gng.fit | R Documentation |
Function to estimate parameters for GNG model, mixture of exponential and k-normal. Parameters are estimated using EM algorithm.
gng.fit(data, avg = NULL, K = 2, weights = NULL, weights.cutoff = -1.345, pi = NULL, mu = NULL, sigma = NULL, beta = NULL, tol = 1e-05, max.iter = 2000, th = NULL)
data |
an R list of vector of normalized intensities (counts). Each element can correspond to 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. |
K |
optional number of normal component that will be fitted in GNG 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 GNG mixture components. The first and last entries are for the estimates of negative and positive exponentials, respectively. The middle k entries are for normal components. |
mu |
optional vector containing initial estimates of the Gaussian means in GNG model. |
sigma |
optional vector containing initial estimates of the Gaussian standard deviation in GNG model. Must have K entries. |
beta |
optional vector containing initial estimates for the shape parameter in exponential components in GNG model. Must have 2 entries, one for negative exponential the other for positive exponential components. |
tol |
optional threshold for convergence for EM algorithm to estimate GNG parameters. |
max.iter |
optional maximum number of iterations for EM algorithm to estimate GNG parameters. |
th |
optional location parameter used to fit the negative and positive exponential model. |
A list of object:
name |
the name of the model "GNG" |
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. |
beta |
a vector of estimated exponential shape values. |
th1 |
negative location parameter used to fit the negative exponential model. |
th2 |
positive location parameter used to fit the positive exponential model. |
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 GNG model. |
phi |
a matrix of estimated GNG mixture component function. |
AIC |
Akaike Information Criteria. |
BIC |
Bayesian Information Criteria. |
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
DIME
, inudge.fit
, nudge.fit
library(DIME) # generate simulated datasets with underlying exponential-normal components N1 <- 1500; N2 <- 500; K <- 4; rmu <- c(-2.25,1.50); rsigma <- c(1,1); rpi <- c(.05,.45,.45,.05); rbeta <- c(12,10); set.seed(1234) chr1 <- c(-rgamma(ceiling(rpi[1]*N1),shape = 1,scale = rbeta[1]), rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]), rnorm(ceiling(rpi[3]*N1),rmu[2],rsigma[2]), rgamma(ceiling(rpi[4]*N1),shape = 1,scale = rbeta[2])); chr2 <- c(-rgamma(ceiling(rpi[1]*N2),shape = 1,scale = rbeta[1]), rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]), rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2]), rgamma(ceiling(rpi[4]*N2),shape = 1,scale = rbeta[2])); chr3 <- c(-rgamma(ceiling(rpi[1]*N2),shape = 1,scale = rbeta[1]), rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]), rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2]), rgamma(ceiling(rpi[4]*N2),shape = 1,scale = rbeta[2])); # analyzing only chromosome 1 and chromosome 3 data <- list(chr1,chr3); # fit GNG model with 2 normal components test <- gng.fit(data, K = 2); # Getting the best fitted GNG model (parameters) test$pi # estimated proportion of each component in GNG test$mu # estimated mean of the normal component(s) GNG # estimated standard deviation of the normal component(s) in GNG test$sigma # estimated shape parameter of the exponential components in best model test$beta
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