new functions with new names to substitute old "strobe"-functions

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: daDoctoR
 Title: Functions For Health Research
-Version: 0.21.4
+Version: 0.21.6
 Year: 2021
 Author: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
 Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>

NAMESPACE

@@ -18,6 +18,11 @@ export(hwe_geno)
 export(hwe_sum)
 export(plot_biv_olr)
 export(plot_ord_odds)
+export(print_diff_bygroup)
+export(print_diff_byvar)
+export(print_log)
+export(print_olr)
+export(print_pred)
 export(print_reg_diff_bin)
 export(quantile_cut)
 export(redcap_clean_csv)

R/print_diff_bygroup.R

@@ -0,0 +1,148 @@
+#' REWRITE UNDERWAY
+#'
+#' Print regression results according to STROBE
+#'
+#' Printable table of two dimensional regression analysis of group vs variable for outcome measure. By group. Includes p-value
+#' Group and variable has to be dichotomous factor.
+#' @param meas outcome measure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
+#' @param var binary exposure variable to compare against (active vs placebo). As string.
+#' @param group binary group to compare, as string.
+#' @param adj variables to adjust for, as string.
+#' @param data dataframe to subset from.
+#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.
+#' @keywords strobe
+#' @export
+#' @examples
+#'   data('mtcars')
+#'   mtcars$vs<-factor(mtcars$vs)
+#'   mtcars$am<-factor(mtcars$am)
+#'   strobe_diff_bygroup(meas="mpg",var="vs",group = "am",adj=c("disp","wt"),data=mtcars)
+
+print_diff_bygroup<-function(meas,var,group,adj,data,dec=2){
+
+  ## meas: sdmt
+  ## var: rtreat
+  ## group: genotype
+  ## for dichotome exposure variable (var)
+
+    d <- data
+    m <- d[, c(meas)]
+    v <- d[, c(var)]
+    g <- d[, c(group)]
+    ads <- d[, c(adj)]
+    dat <- data.frame(m, v, g, ads)
+    df <- data.frame(matrix(ncol = 9))
+    if (!is.factor(m)) {
+      for (i in 1:length(levels(g))) {
+        grp <- levels(dat$g)[i]
+        di <- dat[dat$g == grp, ][, -3]
+        mod <- lm(m ~ v, data = di)
+
+        p <- coef(summary(mod))[2,4]
+        p<-ifelse(p<0.001,"<0.001",round(p,3))
+        p <- ifelse(p<=0.05|p=="<0.001",paste0("*",p),
+                    ifelse(p>0.05&p<=0.1,paste0(".",p),p))
+        pv<-p
+
+        co<-round(coef(mod),dec)[2]
+        ci<-round(confint(mod),dec)[2,]
+        lo<-ci[1]
+        up<-ci[2]
+        ci<-paste0(co," (",lo," to ",up,")")
+
+        amod <- lm(m ~ ., data = di)
+        pa <- coef(summary(amod))[2,4]
+        pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
+        pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
+                     ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
+        apv<-pa
+
+        aco<-round(coef(amod),dec)[2]
+        aci<-round(confint(amod),dec)[2,]
+        alo<-aci[1]
+        aup<-aci[2]
+        aci<-paste0(aco," (",alo," to ",aup,")")
+
+        nr <- c()
+        for (r in 1:2) {
+          vr <- levels(di$v)[r]
+          dr <- di[di$v == vr, ]
+          n <- as.numeric(nrow(dr[!is.na(dr$m), ]))
+          mean <- round(mean(dr$m, na.rm = TRUE), dec -
+                          1)
+          sd <- round(sd(dr$m, na.rm = TRUE), dec - 1)
+          ms <- paste0(mean, " (", sd, ")")
+          nr <- c(nr, n, ms)
+        }
+        irl <- c(grp, nr, ci, pv, aci, apv)
+        df <- rbind(df, irl)
+        names(df) <- c("grp",
+                       paste0("N.", substr(levels(v)[1], 1, 3)),
+                       paste0("M.", substr(levels(v)[1], 1, 3)),
+                       paste0("N.", substr(levels(v)[2], 1, 3)),
+                       paste0("M.", substr(levels(v)[2], 1, 3)),
+                       "diff",
+                       "pval",
+                       "ad.diff",
+                       "ad.pval")
+      }
+    }
+    if (is.factor(m)) {
+      for (i in 1:length(levels(g))) {
+        grp <- levels(dat$g)[i]
+        di <- dat[dat$g == grp, ][, -3]
+
+        mod <- glm(m ~ v, family = binomial(), data = di)
+
+        p <- coef(summary(mod))[2,4]
+        p<-ifelse(p<0.001,"<0.001",round(p,3))
+        p <- ifelse(p<=0.05|p=="<0.001",paste0("*",p),
+                    ifelse(p>0.05&p<=0.1,paste0(".",p),p))
+        pv<-p
+
+        co <- round(exp(coef(mod)[-1]), dec)
+        ci<-round(exp(confint(mod)),dec)[2,]
+        lo<-ci[1]
+        up<-ci[2]
+        ci <- paste0(co, " (", lo, " to ", up, ")")
+
+        amod <- glm(m ~ ., family = binomial(), data = di)
+
+        pa <- coef(summary(amod))[2,4]
+        pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
+        pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
+                     ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
+        apv<-pa
+
+        aco <- round(exp(coef(amod)[2]), dec)
+        aci<-suppressMessages(round(exp(confint(amod)),dec))[2,]
+        alo<-aci[1]
+        aup<-aci[2]
+        aci <- paste0(aco, " (", alo, " to ", aup, ")")
+        nr <- c()
+
+        for (r in 1:2) {
+          vr <- levels(di$v)[r]
+          dr <- di[di$v == vr, ]
+          n <- as.numeric(nrow(dr[!is.na(dr$m), ]))
+          nl <- levels(m)[2]
+          out <- nrow(dr[dr$m == nl & !is.na(dr$m), ])
+          pro <- round(out/n * 100, 0)
+          rt <- paste0(out, " (", pro, "%)")
+          nr <- c(nr, n, rt)
+        }
+        irl <- c(grp, nr, ci, pv, aci, apv)
+        df <- rbind(df, irl)
+        names(df) <- c("grp",
+                       paste0("N.", substr(levels(v)[1], 1, 3)),
+                       paste0(nl, ".", substr(levels(v)[1], 1, 3)),
+                       paste0("N.", substr(levels(v)[2], 1, 3)),
+                       paste0(nl, ".", substr(levels(v)[2], 1, 3)),
+                       "OR",
+                       "pval",
+                       "ad.OR",
+                       "ad.pval")
+      }
+    }
+    return(df)
+  }

