Description Usage Arguments Value Author(s) References Examples

IRLS-EM algorithm for the optimization of penalized maximum likelihood of finite-mixture Cox PH model using the penalty of LASSO, adaptive LASSO, SCAD or elastic net.

1 2 3 |

`Time` |
observed time |

`Delta` |
survival status |

`X` |
a data matrix of explanatory variables, where each colomn correponds to one predictor and each row indicates one sample. |

`K` |
number of components in the finite-mixture Cox model |

`iter.max` |
maximum number of EM iterations |

`u.init` |
initial value of U, a data matrix that gives the probability that each sample belongs to each component. Each row corresponds to one sample and each column indicates one component. |

`tparm` |
value of tuning parameter for variable selection |

`alpha` |
the elastic net mixing parameter |

`scad` |
is the SCAD penalty applied? |

`adpcoef` |
the adaptive weights in the adaptive LASSO method |

`abstol` |
absolute tolerance of EM algorithm |

`reltol` |
relative tolerance of EM algorithm |

`seed` |
random seed for initialing U if it is not given |

a list, where

`U` |
posterior probability matrix |

`fit` |
a list with |

`pi` |
estimate for the mixing probability |

`class` |
subtype classification for each sample |

`ploglik` |
partial log-likelihood |

`mixloglik` |
observed log-likelihood |

`iter` |
number of EM iterations |

`convergence` |
indicator to show the algorithm converged or not |

Shijie Quan, Shun He

Subtype classification and heterogeneous prognosis model construction in precision medicine. Na You, Shun He, Xueqin Wang, Junxian Zhu and Heping Zhang

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
require(PFMC)
data(example.data)
nopenalty.fit = pmixcox(Time = example.data$time,
Delta = example.data$status,
X = example.data[,-1:-2],
K = 2,
tparm = 0,
seed = 1)
adpcoef = lapply(nopenalty.fit$fit, function(x) 1/abs(x))
alasso.cv = calcvcox(Time = example.data$time,
Delta = example.data$status,
X = example.data[,-1:-2],
K = 2,
nopenaltyresult = nopenalty.fit,
adpcoef = adpcoef)
tparmchoice = alasso.cv[1,]
alasso.fit = pmixcox(Time = example.data$time,
Delta = example.data$status,
X = example.data[,-1:-2],
K = 2,
u.init = nopenalty.fit$U,
tparm = tparmchoice,
adpcoef = adpcoef)
``` |

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