The control parameters of the function `runDescend`

and `deconvSingle`

1 2 3 4 5 6 | ```
DESCEND.control(n.points = 50, nStart = 2, nStart.lrt = 2,
max.sparse = c(0.99, 20), LRT.Z.select = T, LRT.Z0.select = T,
LRT.Z.values = 0, zeroInflate = T, dense.add.0 = T, only.integer = F,
rel.info.range = c(5e-04, 0.01), rel.info.guide = 0.005, c0.start = 1,
aStart = 1, bStart = 0, gStart = 0, start.sd = 0.2, penalty.Z0 = T,
pDegree = 5, max.c0.iter = 5, c0.min = 1e-05)
``` |

`n.points` |
number of discritized points of the underlying true expression distribution. Default is 50 |

`nStart` |
number of random starts for the optimization problem (as it is non-convex) to find the global minimum. Default is 2 |

`nStart.lrt` |
number of random starts for the unpenalized optimization problem for likelihood ratio testing |

`max.sparse` |
a vector of 2 indicating the maximum sparsity allowed for a gene to be computed. The first element is the fraction of zero-counts allowed, the second element is the minimum number of non-zero counts. Both criteria should be satisfied. Default is (0.99, 20). For studying nonzero fraction, one should increase the threshold to get estimates with acceptable accuracy. |

`LRT.Z.select` |
a vector of length 1 or the number of columns of |

`LRT.Z0.select` |
a vector of length 1 or the number of columns of |

`LRT.Z.values` |
a vector of length 1 or the number of columns of |

`zeroInflate` |
whether to include the zero inflation part to the deconvolved distribution. Default is TRUE |

`dense.add.0` |
whether smooth the density at 0 into |

`only.integer` |
whether set the discrete points to be integers. Default is FALSE |

`rel.info.range` |
the relative information range allowed for finding the optimal tuning parameter |

`rel.info.guide` |
one parameter inside the |

`c0.start` |
the starting value of |

`aStart` |
the starting values of the spline coefficients of the deconvolved distribution |

`bStart` |
the starting values of the coefficients of Z0 |

`gStart` |
the starting values of the coefficients of Z |

`start.sd` |
standard deviation of the random starting values when nStart > 1 |

`penalty.Z0` |
whether add penalty to the coefficients of Z0 or not in optimization. Default is TRUE |

`pDegree` |
degree of the spline bases. Default is 5 |

`max.c0.iter` |
maximum iteration allowed to find the optimal |

`c0.min` |
minimum |

jingshuw/descend documentation built on Sept. 2, 2018, 11:10 a.m.

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