R/print_diff_byvar.R

@@ -0,0 +1,144 @@
+#' REWRITE UNDERWAY
+#'
+#' Print regression results according to STROBE
+#'
+#' Printable table of three dimensional regression analysis of group vs var for meas. By var. Includes p-values.
+#' @param meas outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
+#' @param var binary exposure variable to compare against (active vs placebo). As string.
+#' @param group groups to compare, as string.
+#' @param adj variables to adjust for, as string.
+#' @param data dataframe of data.
+#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
+#' @keywords strobe
+#' @export
+#' @examples
+#'   data('mtcars')
+#'   mtcars$vs<-factor(mtcars$vs)
+#'   mtcars$am<-factor(mtcars$am)
+#'   strobe_diff_byvar(meas="mpg",var="vs",group = "am",adj=c("disp","wt","hp"),data=mtcars)
+
+print_diff_byvar<-function(meas,var,group,adj,data,dec=2){
+
+  ## meas: sdmt
+  ## var: rtreat
+  ## group: genotype
+  ## for dichotome exposure variable (var)
+
+  d <- data
+  m <- d[, c(meas)]
+  v <- d[, c(var)]
+  g <- d[, c(group)]
+  ads <- d[, c(adj)]
+  dat <- data.frame(m, v, g, ads)
+  df <- data.frame(grp = c(NA, as.character(levels(g))))
+  if (!is.factor(m)) {
+    for (i in 1:length(levels(v))) {
+      grp <- levels(dat$v)[i]
+      di <- dat[dat$v == grp, ][, -2]
+      mod <- lm(m ~ g, data = di)
+
+      p <- coef(summary(mod))[2:length(levels(g)),4]
+      p<-ifelse(p<0.001,"<0.001",round(p,3))
+      p <- ifelse(p<=0.05|p=="<0.001",paste0("*",p),
+                  ifelse(p>0.05&p<=0.1,paste0(".",p),p))
+      pv<-c("-",p)
+
+      co <- c("-", round(coef(mod)[-1], dec))
+      ci<-round(confint(mod),dec)[2:length(levels(g)),]
+      lo <- c("-", ci[,1])
+      up <- c("-", ci[,2])
+      ci <- paste0(co, " (", lo, " to ", up, ")")
+
+      amod <- lm(m ~ ., data = di)
+
+      pa <- coef(summary(amod))[2:length(levels(g)),4]
+      pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
+      pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
+                   ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
+      apv<-c("-",pa)
+
+      aco <- c("-", round(coef(amod)[2:length(levels(g))],
+                          dec))
+      aci<-round(confint(amod),dec)[2:length(levels(g)),]
+      alo <- c("-", aci[,1])
+      aup <- c("-", aci[,2])
+      aci <- paste0(aco, " (", alo, " to ", aup, ")")
+      nr <- c()
+      for (r in 1:length(levels(g))) {
+        vr <- levels(di$g)[r]
+        dr <- di[di$g == vr, ]
+        n <- as.numeric(nrow(dr[!is.na(dr$m), ]))
+        mean <- round(mean(dr$m, na.rm = TRUE), dec -
+                        1)
+        sd <- round(sd(dr$m, na.rm = TRUE), dec - 1)
+        ms <- paste0(mean, " (", sd, ")")
+        nr <- c(nr, n, ms)
+      }
+      irl <- rbind(matrix(grp, ncol = 6), cbind(matrix(nr,
+                                                       ncol = 2, byrow = TRUE), cbind(ci,pv, aci,apv)))
+      colnames(irl) <- c("N",
+                         "Mean (SD)",
+                         "Difference",
+                         "p-value",
+                         "Adjusted Difference",
+                         "Adjusted p-value")
+      df <- cbind(df, irl)
+    }
+  }
+  if (is.factor(m)) {
+    for (i in 1:length(levels(v))) {
+      grp <- levels(dat$v)[i]
+      di <- dat[dat$v == grp, ][, -2]
+      mod <- glm(m ~ g, family = binomial(), data = di)
+
+      p <- coef(summary(mod))[2:length(levels(g)),4]
+      p<-ifelse(p<0.001,"<0.001",round(p,3))
+      p <- ifelse(p<=0.05|p=="<0.001",paste0("*",p),
+                  ifelse(p>0.05&p<=0.1,paste0(".",p),p))
+      pv<-c("-",p)
+
+      co <- c("-", round(exp(coef(mod)[-1]), dec))
+      ci <- suppressMessages(round(exp(confint(mod)),dec))[2:length(levels(g)),]
+      lo <- c("-", ci[,1])
+      up <- c("-", ci[,2])
+      ci <- paste0(co, " (", lo, " to ", up, ")")
+
+      amod <- glm(m ~ ., family = binomial(), data = di)
+
+      pa <- coef(summary(amod))[2:length(levels(g)),4]
+      pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
+      pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
+                   ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
+      apv<-c("-",pa)
+
+      aco <- c("-", suppressMessages(round(exp(coef(amod)[2:length(levels(g))]),
+                                           dec)))
+      aci <- suppressMessages(round(exp(confint(mod)),dec)[2:length(levels(g)),])
+      alo <- c("-", aci[,1])
+      aup <- c("-", aci[,2])
+      aci <- paste0(aco, " (", alo, " to ", aup, ")")
+
+      nr <- c()
+      for (r in 1:length(levels(g))) {
+        vr <- levels(di$g)[r]
+        dr <- di[di$g == vr, ]
+        n <- as.numeric(nrow(dr[!is.na(dr$m), ]))
+        nl <- levels(m)[2]
+        out <- nrow(dr[dr$m == nl & !is.na(dr$m), ])
+        pro <- round(out/n * 100, 0)
+        rt <- paste0(out, " (", pro, "%)")
+        nr <- c(nr, n, rt)
+      }
+      irl <- rbind(matrix(grp, ncol = 4), cbind(matrix(nr,
+                                                       ncol = 2, byrow = TRUE), cbind(ci,pv, aci,apv)))
+      colnames(irl) <- c("N",
+                         paste0("N.", nl),
+                         "OR",
+                         "p-value",
+                         "Adjusted OR",
+                         "Adjusted p-value")
+      df <- cbind(df, irl)
+    }
+  }
+  return(df)
+}

