nudge.fit {DIME} | R Documentation |
Function for Fitting NUDGE model parameters
Description
Function to estimate parameters for both NUDGE model, mixture of uniform and 1-normal. Parameters are estimated using EM algorithm.
Usage
nudge.fit(data, avg = NULL, weights = NULL, weights.cutoff = -1.345,
pi = NULL, mu = NULL, sigma = NULL, tol = 1e-5, max.iter = 2000, z = NULL)
Arguments
data |
an R list of vector of normalized intensities (counts). Each element can correspond to a particular chromosome. User can construct their own list containing only the chromosome(s) they want to analyze. |
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 k entries are for normal components. |
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. |
Value
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. |
Author(s)
Cenny Taslim taslim.2@osu.edu, with contributions from Abbas Khalili khalili@stat.ubc.ca, Dustin Potter potterdp@gmail.com, and Shili Lin shili@stat.osu.edu
See Also
Examples
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