| Type: | Package |
| Title: | Toolkits to Develop Individual-Based Models in Infectious Disease |
| Version: | 1.0.0 |
| Date: | 2016-11-16 |
| Description: | It provides a generic set of tools for initializing a synthetic population with each individual in specific disease states, and making transitions between those disease states according to the rates calculated on each timestep. The new version 1.0.0 has C++ code integration to make the functions run faster. It has also a higher level function to actually run the transitions for the number of timesteps that users specify. Additional functions will follow for changing attributes on demographic, health belief and movement. |
| License: | MIT + file LICENSE |
| LazyData: | TRUE |
| RoxygenNote: | 5.0.1 |
| Suggests: | testthat |
| LinkingTo: | Rcpp |
| Imports: | Rcpp |
| NeedsCompilation: | yes |
| Packaged: | 2016-11-16 09:19:06 UTC; Sai |
| Author: | Sai Thein Than Tun [aut, cre] |
| Maintainer: | Sai Thein Than Tun <theinthantun.sai@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2016-11-16 10:51:54 |
Calculate cumulative probabilities for state transitions.
Description
This function takes in a vector of probabilities of states transitions and calculate the probability of staying in the original state and output the cumulative probabilities for all possibilities.
Usage
cumprob(probs, actual = FALSE)
Arguments
probs |
A numeric vector of the probabilities of transition to states. |
actual |
A logical value, if TRUE, will calculate actual cumulative probabilities which may surpass 1!. |
Value
A numeric vector of cumulative probabilites inclusive of the probability of having the same state in the next timestep.
Examples
cumprob(c(.2,.2,.9))
cumprob(c(.2,.2,.9), actual=TRUE)
cumprob(c(.2,.2,.2))
Miscellaneous functions to support the ibmcraftr packare are here.
Description
Miscellaneous functions to support the ibmcraftr packare are here.
Usage
rate2prob(rates)
Arguments
rates |
A numeric scalar or vector to be transformed into rates. |
Value
A numeric scalar or vector in terms of probabilities.
Examples
rate2prob(c(.1, .5))
Run state_trans function over a given number of timesteps.
Description
Organize population data and transition parameters to run state_trans function over the given number of timesteps.
Usage
run_state_trans(timesteps, param, pop, transient = "", useC = TRUE)
Arguments
timesteps |
A numeric scalar based on which the state_trans function will run for that specific no. of |
param |
A list of lists. Each low-level list must contain transition parameters required by the |
pop |
A state matrix created from |
transient |
A character vector. Each element must include formula(e)/expression(s) to evaluate dynamic parameters after each timestep. |
useC |
A logical value, which is TRUE by default, will run |
Value
A summary matrix of the states all individuals in the population are in.
Examples
pop <- syn_pop(c(19,1,0,0,0)) #synthesizing population
b <- 2 #effective contact rate
param <- list(
list(1,c(2,5),c(NA,.1)), #transition from state 1 to 2 using FOI lambda
list(2,3,100), #transition from state 2 to 3,
list(3,4,100) #the 3rd term ensures the transition to the next stage
)
timesteps <- 10
transient <- c("param[[1]][[3]][1] <- rate2prob(b*sum(pop[,2],pop[,3])/sum(pop))")
eval(parse(text=transient))
run_state_trans(timesteps, param, pop, transient)
run_state_trans(timesteps, param, pop, transient, useC = FALSE)
Make state transitions using Rcpp.
Description
Take in the matrix of the states of synthetic population (created by syn_pop function)
and calculate the transitions from one state to other state(s) using the transition probabilities [not rate(s)].
The major difference from the R alone version was that instead of having the transition rate(s),
transition probabilities are used. These probabilities will thus be calculated with another function.
Usage
stRCPP(origin, new.states, params, s.matrix)
Arguments
origin |
A number which represents the column index |
new.states |
A numeric vector or a number which represents the column index |
params |
A numeric vector of similar length to |
s.matrix |
A state matrix created from |
Value
A transition matrix of the same dimension as s.matrix. -1 indicates that the individual has left
the corresponding state. +1 indicates that the individual has become the corresponding state.
Examples
pop <- syn_pop(c(19,1,0,0))
stRCPP(1,2,.1,pop)
Make state transitions.
Description
Take in the matrix of the states of synthetic population (created by syn_pop function)
and calculate the transitions from one state to other state(s) using the transition rate(s).
Usage
state_trans(origin, new.states, params, s.matrix)
Arguments
origin |
A number which represents the column index |
new.states |
A numeric vector or a number which represents the column index |
params |
A numeric vector of similar length to |
s.matrix |
A state matrix created from |
Value
A transition matrix of the same dimension as s.matrix. -1 indicates that the individual has left
the corresponding state. +1 indicates that the individual has become the corresponding state.
Examples
pop <- syn_pop(c(19,1,0,0))
state_trans(1,2,.1,pop)
state_trans(1,4,100,pop)
Create a synthetic population having several states.
Description
Populate a matrix in which columns represent the states of the individuals and rows represent the individuals.
Usage
syn_pop(states, shuffle = FALSE)
Arguments
states |
A numeric vector with each element representing the number of individuals in a particular state its index corresponds to. |
shuffle |
A logical value to enable shuffling of the individuals (rows) in the resulting matrix. |
Value
A matrix of 0s, and 1s. The rows representing the individuals and the columns representing the states the individuals are in
Examples
syn_pop(c(3,2,1))
syn_pop(c(0,0,1,5), shuffle=TRUE)