R/print_log.R

@@ -0,0 +1,139 @@
+#' Print regression results according to STROBE
+#'
+#' Printable table of logistic regression analysis according to STROBE.
+#' @param meas outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
+#' @param var exposure variable to compare against (active vs placebo). As string.
+#' @param adj variables to adjust for, as string.
+#' @param data dataframe of data.
+#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
+#' @keywords logistic
+#' @export
+
+print_log<-function(meas,var,adj,data,dec=2){
+  ## Ønskeliste:
+  ##
+  ## - Sum af alle, der indgår (Overall N)
+  ## - Ryd op i kode, der der er overflødig %-regning, alternativt, så fiks at NA'er ikke skal regnes med.
+  ##
+
+  require(dplyr)
+
+  d<-data
+  m<-d[,c(meas)]
+  v<-d[,c(var)]
+
+  ads<-d[,c(adj)]
+  dat<-data.frame(m,v)
+  df<-data.frame(matrix(ncol=4))
+
+  mn <- glm(m ~ .,family = binomial(), data = dat)
+
+  dat<-data.frame(dat,ads)
+  ma <- glm(m ~ .,family = binomial(), data = dat)
+
+  ctable <- coef(summary(mn))
+  pa <- ctable[, 4]
+  pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
+  pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
+               ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
+  pv<-c("REF",pa[2:length(coef(mn))])
+
+  co<-round(exp(coef(mn)),dec)[-1]
+  ci<-round(exp(confint(mn)),dec)[-1,]
+  lo<-ci[,1]
+  up<-ci[,2]
+
+  or_ci<-c("REF",paste0(co," (",lo," to ",up,")"))
+
+  nr<-c()
+
+  for (r in 1:length(levels(dat[,2]))){
+    vr<-levels(dat[,2])[r]
+    dr<-dat[dat[,2]==vr,]
+    n<-as.numeric(nrow(dr))
+
+    ## Af en eller anden grund bliver der talt for mange med.
+    # nall<-as.numeric(nrow(dat[!is.na(dat[,2]),]))
+    nl<-levels(m)[r]
+    # pro<-round(n/nall*100,0)
+    # rt<-paste0(n," (",pro,"%)")
+    nr<-rbind(nr,cbind(nl,n))
+  }
+
+  mms<-data.frame(cbind(nr,or_ci,pv))
+  header<-data.frame(matrix(var,ncol = ncol(mms)))
+  names(header)<-names(mms)
+
+  ls<-list(unadjusted=data.frame(rbind(header,mms)))
+
+  actable <- coef(summary(ma))
+  pa <- actable[,4]
+  pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
+  pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
+               ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
+
+  apv<-pa[1:length(coef(ma))]
+
+  aco<-round(exp(coef(ma)),dec)
+  aci<-round(exp(confint(ma)),dec)
+  alo<-aci[,1]
+  aup<-aci[,2]
+  aor_ci<-paste0(aco," (",alo," to ",aup,")")
+
+  dat2<-dat[,-1]
+  # names(dat2)<-c(var,names(ads))
+  nq<-c()
+
+  for (i in 1:ncol(dat2)){
+    if (is.factor(dat2[,i])){
+      vec<-dat2[,i]
+      ns<-names(dat2)[i]
+      for (r in 1:length(levels(vec))){
+        vr<-levels(vec)[r]
+        dr<-vec[vec==vr]
+        n<-as.numeric(length(dr))
+        # nall<-as.numeric(nrow(dat[!is.na(dat2[,c(ns)]),]))
+        nl<-paste0(ns,levels(vec)[r])
+        # pro<-round(n/nall*100,0)
+        # rt<-paste0(n," (",pro,"%)")
+        nq<-rbind(nq,cbind(nl,n))
+      }
+    }
+    if (!is.factor(dat2[,i])){
+      num<-dat2[,i]
+      ns<-names(dat2)[i]
+      nall<-as.numeric(nrow(dat[!is.na(dat2[,c(ns)]),]))
+      nq<-rbind(nq,cbind(ns,nall))
+    }
+  }
+
+  rnames<-c()
+
+  for (i in 1:ncol(dat2)){
+    if (is.factor(dat2[,i])){
+      rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
+    }
+    if (!is.factor(dat2[,i])){
+      rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
+    }
+  }
+  res<-cbind(aor_ci,apv)
+  rest<-data.frame(names=row.names(res),res,stringsAsFactors = F)
+
+  numb<-data.frame(names=nq[,c("nl")],N=nq[,c("n")],stringsAsFactors = F)
+  namt<-data.frame(names=rnames,stringsAsFactors = F)
+
+  coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
+
+  header<-data.frame(matrix("Adjusted",ncol = ncol(coll)))
+  names(header)<-names(coll)
+
+  ls$adjusted<-data.frame(rbind(header,coll))
+
+  fnames<-c("Variable","N","OR (95 % CI)","p value")
+
+  names(ls$unadjusted)<-fnames
+  names(ls$adjusted)<-fnames
+
+  return(ls)
+}

