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adding exp() to OR´s -> major fix!!
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@ -1,7 +1,7 @@
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Package: daDoctoR
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Package: daDoctoR
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Type: Package
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Type: Package
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Title: FUNCTIONS FOR HEALTH RESEARCH
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Title: FUNCTIONS FOR HEALTH RESEARCH
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Version: 0.1.0.9016
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Version: 0.1.0.9018
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Author@R: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
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Author@R: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
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Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
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Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
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Description: I am a Danish medical doctor involved in neuropsychiatric research.
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Description: I am a Danish medical doctor involved in neuropsychiatric research.
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@ -67,16 +67,16 @@ strobe_diff_twodim<-function(meas,group,adj,data,dec=2){
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di<-dat
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di<-dat
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mod<-glm(m~g,family=binomial(),data=di)
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mod<-glm(m~g,family=binomial(),data=di)
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ci<-confint(mod)
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ci<-exp(confint(mod))
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co<-round(coef(mod)[-1],dec)
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co<-round(exp(coef(mod))[-1],dec)
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lo<-round(ci[-1,1],dec)
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lo<-round(ci[-1,1],dec)
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up<-round(ci[-1,2],dec)
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up<-round(ci[-1,2],dec)
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or_ci<-c("REF",paste0(co," (",lo," to ",up,")"))
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or_ci<-c("REF",paste0(co," (",lo," to ",up,")"))
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amod<-glm(m~.,family=binomial(),data=di)
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amod<-glm(m~.,family=binomial(),data=di)
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aci<-confint(amod)
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aci<-exp(confint(amod))
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aco<-round(coef(amod)[2:length(levels(g))],dec)
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aco<-round(exp(coef(amod))[2:length(levels(g))],dec)
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alo<-round(aci[2:length(levels(g)),1],dec)
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alo<-round(aci[2:length(levels(g)),1],dec)
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aup<-round(aci[2:length(levels(g)),2],dec)
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aup<-round(aci[2:length(levels(g)),2],dec)
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@ -4,14 +4,12 @@
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\alias{strobe_olr}
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\alias{strobe_olr}
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\title{Print regression results according to STROBE}
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\title{Print regression results according to STROBE}
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\usage{
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\usage{
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strobe_olr(meas, var, adj, data, dec = 2)
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strobe_olr(meas, vars, data, dec = 2)
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}
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}
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\arguments{
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\arguments{
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\item{meas}{outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
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\item{meas}{outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
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\item{var}{exposure variable to compare against (active vs placebo). As string.}
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\item{vars}{variables to compare against. As vector of columnnames.}
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\item{adj}{variables to adjust for, as string.}
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\item{data}{dataframe of data.}
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\item{data}{dataframe of data.}
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