Description Usage Arguments Value Author(s) See Also Examples

Function to estimate parameters for both NUDGE model, mixture of uniform and 1-normal. Parameters are estimated using EM algorithm.

1 2 |

`data` |
an |

`avg` |
optional vector of mean data (or log intensities). Only required when any one of huber weight (lower, upper or full) is selected. |

`weights` |
optional weights to be used for robust fitting. Can be a matrix the same length as data, or a character description of the huber weight method to be employed: "lower" - only value below weights.cutoff are weighted,\ "upper" - only value above weights.cutoff are weighted,\ "full" - both values above and below weights.cutoff are weighted,\ If selected, mean of data (avg) is required. |

`weights.cutoff` |
optional cutoff to be used with the Huber weighting scheme. |

`pi` |
optional vector containing initial estimates for proportion of the NUDGE mixture
components. The first entry is for the uniform component, the middle |

`mu` |
optional vector containing initial estimates of the Gaussian means in NUDGE model. |

`sigma` |
optional vector containing initial estimates of the Gaussian standard deviation in (i)NUDGE model. Must have K entries. |

`tol` |
optional threshold for convergence for EM algorithm to estimate NUDGE parameters. |

`max.iter` |
optional maximum number of iterations for EM algorithm to estimate NUDGE parameters. |

`z` |
optional 2-column matrix with each row giving initial estimate of probability of the region being non-differential and a starting estimate for the probability of the region being differential. Each row must sum to 1. Number of row must be equal to data length. |

A list of object:

`name` |
the name of the model "NUDGE" |

`pi` |
a vector of estimated proportion of each components in the model |

`mu` |
a vector of estimated Gaussian means for k-normal components. |

`sigma` |
a vector of estimated Gaussian standard deviation for k-normal components. |

`loglike` |
the log likelihood for the fitted mixture model. |

`iter` |
the actual number of iterations run by the EM algorithm. |

`fdr` |
the local false discover rate estimated based on NUDGE model. |

`phi` |
a matrix of estimated NUDGE mixture component function. |

`AIC` |
Akaike Information Criteria. |

`BIC` |
Bayesian Information Criteria. |

Cenny Taslim [email protected], with contributions from Abbas Khalili [email protected], Dustin Potter [email protected], and Shili Lin [email protected]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
library(DIME);
# generate simulated datasets with underlying uniform and 1-normal components
set.seed(1234);
N1 <- 1500; N2 <- 500; rmu <- c(1.5); rsigma <- c(1);
rpi <- c(.10,.90); a <- (-6); b <- 6;
chr1 <- c(-runif(ceiling(rpi[1]*N1),min = a,max =b),
rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]));
chr4 <- c(-runif(ceiling(rpi[1]*N2),min = a,max =b),
rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]));
# analyzing chromosome 1 and 4
data <- list(chr1,chr4);
# fit NUDGE model with maximum iterations = 20 only
set.seed(1234);
bestNudge <- nudge.fit(data, max.iter=20);
# Getting the best fitted NUDGE model (parameters)
bestNudge$pi # estimated proportion of each component in NUDGE
bestNudge$mu # estimated mean of the normal component(s) in NUDGE
# estimated standard deviation of the normal component(s) in NUDGE
bestNudge$sigma
``` |

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