R/print_olr.R

@@ -0,0 +1,174 @@
+#' Print ordinal logistic regression results according to STROBE
+#'
+#' Printable table of ordinal logistic regression with bivariate and multivariate analyses.
+#' Table according to STROBE. Uses polr() funtion of the MASS-package.
+#' Formula analysed is the most simple m~v1+v2+vn. The is no significance test. Results are point estimates with 95 percent CI.
+#' @param meas outcome meassure variable name or response in data-data.frame as a string. Should be factor, preferably ordered.
+#' @param vars variables to compare against. As vector of columnnames.
+#' @param data dataframe of data.
+#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
+#' @param n.by.adj flag to indicate wether to count number of patients in adjusted model or overall for outcome meassure not NA.
+#' @keywords olr
+#' @export
+
+print_olr<-function(meas,vars,data,dec=2,n.by.adj=FALSE){
+## For calculation of p-value from t-value see rep_olr()
+
+  require(MASS)
+  require(dplyr)
+
+  d<-data
+  m<-d[,c(meas)]
+
+  ads<-d[,c(vars)]
+
+  if(!is.factor(m)){stop("'meas' should be a factor, preferably ordered.")}
+
+  if(is.factor(m)){
+
+    ## Crude ORs
+
+    dfcr<-data.frame(matrix(NA,ncol = 2))
+    names(dfcr)<-c("pred","or_ci")
+    n.mn<-c()
+    nref<-c()
+
+    for(i in 1:ncol(ads)){
+      dat<-data.frame(m=m,ads[,i])
+      names(dat)<-c("m",names(ads)[i])
+      mn<-polr(m ~ ., data = dat, Hess=TRUE)
+      n.mn<-c(n.mn,nrow(mn$model))
+
+      suppressMessages(ci<-matrix(exp(confint(mn)),ncol=2))
+      l<-round(ci[,1],dec)
+      u<-round(ci[,2],dec)
+      or<-round(exp(coef(mn)),dec)
+      or_ci<-paste0(or," (",l," to ",u,")")
+
+      x1<-ads[,i]
+
+      if (is.factor(x1)){
+        pred<-paste0(names(ads)[i],levels(x1)[-1])
+      }
+
+      else {
+        pred<-names(ads)[i]
+      }
+
+      dfcr<-rbind(dfcr,cbind(pred,or_ci))
+    }
+
+
+    ## Mutually adjusted  ORs
+
+    dat<-data.frame(m=m,ads)
+    ma <-polr(m ~ ., data = dat, Hess=TRUE)
+    miss<-length(ma$na.action)
+
+    aco<-round(exp(coef(ma)),dec)
+    suppressMessages(aci<-round(exp(confint(ma)),dec))
+    alo<-aci[,1]
+    aup<-aci[,2]
+    aor_ci<-paste0(aco," (",alo," to ",aup,")")
+
+    nq<-c()
+
+    if (n.by.adj==TRUE){
+      dat2<-ma$model[,-1]
+      for (i in 1:ncol(dat2)){
+        if (is.factor(dat2[,i])){
+          vec<-dat2[,i]
+          ns<-names(dat2)[i]
+          for (r in 1:length(levels(vec))){
+            vr<-levels(vec)[r]
+            n<-as.numeric(length(vec[vec==vr&!is.na(vec)]))
+            nall<-as.numeric(length(dat2[,c(ns)]))
+            n.meas<-nall
+            nl<-paste0(ns,levels(vec)[r])
+            pro<-round(n/nall*100,0)
+            rt<-paste0(n," (",pro,"%)")
+            nq<-rbind(nq,cbind(nl,rt))
+          }}
+        if (!is.factor(dat2[,i])){
+          num<-dat2[,i]
+          nl<-names(dat2)[i]
+          n<-as.numeric(length(num[!is.na(num)]))
+          nall<-as.numeric(nrow(dat2))
+          n.meas<-nall
+          pro<-round(n/nall*100,0)
+          rt<-paste0(n," (",pro,"%)")
+          nq<-rbind(nq,cbind(nl,rt))
+        }}}
+
+    else {
+      dat2<-dat[!is.na(dat[,1]),][,-1]
+      n.meas<-nrow(dat2)
+      for (i in 1:ncol(dat2)){
+        if (is.factor(dat2[,i])){
+          vec<-dat2[,i]
+          ns<-names(dat2)[i]
+          for (r in 1:length(levels(vec))){
+            vr<-levels(vec)[r]
+            n<-as.numeric(length(vec[vec==vr&!is.na(vec)]))
+            nall<-as.numeric(n.mn[i])
+            nl<-paste0(ns,levels(vec)[r])
+            pro<-round(n/nall*100,0)
+            rt<-paste0(n," (",pro,"%)")
+            nq<-rbind(nq,cbind(nl,rt))
+          }}
+        if (!is.factor(dat2[,i])){
+          num<-dat2[,i]
+          nl<-names(dat2)[i]
+          n<-as.numeric(length(num[!is.na(num)]))
+          nall<-as.numeric(n.meas)
+          pro<-round(n/nall*100,0)
+          rt<-paste0(n," (",pro,"%)")
+          nq<-rbind(nq,cbind(nl,rt))
+        }}
+    }
+
+    rnames<-c()
+    for (i in 1:ncol(dat2)){
+      if (is.factor(dat2[,i])){
+        rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
+      }
+      if (!is.factor(dat2[,i])){
+        rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
+      }
+    }
+    rest<-data.frame(names=names(aco),aor_ci,stringsAsFactors = F)
+
+    numb<-data.frame(names=nq[,c("nl")],N=nq[,c("rt")],stringsAsFactors = F)
+    namt<-data.frame(names=rnames,stringsAsFactors = F)
+
+    coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
+
+    header<-data.frame(matrix(paste0("Chance of higher ",meas),ncol = ncol(coll)),stringsAsFactors = F)
+    names(header)<-names(coll)
+
+    df<-data.frame(rbind(header,coll),stringsAsFactors = F)
+
+    names(dfcr)[1]<-c("names")
+
+    suppressWarnings(re<-left_join(df,dfcr,by="names"))
+
+    rona<-c()
+    for (i in 1:length(ads)){
+      if (is.factor(ads[,i])){
+        rona<-c(rona,names(ads[i]),levels(ads[,i]))}
+      if (!is.factor(ads[,i])){
+        rona<-c(rona,names(ads[i]),"Per unit increase")
+      }
+    }
+
+    ref<-data.frame(c(NA,rona),re[,2],re[,4],re[,3])
+
+    names(ref)<-c("Variable",paste0("N=",n.meas),"Bivariate OLR (95 % CI)","Mutually adjusted OLR (95 % CI)")
+
+    ls<-list(tbl=ref,miss,n.meas,nrow(d))
+    names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe")
+  }
+
+
+  return(ls)
+}

