Implementation of double logistic function (a mixture of logistics)

doublelogisticFunction(t, b, a = 1, q = 1, pi, d)

Arguments

t: numeric vector of time points
b: numeric vector of rate parameters
a: numeric vector of plateau parameters
q: numeric vector of stretch parameters
pi: numeric vector of mixing parameters
d: numeric vector of rate parameters

Value

numeric vector of model predictions

Examples


t <- seq(0, 10, length.out = 100)
b <- c(0.1)
a <- c(1)
q <- c(1)
pi <- c(0.5)
d <- c(0.1)
doublelogisticFunction(t, b, a, q, pi, d)
#>   [1] 0.00000000 0.01005017 0.01999933 0.02984850 0.03959868 0.04925087
#>   [7] 0.05880606 0.06826522 0.07762930 0.08689928 0.09607610 0.10516068
#>  [13] 0.11415397 0.12305687 0.13187029 0.14059514 0.14923230 0.15778266
#>  [19] 0.16624708 0.17462644 0.18292158 0.19113335 0.19926260 0.20731014
#>  [25] 0.21527681 0.22316340 0.23097074 0.23869961 0.24635081 0.25392511
#>  [31] 0.26142329 0.26884610 0.27619432 0.28346869 0.29066995 0.29779883
#>  [37] 0.30485607 0.31184238 0.31875848 0.32560507 0.33238285 0.33909252
#>  [43] 0.34573475 0.35231022 0.35881961 0.36526358 0.37164279 0.37795788
#>  [49] 0.38420951 0.39039831 0.39652490 0.40258993 0.40859400 0.41453773
#>  [55] 0.42042172 0.42624658 0.43201290 0.43772126 0.44337226 0.44896646
#>  [61] 0.45450444 0.45998676 0.46541398 0.47078666 0.47610534 0.48137057
#>  [67] 0.48658288 0.49174281 0.49685088 0.50190761 0.50691352 0.51186912
#>  [73] 0.51677492 0.52163141 0.52643910 0.53119846 0.53590999 0.54057418
#>  [79] 0.54519148 0.54976238 0.55428735 0.55876683 0.56320130 0.56759120
#>  [85] 0.57193698 0.57623908 0.58049795 0.58471402 0.58888771 0.59301946
#>  [91] 0.59710968 0.60115879 0.60516721 0.60913535 0.61306360 0.61695238
#>  [97] 0.62080207 0.62461307 0.62838577 0.63212056