Description Usage Arguments Value Author(s) See Also Examples

Function to estimate parameters for NUDGE model, mixture of
uniform and *k*-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. |

`K` |
optional number of normal component that will be fitted in iNUDGE model. |

`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 iNUDGE mixture
components. The first entry is for the uniform component, the middle |

`mu` |
optional vector containing initial estimates of the Gaussian means in iNUDGE 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 iNUDGE parameters. |

`max.iter` |
optional maximum number of iterations for EM algorithm to estimate iNUDGE 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 "iNUDGE" |

`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. |

`K` |
the number of normal components in the corresponding mixture model. |

`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 iNUDGE model. |

`phi` |
a matrix of estimated iNUDGE 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 22 23 24 | ```
library(DIME);
# generate simulated datasets with underlying uniform and 2-normal distributions
set.seed(1234);
N1 <- 1500; N2 <- 500; rmu <- c(-2.25,1.5); rsigma <- c(1,1);
rpi <- c(.10,.45,.45); a <- (-6); b <- 6;
chr4 <- list(c(-runif(ceiling(rpi[1]*N1),min = a,max =b),
rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]),
rnorm(ceiling(rpi[3]*N1),rmu[2],rsigma[2])));
chr9 <- list(c(-runif(ceiling(rpi[1]*N2),min = a,max =b),
rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]),
rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2])));
# analyzing chromosome 4 and 9
data <- list(chr4,chr9);
# fit iNUDGE model with 2 normal components and maximum iterations = 20
set.seed(1234);
test <- inudge.fit(data, K = 2, max.iter=20);
# Getting the best fitted iNUDGE model (parameters)
test$best$pi # estimated proportion of each component in iNUDGE
test$best$mu # estimated mean of the normal component(s) in iNUDGE
# estimated standard deviation of the normal component(s) in iNUDGE
test$best$sigma
``` |

DIME documentation built on May 29, 2017, 6:25 p.m.

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