R/print_pred.R

@@ -0,0 +1,358 @@
+#' Regression model of predictors according to STROBE, bi- and multivariable.
+#'
+#' Printable table of regression model according to STROBE for linear or binary outcome-variables.
+#' Includes both bivariate and multivariate in the same table.
+#' Output is a list, with the first item being the main "output" as a dataframe.
+#' Automatically uses logistic regression model for dichotomous outcome variable and linear regression model for continuous outcome variable. Linear regression will give estimated adjusted true mean in list.
+#' For logistic regression gives count of outcome variable pr variable level.
+#' @param meas binary outcome measure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
+#' @param adj variables to adjust for, as string.
+#' @param data dataframe of data.
+#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
+#' @param n.by.adj flag to indicate whether to count number of patients in adjusted model or overall for outcome measure not NA.
+#' @param p.val flag to include p-values in table, set to FALSE as standard.
+#' @keywords logistic
+#' @export
+
+print_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
+
+## Wish list:
+  ## - SPEED, maybe flags to include/exclude time consuming tasks
+  ## - Include ANOVA in output list, flag to include
+
+  require(dplyr)
+
+  d<-data
+  m<-d[,c(meas)]
+
+  ads<-d[,c(adj)]
+
+  if(is.factor(m)){
+
+    ## Crude ORs
+
+    dfcr<-data.frame(matrix(NA,ncol = 3))
+    names(dfcr)<-c("pred","or_ci","pv")
+    n.mn<-c()
+
+    nref<-c()
+
+    for(i in 1:ncol(ads)){
+      dat<-data.frame(m=m,ads[,i])
+      names(dat)<-c("m",names(ads)[i])
+      mn<-glm(m~.,family = binomial(),data=dat)
+      n.mn<-c(n.mn,nrow(mn$model))
+
+      suppressMessages(ci<-exp(confint(mn)))
+      l<-round(ci[-1,1],dec)
+      u<-round(ci[-1,2],dec)
+      or<-round(exp(coef(mn))[-1],dec)
+      or_ci<-paste0(or," (",l," to ",u,")")
+      pv<-round(tidy(mn)$p.value[-1],dec+1)
+      x1<-ads[,i]
+
+      if (is.factor(x1)){
+        pred<-paste0(names(ads)[i],levels(x1)[-1])
+      }
+
+      else {
+        pred<-names(ads)[i]
+      }
+
+      dfcr<-rbind(dfcr,cbind(pred,or_ci,pv))
+    }
+
+
+    ## Mutually adjusted  ORs
+
+    dat<-data.frame(m=m,ads)
+    ma <- glm(m ~ .,family = binomial(), data = dat)
+    miss<-length(ma$na.action)
+
+    actable <- coef(summary(ma))
+    pa <- actable[,4]
+    pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
+    pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
+                 ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
+
+    apv<-pa[1:length(coef(ma))]
+
+    aco<-round(exp(coef(ma)),dec)
+    suppressMessages(aci<-round(exp(confint(ma)),dec))
+    alo<-aci[,1]
+    aup<-aci[,2]
+    aor_ci<-paste0(aco," (",alo," to ",aup,")")
+
+    # names(dat2)<-c(var,names(ads))
+
+    nq<-c()
+    nall<-length(!is.na(dat[,1]))
+
+    if (n.by.adj==TRUE){
+      dat2<-ma$model
+      # nalt<-nrow(dat2)
+      for (i in 2:ncol(dat2)) {
+        if (is.factor(dat2[, i])) {
+          vec <- dat2[, i]
+          ns <- names(dat2)[i]
+          for (r in 1:length(levels(vec))) {
+            vr <- levels(vec)[r]
+            ## Counting all included in analysis
+            n <- length(vec[vec == vr & !is.na(vec)])
+            rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
+            ## Counting all included in analysis with outcome
+            lvl<-levels(dat2[,1])[2]
+            no <- length(vec[vec == vr & dat2[,1]==lvl & !is.na(vec)])
+            ro <- paste0(no, " (", round(no/n * 100, 0), "%)")
+            ## Combining
+            nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt,ro))
+          }
+        }
+        if (!is.factor(dat2[, i])) {
+          num <- dat2[, i]
+          n <- length(num[!is.na(num)])
+          rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
+          nq <- rbind(nq, cbind(names(dat2)[i], rt,ro="-"))
+        }
+      }
+    }
+
+    else {
+      dat2<-dat[!is.na(dat[,1]),]
+      for (i in 2:ncol(dat2)) {
+        if (is.factor(dat2[, i])) {
+          vec <- dat2[, i]
+          ns <- names(dat2)[i]
+          for (r in 1:length(levels(vec))) {
+            vr <- levels(vec)[r]
+            ## Counting all included in analysis
+            n <- length(vec[vec == vr & !is.na(vec)])
+            rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
+            ## Counting all included in analysis with outcome
+            lvl<-levels(dat2[,1])[2]
+            no <- length(vec[vec == vr & dat2[,1]==lvl & !is.na(vec)])
+            ro <- paste0(no, " (", round(no/n * 100, 0), "%)")
+            ## Combining
+            nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt,ro))
+          }
+        }
+        if (!is.factor(dat2[, i])) {
+          num <- dat2[, i]
+          n <- length(num[!is.na(num)])
+          rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
+          nq <- rbind(nq, cbind(names(dat2)[i], rt,ro="-"))
+        }
+      }
+    }
+
+    rnames<-c()
+    for (i in 1:ncol(dat2)){
+      if (is.factor(dat2[,i])){
+        rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
+      }
+      if (!is.factor(dat2[,i])){
+        rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
+      }
+    }
+    res<-cbind(aor_ci,apv)
+    rest<-data.frame(names=row.names(res),res,stringsAsFactors = F)
+
+    numb<-data.frame(names=nq[,1],N=nq[,2],N.out=nq[,3],stringsAsFactors = F)
+    namt<-data.frame(names=tail(rnames,-3),stringsAsFactors = F)
+
+    coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
+
+    header<-data.frame(matrix(paste0("Chance of ",meas," is ",levels(m)[2]),ncol = ncol(coll)),stringsAsFactors = F)
+    names(header)<-names(coll)
+
