWeights, emigrRule,
prop.func, state.var, infl.var, state.change.matrix)
#### running the simulation ####
model2simulate$results <- simHM(model2simulate, network, sim.number, num.cores, fill.time = F)
return(model2simulate)
}
fernandosm/hybridModels documentation built on July 2, 2020, 10:50 p.m.
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Defines functions hybridModel
Documented in hybridModel
#' Hybrid model simulation.
#'
#' @description \code{hybridModel} function runs hybrid models simulations.
#'
#' @param network a \code{\link{data.frame}} with variables that describe
#' the donor node, the receiver node, the time when each connection between
#' donor to the receiver happened and the number of individual or weight of
#' these connection.
#'
#' @param var.names a \code{\link{list}} with variable names of the network:
#' the donor node, the receiver node, the time when each connection between
#' donor to the receiver happened and the weight of these connection.
#' The variables names must be "from", "to", "Time" and "arc", respectively.
#'
#' @param link.type a \code{\link{character}} describing the link type between
#' nodes. There are two types: 'migration' and 'influence'. In the migration
#' link type there are actual migration between nodes. In the influence
#' link type individuals does not migrate, just influences another node.
#'
#' @param model a \code{\link{character}} describing model's name.
#'
#' @param init.cond a named \code{\link{vector}} with initial conditions.
#'
#' @param fill.time It indicates whether to return all dates or just the dates
#' when nodes get connected.
#'
#' @param model.parms a named \code{\link{vector}} with model's parameters.
#'
#' @param prop.func a character \code{\link{vector}} with propensity functions
#' of a generic node. See references for more details
#'
#' @param state.change.matrix is a state-change \code{\link{matrix}}. See references
#' for more details
#'
#' @param state.var a character \code{\link{vector}} with the state variables of
#' the propensity functions.
#'
#' @param infl.var a named \code{\link{vector}} mapping state variables to influence
#' variables.
#'
#' @param ssa.method a \code{\link{list}} with SSA parameters. The default method
#' is the direct method. See references for more details
#'
#' @param nodesCensus a \code{\link{data.frame}} with the first column describing
#' nodes' ID, the second column with the number of individuals and the third
#' describing the day of the census.
#'
#' @param sim.number Number of repetitions.The default value is 1
#'
#' @param pop.correc Whether \code{hybridModel} function tries to balance the number
#' of individuals or not. The default value is TRUE.
#'
#' @param num.cores number of threads/cores that the simulation will use. the
#' default value is num.cores = 'max', the Algorithm will use all
#' threads/cores available.
#'
#' @param probWeights a named \code{\link{vector}} (optional and for migration type
#' only) mapping state variables to migration probability weights based on
#' state variables. These argument can be used to give weights for sampling
#' individuals from node. They need not sum to one, they should be
#' non-negative and not zero. For more information on the sampling method
#' \link[base]{sample}.
#'
#' @param emigrRule a string (optional and for migration type only) stating how
#' many individual emigrate based on state variables. It requires that the
#' network have weights instead of number of individuals that migrate.
#'
#' @return Object containing a \code{\link{data.frame}} (results) with the number
#' of individuals through time per node and per state.
#'
#' @references
#' [1] Pineda-krch, M. (2008). GillespieSSA : Implementing the Stochastic
#' Simulation Algorithm in R. Journal of Statistical Software, Volume 25
#' Issue 12 <doi:10.1146/annurev.physchem.58.032806.104637>.
#'
#' [2] Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al.
#' hybridModels: An R Package for the Stochastic Simulation of Disease Spreading
#' in Dynamic Network. In: Jounal of Statistical Software Volume 94, Issue 6
#' <doi:10.18637/jss.v094.i06>.
#'
#' @seealso \link{GillespieSSA}.
