mxtrmodLL: A function to return the negative log-likelihood of mixture...

In mnodzenski/metabomxtr: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions

A function to return the negative log-likelihood of mixture models of truncated, normal or lognormal data

Description

This function returns the negative log-likelihood of the specified mixture model.

Usage

mxtrmodLL(params, obsY, xVars, zVars, Tvals, includeDiscrete)

Arguments

params	A vector of parameter estimates for all paramters in the mixture model.
obsY	A vector containing the response variable, which must be normally or log normally distributed. If lognormal, log transformed Y's should be input as the response variable. Missing Y values should be indicated by NA.
xVars	The design matrix for the covariates included in the discrete portion of the model.
zVars	The design matrix for the covariates inlcuded in the continuous portion of the model.
Tvals	A vector of thresholds below which continuous variables are not observable.
includeDiscrete	A logical indicator for whether or not to include the discrete portion of the model.

Value

Returns the negative log-likelihood of the specified mixture model.

Author(s)

Michael Nodzenski, Anna Reisetter, Denise Scholtens

References

Moulton LH, Halsey NA. A mixture model with detection limits for regression analyses of antibody response to vaccine. Biometrics. 1995 Dec;51(4):1570-8.

Examples

#Create sample data
set.seed(123)
yvar<-rlnorm(200)
these<-sample(1:100,20)
yvar[these]<-NA
logyvar<-log(yvar)
y2var<-rlnorm(200)
those<-sample(1:200,25)
y2var[those]<-NA
logy2var<-log(y2var)
pred1<-sample(0:1,200,replace=TRUE)
pred2<-sample(1:10,200,replace=TRUE)
testdata<-data.frame(cbind(yvar,y2var,logyvar,logy2var,pred1,pred2))

#Create a vector of starting values for the function 
startvals<-c(2,0,0,1.5,0,0,2)

#Create a vector of response variables
obsY<-testdata$logyvar

#Create the design matrix for the discrete portion of the model
xVars<-model.matrix(~pred1+pred2,data=testdata)

#Create the design matrix for the continuous portion of the model 
zVars<-model.matrix(~pred1+pred2,data=testdata)

#Create the Tvals vector 
Tvals<-rep(min(obsY,na.rm=TRUE),length(obsY))

#Determine if the discrete portion should be inlcuded in the model 
includeDiscrete<-sum(is.na(obsY))>5

#Calculate the negative log-likelihood
mxtrmodLL(params=startvals,obsY=obsY,xVars=xVars,zVars=zVars,Tvals=Tvals,
          includeDiscrete=includeDiscrete)

mnodzenski/metabomxtr documentation built on Aug. 24, 2022, 1:40 p.m.