From e42355c9489db2435eba600dda49553dc93a2acc Mon Sep 17 00:00:00 2001 From: Patric Wyss <patric.wyss@upd.unibe.ch> Date: Tue, 2 Nov 2021 19:09:02 +0000 Subject: [PATCH] Remove reference to global training variable --- R/POSEIDON.R | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/R/POSEIDON.R b/R/POSEIDON.R index 600fe95..87663d5 100644 --- a/R/POSEIDON.R +++ b/R/POSEIDON.R @@ -63,16 +63,16 @@ daLmeMulti<- function(resp, variable, diagnosis, predFix, predRand, clust, metho } # 4) fit linear mixed-effects model - model = nlme::lme(formulaFix, data = data, - random = formulaRand, - weights = weightsLmer, - control = lmeControl(returnObject=F, natural=F, - maxIter=maxIter, msMaxIter=msMaxIter, - niterEM=niterEM, msMaxEval=msMaxEval, - optimMethod=optimMethod)) + model<- nlme::lme(formulaFix, data = data, + random = formulaRand, + weights = weightsLmer, + control = lmeControl(returnObject=F, natural=F, + maxIter=maxIter, msMaxIter=msMaxIter, + niterEM=niterEM, msMaxEval=msMaxEval, + optimMethod=optimMethod)) # 5) Compute relative frequencies of diagnoses in trainin set - dataTrainNoDup<- dataTrain[duplicated(dataTrain[[clust]])==F, ] + dataTrainNoDup<- data[duplicated(data[[clust]])==F, ] relFreq<- table(dataTrainNoDup[[diagnosis]])/sum(table(dataTrainNoDup[[diagnosis]])) diagLabels<- names(relFreq) prior<- relFreq[[2]] @@ -116,10 +116,6 @@ predDistParamLme<- function(model, dataSub){ ## (for multivariate models fitted by fitLmeMulti only) varName<- model$input$variable - ## Extract training data and estimate priors - dataTrain<- model$input$dTrain - dataTrain<- dataTrain[duplicated(dataTrain[[nameSubId]])==F, ] - ## Extract prior priorD1<- model$input$prior -- GitLab