#' @export
#' @examples
#' # Migration model
#' # Parameters and initial conditions for an SIS model
#' # loading the data set
#' data(networkSample) # help("networkSample"), for more info
#' networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]
#'
#' var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
#' arc = 'num.animals')
#'
#' prop.func <- c('beta * S * I / (S + I)', 'gamma * I')
#' state.var <- c('S', 'I')
#' state.change.matrix <- matrix(c(-1, 1, # S
#' 1, -1), # I
#' nrow = 2, ncol = 2, byrow = TRUE)
#'
#' model.parms <- c(beta = 0.1, gamma = 0.01)
#'
#' init.cond <- rep(100, length(unique(c(networkSample$originID,
#' networkSample$destinationID))))
#' names(init.cond) <- paste('S', unique(c(networkSample$originID,
#' networkSample$destinationID)), sep = '')
#' init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
#'
#' # running simulations, check the number of cores available (num.cores)
#' sim.results <- hybridModel(network = networkSample, var.names = var.names,
#' model.parms = model.parms, state.var = state.var,
#' prop.func = prop.func, init.cond = init.cond,
#' state.change.matrix = state.change.matrix,
#' sim.number = 2, num.cores = 2)
#'
#' # default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
#' plot(sim.results, plot.type = 'subpop.mean')
#'
#' # changing plot layout with ggplot2 (example)
#' # uncomment the lines below to test new layout exemple
#' #library(ggplot2)
#' #plot(sim.results, plot.type = 'subpop') + ggtitle('New Layout') +
#' # theme_bw() + theme(axis.title = element_text(size = 14, face = "italic"))
#'
#' # Influence model
#' # Parameters and initial conditions for an SIS model
#' # loading the data set
#' data(networkSample) # help("networkSample"), for more info
#' networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]
#'
#' var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
#' arc = 'num.animals')
#'
#' prop.func <- c('beta * S * (I + i) / (S + I + s + i)', 'gamma * I')
#' state.var <- c('S', 'I')
#' infl.var <- c(S = "s", I = "i") # mapping influence
#' state.change.matrix <- matrix(c(-1, 1, # S
#' 1, -1), # I
#' nrow = 2, ncol = 2, byrow = TRUE)
#'
#' model.parms <- c(beta = 0.1, gamma = 0.01)
#'
#' init.cond <- rep(100, length(unique(c(networkSample$originID,
#' networkSample$destinationID))))
#' names(init.cond) <- paste('S', unique(c(networkSample$originID,
#' networkSample$destinationID)), sep = '')
#' init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
#'
#' # running simulations, check num of cores available (num.cores)
#' # Uncomment to run
#' # sim.results <- hybridModel(network = networkSample, var.names = var.names,
#' # model.parms = model.parms, state.var = state.var,
#' # infl.var = infl.var, prop.func = prop.func,
#' # init.cond = init.cond,
#' # state.change.matrix = state.change.matrix,
#' # sim.number = 2, num.cores = 2)
#'
#' # default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
#' # plot(sim.results, plot.type = 'subpop.mean')
#'
hybridModel <- function(network = stop("undefined 'network'"), var.names = NULL,
link.type = 'migration', model = 'custom', probWeights = NULL,
emigrRule = NULL, init.cond = stop("undefined 'initial conditions'"),
fill.time = F, model.parms = stop("undefined 'model parmeters'"),
prop.func = NULL, state.var = NULL, infl.var = NULL,
state.change.matrix = NULL, ssa.method = NULL,
nodesCensus = NULL, sim.number = 1, pop.correc = TRUE,
num.cores = 'max'){
#### setting dynamic type #####
if (!is.null(var.names)){
network <- network[, c(var.names$from, var.names$to, var.names$Time,
var.names$arc)]
} else{
network <- network[, c("from", "to", "Time",
"arc")]
}
if (!is.null(infl.var)){
link.type = 'influence'
}
#### building classes ####
if(model == 'SI model without demographics' & link.type == 'migration'){
model1 <- 'siWoDemogrMigr'
} else if(model == 'SI model without demographics' & link.type == 'influence'){
model1 <- 'siWoDemogrInfl'
} else if(model == 'custom' & link.type == 'migration'){
model1 <- 'customMigr'
} else if(model == 'custom' & link.type == 'influence'){
model1 <- 'customInfl'
}
model2simulate <- buildModelClass(structure(list(network = network,
ssa.method = ssa.method,
pop.correc = pop.correc,
nodes.info = nodesCensus),
class = c(model1, 'HM')), var.names,
init.cond, model.parms, probWeights, emigrRule,
prop.func, state.var, infl.var, state.change.matrix)
#### running the simulation ####
model2simulate$results <- simHM(model2simulate, network, sim.number, num.cores, fill.time = F)
return(model2simulate)
}
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