Description Usage Arguments Details See Also Examples

Simulates a clone up to a time t and represents the clone as a binary tree.

1 2 3 |

`t` |
time of end of experiment . |

`mutprob` |
mutation probability: numeric between 0 and 1. By default 1.e-2. |

`fitness` |
fitness parameter: numeric positive. By default 1. |

`death` |
death probability: numeric between 0 and 0.5. By default 0. |

`dist` |
lifetime distribution for mutant cells. See Details. |

`col` |
vector of size 2 of colors. The first is for the normal cells, the second for the mutant cells. |

This function does not provide a representation of a realistic realization of a mutation model (mutation probability too high, time of end of experiment to small).

The argument `dist`

is a list beginning with the distribution name followed by its parameters, and must be one the 4 following distributions: "dirac", "exp", "lnorm"(meanlog, sdlog), "gamma"(shape, scale).
Note that the parameters related to the "dirac" and "exp" cases are directly computed with inputs `fitness`

and `death`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# Luria-Delbrück model, mutation probability 1e-2, fitness 1
draw.clone(t=9,dist=list(name="exp",rate=1))
# Luria-Delbrück model, mutation probability 0.1, fitness 0.6
draw.clone(t=9,mutprob=0.1,fitness=0.6,dist=list(name="exp",rate=1))
# Haldane model, mutation probability 1e-2, fitness 1
draw.clone(t=7,dist=list(name="dirac",location=1))
# Lognormal lifetime distribution
draw.clone(t=7,fitness=0.5,death=0.1)
# Luria-Delbrück model with positive cell death probability
draw.clone(t=7,dist=list(name="exp",rate=1),death=0.2)
``` |

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