Nothing

```
runMxtrmod <-function(ynames,mxtrModel,Tvals=NULL,nNA=5,minProp=0.2, method="BFGS",data,fullModel=NULL){
###############################################################################################
# Determines optimized estimates for all parameters in a mixture model.
#
# Args:
# ynames: A character vector of the mixture model outcome names, e.g. metabolites.
# If the input data object is a matrix or data frame, these should be column
# names. If the input data object is an expression set, these should be row
# names. Response variables should have a normal or lognormal distribution.
# If lognormal,log transformed variables should be input. Missing values should
# be denoted by NA.
# mxtrModel: A formula of the form ~x1+x2...|z1+z2..., where x's are the names of
# covariates included in the discrete portion of the model and z's are names
# of covariates included in the continuous portion. For intercept only models,
# enter 1 instead of covariate names on the appropriate side of the |.
# Tvals: A vector of thresholds below which continuous variables are not
# observable. By default, this parameter will be set to the minimum
# of the response variable.
# nNA: The minimum number of unobserved response values for the
# discrete portion of the model to be included in the likelihood.
# Models for variables with fewer than nNA missing values will include only
# the continuous portion of the likelihood. The default value is 5.
# minProp: The minimum proportion of non-missing values in the response variable
# necessary to run the model. The default value is 0.2. Models
# will not be run if more than 80% of response variable values are missing.
# method: The method used to optimize the parameter estimates of the mixture model.
# "BFGS" is the default method. Other options are documented in the
# manual for the function 'optimx' in package optimx.
# data: The input data object. Matrices, data frames, and expression sets are
# all acceptable classes. If a data frame or matrix, rows are subjects
# and columns are metabolites or outcomes.
# fullModel: A formula of the form ~x1+x2...|z1+z2..., where x's are the names of
# covariates included in the discrete portion of the full model and z's are names
# of covariates included in the continuous portion. Input if the mxtrModel parameter
# represents a reduced model.
# Returns:
# A data frame with optimized estimates for all parameters in the mixture model, the negative
# log likelihood, the optimization method used, whether the algorithm converged, and the
# number of observations used.
#################################################################################################
#Define a data frame containing the response variables
obsY<-yvals(data,ynames)
#Make the model a Formula object
mxtrModel1<-Formula(mxtrModel)
#Define the design matrix for the discrete portion of the model
xVars<-xdesign(data,mxtrModel1)
#Define the design matrix for the continuous portion of the model
zVars<-zdesign(data,mxtrModel1)
#If mxtrModel is a reduced model, make the fullModel parameter a formula object.
#Then define a design matrices for the full version of the mixture model
#and merge the two design matrices together.
if(!is.null(fullModel)){
fullMod<-Formula(fullModel)
xVarsFull<-xdesign(data,fullMod)
zVarsFull<-zdesign(data,fullMod)
allCovariates<-cbind(xVarsFull,zVarsFull)
} else {
#Otherwise, merge the original desgin matrices
allCovariates<-cbind(xVars,zVars)
}
#Determine if any of the observations have missing covariate values
keep.these<-apply(allCovariates,1,function(x){all(!is.na(x))})
#Remove observations from the data frame of response variables that
#do not have fully observed covariates
obsY<-obsY[keep.these, ,drop=FALSE]
#Remove observations from the discrete design matrix that
#do not have fully observed covariates
xVars<-xVars[keep.these, ,drop=FALSE]
#Remove observations from the continuous design matrix that
#do not have fully observed covariates
zVars<-zVars[keep.these, ,drop=FALSE]
#Run a mixture model on each response variable, to return optimized
#parameter estimates and the negative log likelihood
result<-apply(obsY,2,function(obsY) {
#Determine if there are enough missing values to include the discrete
#component of the mixture model.
includeDiscrete <- sum(is.na(obsY))>nNA
#Determine if there are enough non-missing values present to run the model
if(sum(!is.na(obsY))>minProp*length(obsY)){
#If enough non-missing values are present, specify starting values for the model
myStart<-mxtrmodstart(obsY=obsY,xVars=xVars,zVars=zVars,includeDiscrete=includeDiscrete)
#Specify Tvals. By default, the function will use the minimum of the observed
#responses.
if(is.null(Tvals)){
Tvals <- rep(min(obsY,na.rm=TRUE),length(obsY))
}
#Optimize the function.
optimResult <- optimx(myStart,
fn=mxtrmodLL,
obsY=obsY,xVars=xVars,zVars=zVars,Tvals=Tvals,includeDiscrete=includeDiscrete,
method=method)
#Get the optimized parameter estimates.
modelPars <- as.vector(coef(optimResult))
#Name the parameters
if (dim(xVars)[2]==1){
xVarNames<-NULL
} else {
xVarNames<-paste("x_",colnames(xVars)[-1],sep="")
}
if (dim(zVars)[2]==1){
zVarNames<-NULL
} else {
zVarNames<-paste("z_",colnames(zVars)[-1],sep="")
}
#If the discrete component is included, name both continuous and discrete variables.
#also, store the number of included observations, including missing
namedPars<- modelPars
if(includeDiscrete){
names(namedPars) <- c("xInt",xVarNames,"zInt",zVarNames,"sigma")
nObs<-length(obsY)
#Otherwise, only name the continuous variables, and store the number of included observations,
#excluding missing values
} else if(!includeDiscrete){
names(namedPars) <- c("zInt",zVarNames,"sigma")
nObs<-sum(!is.na(obsY))
}
namedPars<-t(namedPars)
#Calculate the negative log-likelihood of the model.
negLL <- mxtrmodLL(modelPars,obsY=obsY,xVars=xVars,zVars=zVars,Tvals=Tvals,includeDiscrete=includeDiscrete)
#Combine the final results into a data frame.
ans <- data.frame(namedPars, method=rownames(optimResult),conv=optimResult$convcode,negLL=negLL,nObs=nObs)
} else {
ans <- NULL
}
return(ans)
})
#bind together all metabolite specific results into a single data frame
ldply(result,rbind)
}
```

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