cond_ev: Conditional expectation of next step of process

In gmonette/Tcells2: Fits Tcell dynamic models with mixed models and STAN

Description

Conditional expectation of next step of process

Usage

cond_ev(Y, lambda = parms$lambda, birth = parms$birth,
  death = parms$death, tran = parms$tran, parms, verbose = FALSE)

cond_ev_k(k, Y, lambda = parms$lambda, birth = parms$birth,
  death = parms$death, tran = parms$tran, parms, verbose = FALSE)

Arguments

Y	vector of cell counts at time t
lambda	autonomous rate of cell formation for first cell type per unit of time
birth	vector for each type of cell with proportions of cells that replicate per unit of time
death	vector for each type of cell with proportions of cells that die per unit of time
tran	vector of length equal to length(Y) -1 with proportions of cells that transform to the next cell type per unit of time
parms	option list with parameters lambda, birth, death and tran
verbose	(default FALSE) print additional output for debugging
k	number of steps for conditional expectation

Value

conditional expectation at time t+1

Functions

• cond_ev_k: Conditional expectation in k steps of the process

Examples

cond_ev(c(100,100,100), 10, .01, .03, c(.01,.02))
parms <- list(lambda = 10, birth = rep(.01,3),
              death = .03, tran = c(.01,.02))
cond_ev(c(100,100,100), parms = parms)

Nsteps <- 2000
mat <- matrix(NA, 3, Nsteps)
mat[,1] <- c(100,100,100)
for ( i in 2:Nsteps) mat[,i] <- cond_ev(mat[,i-1],parms = parms)
# Equilibrium profile with these parameters:
head(t(mat),10)
tail(t(mat),10)

gmonette/Tcells2 documentation built on May 17, 2019, 7:25 a.m.