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: 0.21.5
 Year: 2021
 Author: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
 Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>

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)
+
+strobe_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)
+
+strobe_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
+
+strobe_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
+
+strobe_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
+
+strobe_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/strobe_diff_bygroup.Rd

@@ -1,9 +1,11 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/strobe_diff_bygroup.R
+% Please edit documentation in R/print_diff_bygroup.R, R/strobe_diff_bygroup.R
 \name{strobe_diff_bygroup}
 \alias{strobe_diff_bygroup}
 \title{REWRITE UNDERWAY}
 \usage{
+strobe_diff_bygroup(meas, var, group, adj, data, dec = 2)
+
 strobe_diff_bygroup(meas, var, group, adj, data, dec = 2)
 }
 \arguments{
@@ -20,9 +22,14 @@ strobe_diff_bygroup(meas, var, group, adj, data, dec = 2)
 \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
+
 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.
+
 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.
 }
@@ -31,5 +38,9 @@ Group and variable has to be dichotomous factor.
   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)
+  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/strobe_diff_byvar.Rd

@@ -1,9 +1,11 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/strobe_diff_byvar.R
+% Please edit documentation in R/print_diff_byvar.R, R/strobe_diff_byvar.R
 \name{strobe_diff_byvar}
 \alias{strobe_diff_byvar}
 \title{REWRITE UNDERWAY}
 \usage{
+strobe_diff_byvar(meas, var, group, adj, data, dec = 2)
+
 strobe_diff_byvar(meas, var, group, adj, data, dec = 2)
 }
 \arguments{
@@ -20,9 +22,13 @@ strobe_diff_byvar(meas, var, group, 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
+
 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.
+
 Printable table of three dimensional regression analysis of group vs var for meas. By var. Includes p-values.
 }
 \examples{
@@ -30,5 +36,9 @@ Printable table of three dimensional regression analysis of group vs var for mea
   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)
+  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/strobe_log.Rd

@@ -1,9 +1,11 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/strobe_log.R
+% Please edit documentation in R/print_log.R, R/strobe_log.R
 \name{strobe_log}
 \alias{strobe_log}
 \title{Print regression results according to STROBE}
 \usage{
+strobe_log(meas, var, adj, data, dec = 2)
+
 strobe_log(meas, var, adj, data, dec = 2)
 }
 \arguments{
@@ -19,5 +21,10 @@ strobe_log(meas, var, adj, data, dec = 2)
 }
 \description{
 Printable table of logistic regression analysis according to STROBE.
+
+Print regression results according to STROBE
+}
+\details{
+Printable table of logistic regression analysis according to STROBE.
 }
 \keyword{logistic}

man/strobe_olr.Rd

@@ -1,9 +1,11 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/strobe_olr.R
+% Please edit documentation in R/print_olr.R, R/strobe_olr.R
 \name{strobe_olr}
 \alias{strobe_olr}
 \title{Print ordinal logistic regression results according to STROBE}
 \usage{
+strobe_olr(meas, vars, data, dec = 2, n.by.adj = FALSE)
+
 strobe_olr(meas, vars, data, dec = 2, n.by.adj = FALSE)
 }
 \arguments{
@@ -21,5 +23,12 @@ strobe_olr(meas, vars, data, dec = 2, n.by.adj = FALSE)
 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.
+
+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.
 }
 \keyword{olr}

man/strobe_pred.Rd

@@ -1,9 +1,11 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/strobe_pred.R
+% Please edit documentation in R/print_pred.R, R/strobe_pred.R
 \name{strobe_pred}
 \alias{strobe_pred}
 \title{Regression model of predictors according to STROBE, bi- and multivariable.}
 \usage{
+strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE, p.val = FALSE)
+
 strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE, p.val = FALSE)
 }
 \arguments{
@@ -25,5 +27,14 @@ 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.
+
+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.
+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}