Description Usage Arguments Value
Association analysis using mutationlevel association analysis.
1 2 3 
Y 
The response variable. Could be continuous or binary. 
X 
The design matrix. Intercept included. 
O 
A vector for the observed somatic mutation. 
D 
A vector for the total readdepth. 
A 
A vector for the number of alternative number matrix. 
out_type 
The outcome type, "C" for continous, "D" for dichotomous. Default is "C". 
theta_init 
The initail values of the parameters. Can be NULL. 
mix_4bb 
A dataframe indicating the parameters of four betabinomial distributions depending on the values of the observed somatic mutaton and the true somatic mutation when the readdepth is high. 
null 
Logical. Indicating the estimation using EM algorim under the null hypothesis or not. The default is FALSE. 
d0 
The minimum of the total readdepth for obtaining the observed somatic mutation value. The default value is 20. 
bounds 
Some parameters for the bounds in the EM algorithm. Can be NULL. 
converged 
The tolerance for the convergence. Default is 1e6. 
gamm0 
The specificity of the somatic mutation. Default is 1. 
gamm1 
The sensitivity of the somatic mutation. Default is 1. 
maxIT 
The maximal number of the EM iteration times. Default is 200. 
A list containing the output of the EM algorithm.
Theta 

theta 

LogLik 

logLik 

it 
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