+    df<-data.frame(rbind(header,coll),stringsAsFactors = F)
+
+    names(dfcr)[1]<-c("names")
+
+    suppressWarnings(re<-left_join(df,dfcr,by="names"))
+
+    rona<-c()
+    for (i in 1:length(ads)){
+      if (is.factor(ads[,i])){
+        rona<-c(rona,names(ads[i]),levels(ads[,i]))}
+      if (!is.factor(ads[,i])){
+        rona<-c(rona,names(ads[i]),"Per unit increase")
+      }
+    }
+
+    if (p.val==TRUE){
+      ref<-data.frame(c(NA,rona),re[,"N"],re[,"N.out"],re[,"or_ci"],re[,"pv"],re[,"aor_ci"],re[,"apv"])
+
+      names(ref)<-c("Variable",paste0("N=",nall),paste0("N, ",meas," is ",levels(m)[2]),"Crude OR (95 % CI)","p-value","Mutually adjusted OR (95 % CI)","A p-value")
+    }
+    else{
+      ref<-data.frame(c(NA,rona),re[,"N"],re[,"N.out"],re[,"or_ci"],re[,"aor_ci"])
+
+      names(ref)<-c("Variable",paste0("N=",nall),paste0("N, ",meas," is ",levels(m)[2]),"Crude OR (95 % CI)","Mutually adjusted OR (95 % CI)")
+    }
+
+    ls<-list(tbl=ref,miss,nall,nrow(d))
+    names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe")
+  }
+
+  if(!is.factor(m)){
+
+    dfcr<-data.frame(matrix(NA,ncol = 3))
+    names(dfcr)<-c("pred","dif_ci","pv")
+    n.mn<-c()
+
+    nref<-c()
+
+    for(i in 1:ncol(ads)){
+      dat<-data.frame(m=m,ads[,i])
+      names(dat)<-c("m",names(ads)[i])
+      mn<-lm(m~.,data=dat)
+      n.mn<-c(n.mn,nrow(mn$model))
+
+      suppressMessages(ci<-confint(mn))
+      l<-round(ci[-1,1],dec)
+      u<-round(ci[-1,2],dec)
+      dif<-round(coef(mn)[-1],dec)
+      dif_ci<-paste0(dif," (",l," to ",u,")")
+      pv<-round(tidy(mn)$p.value[-1],dec+1)
+      pv<-ifelse(pv<0.001,"<0.001",round(pv,3))
+      pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
+                   ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
+
+      x1<-ads[,i]
+
+      if (is.factor(x1)){
+        pred<-paste0(names(ads)[i],levels(x1)[-1])
+      }
+
+      else {
+        pred<-names(ads)[i]
+      }
+
+      dfcr<-rbind(dfcr,cbind(pred,dif_ci,pv))
+    }
+
+    ## Mutually adjusted  ORs
+
+    dat<-data.frame(m=m,ads)
+    ma <- lm(m ~ ., data = dat)
+    miss<-length(ma$na.action)
+
+
+    actable <- coef(summary(ma))
+    pa <- actable[,4]
+    pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
+    pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
+                 ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
+
+    apv<-pa[1:length(coef(ma))]
+
+    aco<-round(coef(ma),dec)
+    suppressMessages(aci<-round(confint(ma),dec))
+    alo<-aci[,1]
+    aup<-aci[,2]
+    amean_ci<-paste0(aco," (",alo," to ",aup,")")
+
+    mean_est<-amean_ci[[1]]
+
+
+    nq<-c()
+    nall<-length(!is.na(dat[,1]))
+
+    if (n.by.adj==TRUE){
+      dat2<-ma$model[,-1]
+      # nalt<-nrow(dat2)
+      for (i in 1:ncol(dat2)){
+        if (is.factor(dat2[,i])){
+          vec<-dat2[,i]
+          ns<-names(dat2)[i]
+          for (r in 1:length(levels(vec))){
+            vr<-levels(vec)[r]
+            n<-length(vec[vec==vr&!is.na(vec)])
+            rt<-paste0(n," (",round(n/nall*100,0),"%)")
+            nq<-rbind(nq,cbind(paste0(ns,levels(vec)[r]),rt))
+          }}
+        if (!is.factor(dat2[,i])){
+          num<-dat2[,i]
+          n<-as.numeric(length(num[!is.na(num)]))
+          rt<-paste0(n," (",round(n/nall*100,0),"%)")
+          nq<-rbind(nq,cbind(names(dat2)[i],rt))
+        }}
+    }
+
+    else {
+      dat2<-dat[!is.na(dat[,1]),][,-1]
+      for (i in 1:ncol(dat2)) {
+        if (is.factor(dat2[, i])) {
+          vec <- dat2[, i]
+          ns <- names(dat2)[i]
+          for (r in 1:length(levels(vec))) {
+            vr <- levels(vec)[r]
+            n <- length(vec[vec == vr & !is.na(vec)])
+            rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
+            nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt))
+          }
+        }
+        if (!is.factor(dat2[, i])) {
+          num <- dat2[, i]
+          n <- length(num[!is.na(num)])
+          rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
+          nq <- rbind(nq, cbind(names(dat2)[i], rt))
+        }
+      }
+    }
+
+
+    rnames<-c()
+    for (i in 1:ncol(dat2)){
+      if (is.factor(dat2[,i])){
+        rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
+      }
+      if (!is.factor(dat2[,i])){
+        rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
+      }
+    }
+    res<-cbind(amean_ci,apv)
+    rest<-data.frame(names=row.names(res),res,stringsAsFactors = F)
+
+    numb<-data.frame(names=nq[,1],N=nq[,2],stringsAsFactors = F)
+    namt<-data.frame(names=rnames,stringsAsFactors = F)
+
+    coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
+
+    header<-data.frame(matrix("Adjusted",ncol = ncol(coll)),stringsAsFactors = F)
+    names(header)<-names(coll)
+
+    df<-data.frame(rbind(header,coll),stringsAsFactors = F)
+
+    names(dfcr)[1]<-c("names")
+
+    suppressWarnings(re<-left_join(df,dfcr,by="names"))
+
+    rona<-c()
+    for (i in 1:length(ads)){
+      if (is.factor(ads[,i])){
+        rona<-c(rona,names(ads[i]),levels(ads[,i]))}
+      if (!is.factor(ads[,i])){
+        rona<-c(rona,names(ads[i]),"Per unit increase")
+      }
+    }
+
+    if (p.val==TRUE){
+      ref<-data.frame(c(NA,rona),re[,2],re[,5],re[,6],re[,3],re[,4])
+
+      names(ref)<-c("Variable",paste0("N=",nall),"Difference (95 % CI)","p-value","Mutually adjusted difference (95 % CI)","A p-value")
+    }
+    else{
+      ref<-data.frame(c(NA,rona),re[,2],re[,5],re[,3])
+
+      names(ref)<-c("Variable",paste0("N=",nall),"Difference (95 % CI)","Mutually adjusted difference (95 % CI)")
+    }
+
+    ls<-list(tbl=ref,miss,nall,nrow(d),mean_est)
+    names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe","Estimated true mean (95 % CI) in adjusted analysis")
+
+  }
+
+  return(ls)
+}

