Prob_resistance: Prob_resistance

In Michorlab/ACESO: A Cancer Evolution Simulation Optimizer

Description

Probability of resistance at time t

Usage

Prob_resistance(t, type_0, type_i, N, approximation = T,
  int.function = c("integrate", "pracma"))

Arguments

t	time of production of a resistant cell clone
type_0	Type-0 S4 object
type_i	Type-i S4 object
N	number of resistant cell clones
approximation	logical argument indicating if an approximation of the numerical integration method must be used or not. Default to TRUE for faster computation.
int.function	integration function. Possible options are "integrate" or "pracma". The default option is integrate function in R. If "pracma" is selected, the numerical integration methods from pracma package are used (more robust but slower option).

Value

returns the probability that there exists at least one resistant cell of any type at time T after treatment iniciation

Examples

## Not run: 
#Birth rate of sensitive cells as a function of time:
 b0 <- function(time){0.05*sin(0.1*time)+0.14}
 #Birth rate of resistant cells as a function of time:
 b1 <- function(time){0.05*sin(0.1*time)+0.12}
 #Create Type-0 S4 object structure with the parameters of sensitive cells
 Type0 <-define.Type0.cells(N0=100,birth_rate = b0,death_rate= 0.14)
 #Create Type-i S4 object structure with the parameters of resistant cells
 Type1 <- define.Typei.cells(Ni=0,birth_rate = b1,death_rate= 0.1,mutation_rate=10^-3)
 Group the resistant cells types in a list
 Type_i<-list(Type1)
 Call the function:
 Prob_resistance(t=100,type_0=Type0,type_i=Type_i,N=1)
 
## End(Not run)

Michorlab/ACESO documentation built on June 4, 2021, 4:57 p.m.