R/strobe_diff_bygroup.R

@@ -1,4 +1,4 @@
-#' REWRITE UNDERWAY
+#' REWRITE UNDERWAY - replaced by 'print_diff_bygroup'
 #'
 #' Print regression results according to STROBE
 #'

R/strobe_diff_byvar.R

@@ -1,4 +1,4 @@
-#' REWRITE UNDERWAY
+#' REWRITE UNDERWAY - replaced by 'print_diff_byvar'
 #'
 #' Print regression results according to STROBE
 #'

R/strobe_log.R

@@ -1,3 +1,5 @@
+#' OBSOLETE - use 'print_log'
+#'
 #' Print regression results according to STROBE
 #'
 #' Printable table of logistic regression analysis according to STROBE.

R/strobe_olr.R

@@ -1,3 +1,5 @@
+#' OBSOLETE - use 'print_olr'
+#'
 #' Print ordinal logistic regression results according to STROBE
 #'
 #' Printable table of ordinal logistic regression with bivariate and multivariate analyses.

R/strobe_pred.R

@@ -1,3 +1,5 @@
+#' OBSOLETE - use 'print_pred'
+#'
 #' Regression model of predictors according to STROBE, bi- and multivariable.
 #'
 #' Printable table of regression model according to STROBE for linear or binary outcome-variables.

man/print_diff_bygroup.Rd

@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/print_diff_bygroup.R
+\name{print_diff_bygroup}
+\alias{print_diff_bygroup}
+\title{REWRITE UNDERWAY}
+\usage{
+print_diff_bygroup(meas, var, group, adj, data, dec = 2)
+}
+\arguments{
+\item{meas}{outcome measure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
+
+\item{var}{binary exposure variable to compare against (active vs placebo). As string.}
+
+\item{group}{binary group to compare, as string.}
+
+\item{adj}{variables to adjust for, as string.}
+
+\item{data}{dataframe to subset from.}
+
+\item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.}
+}
+\description{
+Print regression results according to STROBE
+}
+\details{
+Printable table of two dimensional regression analysis of group vs variable for outcome measure. By group. Includes p-value
+Group and variable has to be dichotomous factor.
+}
+\examples{
+  data('mtcars')
+  mtcars$vs<-factor(mtcars$vs)
+  mtcars$am<-factor(mtcars$am)
+  strobe_diff_bygroup(meas="mpg",var="vs",group = "am",adj=c("disp","wt"),data=mtcars)
+}
+\keyword{strobe}

man/print_diff_byvar.Rd

@@ -0,0 +1,34 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/print_diff_byvar.R
+\name{print_diff_byvar}
+\alias{print_diff_byvar}
+\title{REWRITE UNDERWAY}
+\usage{
+print_diff_byvar(meas, var, group, adj, data, dec = 2)
+}
+\arguments{
+\item{meas}{outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
+
+\item{var}{binary exposure variable to compare against (active vs placebo). As string.}
+
+\item{group}{groups to compare, as string.}
+
+\item{adj}{variables to adjust for, as string.}
+
+\item{data}{dataframe of data.}
+
+\item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1.}
+}
+\description{
+Print regression results according to STROBE
+}
+\details{
+Printable table of three dimensional regression analysis of group vs var for meas. By var. Includes p-values.
+}
+\examples{
+  data('mtcars')
+  mtcars$vs<-factor(mtcars$vs)
+  mtcars$am<-factor(mtcars$am)
+  strobe_diff_byvar(meas="mpg",var="vs",group = "am",adj=c("disp","wt","hp"),data=mtcars)
+}
+\keyword{strobe}

man/print_log.Rd

@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/print_log.R
+\name{print_log}
+\alias{print_log}
+\title{Print regression results according to STROBE}
+\usage{
+print_log(meas, var, adj, data, dec = 2)
+}
+\arguments{
+\item{meas}{outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
+
+\item{var}{exposure variable to compare against (active vs placebo). As string.}
+
+\item{adj}{variables to adjust for, as string.}
+
+\item{data}{dataframe of data.}
+
+\item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1.}
+}
+\description{
+Printable table of logistic regression analysis according to STROBE.
+}
+\keyword{logistic}

man/print_olr.Rd

@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/print_olr.R
+\name{print_olr}
+\alias{print_olr}
+\title{Print ordinal logistic regression results according to STROBE}
+\usage{
+print_olr(meas, vars, data, dec = 2, n.by.adj = FALSE)
+}
+\arguments{
+\item{meas}{outcome meassure variable name or response in data-data.frame as a string. Should be factor, preferably ordered.}
+
+\item{vars}{variables to compare against. As vector of columnnames.}
+
+\item{data}{dataframe of data.}
+
+\item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1.}
+
+\item{n.by.adj}{flag to indicate wether to count number of patients in adjusted model or overall for outcome meassure not NA.}
+}
+\description{
+Printable table of ordinal logistic regression with bivariate and multivariate analyses.
+Table according to STROBE. Uses polr() funtion of the MASS-package.
+Formula analysed is the most simple m~v1+v2+vn. The is no significance test. Results are point estimates with 95 percent CI.
+}
+\keyword{olr}

man/print_pred.Rd

@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/print_pred.R
+\name{print_pred}
+\alias{print_pred}
+\title{Regression model of predictors according to STROBE, bi- and multivariable.}
+\usage{
+print_pred(meas, adj, data, dec = 2, n.by.adj = FALSE, p.val = FALSE)
+}
+\arguments{
+\item{meas}{binary outcome measure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
+
+\item{adj}{variables to adjust for, as string.}
+
+\item{data}{dataframe of data.}
+
+\item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1.}
+
+\item{n.by.adj}{flag to indicate whether to count number of patients in adjusted model or overall for outcome measure not NA.}
+
+\item{p.val}{flag to include p-values in table, set to FALSE as standard.}
+}
+\description{
+Printable table of regression model according to STROBE for linear or binary outcome-variables.
+Includes both bivariate and multivariate in the same table.
+Output is a list, with the first item being the main "output" as a dataframe.
+Automatically uses logistic regression model for dichotomous outcome variable and linear regression model for continuous outcome variable. Linear regression will give estimated adjusted true mean in list.
+For logistic regression gives count of outcome variable pr variable level.
+}
+\keyword{logistic}

man/strobe_diff_bygroup.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/strobe_diff_bygroup.R
 \name{strobe_diff_bygroup}
 \alias{strobe_diff_bygroup}
-\title{REWRITE UNDERWAY}
+\title{REWRITE UNDERWAY - replaced by 'print_diff_bygroup'}
 \usage{
 strobe_diff_bygroup(meas, var, group, adj, data, dec = 2)
 }

man/strobe_diff_byvar.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/strobe_diff_byvar.R
 \name{strobe_diff_byvar}
 \alias{strobe_diff_byvar}
-\title{REWRITE UNDERWAY}
+\title{REWRITE UNDERWAY - replaced by 'print_diff_byvar'}
 \usage{
 strobe_diff_byvar(meas, var, group, adj, data, dec = 2)
 }

man/strobe_log.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/strobe_log.R
 \name{strobe_log}
 \alias{strobe_log}
-\title{Print regression results according to STROBE}
+\title{OBSOLETE - use 'print_log'}
 \usage{
 strobe_log(meas, var, adj, data, dec = 2)
 }
@@ -18,6 +18,9 @@ strobe_log(meas, var, adj, data, dec = 2)
 \item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1.}
 }
 \description{
+Print regression results according to STROBE
+}
+\details{
 Printable table of logistic regression analysis according to STROBE.
 }
 \keyword{logistic}

man/strobe_olr.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/strobe_olr.R
 \name{strobe_olr}
 \alias{strobe_olr}
-\title{Print ordinal logistic regression results according to STROBE}
+\title{OBSOLETE - use 'print_olr'}
 \usage{
 strobe_olr(meas, vars, data, dec = 2, n.by.adj = FALSE)
 }
@@ -18,6 +18,9 @@ strobe_olr(meas, vars, data, dec = 2, n.by.adj = FALSE)
 \item{n.by.adj}{flag to indicate wether to count number of patients in adjusted model or overall for outcome meassure not NA.}
 }
 \description{
+Print ordinal logistic regression results according to STROBE
+}
+\details{
 Printable table of ordinal logistic regression with bivariate and multivariate analyses.
 Table according to STROBE. Uses polr() funtion of the MASS-package.
 Formula analysed is the most simple m~v1+v2+vn. The is no significance test. Results are point estimates with 95 percent CI.

man/strobe_pred.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/strobe_pred.R
 \name{strobe_pred}
 \alias{strobe_pred}
-\title{Regression model of predictors according to STROBE, bi- and multivariable.}
+\title{OBSOLETE - use 'print_pred'}
 \usage{
 strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE, p.val = FALSE)
 }
@@ -20,6 +20,9 @@ strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE, p.val = FALSE)
 \item{p.val}{flag to include p-values in table, set to FALSE as standard.}
 }
 \description{
+Regression model of predictors according to STROBE, bi- and multivariable.
+}
+\details{
 Printable table of regression model according to STROBE for linear or binary outcome-variables.
 Includes both bivariate and multivariate in the same table.
 Output is a list, with the first item being the main "output" as a dataframe.

update local version.R

@@ -5,7 +5,7 @@
 remove.packages("daDoctoR")
 .rs.restartR()
 
-# setwd("/Users/andreas/")
+setwd("/Users")
 
 devtools::install_github('agdamsbo/daDoctoR')