mirror of
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new functions with new names to substitute old "strobe"-functions
This commit is contained in:
Andreas Gammelgaard Damsbo
parent
96479031e5
commit
d8ffa3dc7b
@ -1,6 +1,6 @@
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Package: daDoctoR
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Title: Functions For Health Research
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Version: 0.21.4
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Version: 0.21.5
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Year: 2021
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Author: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
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Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
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148
R/print_diff_bygroup.R
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148
R/print_diff_bygroup.R
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#' REWRITE UNDERWAY
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#'
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#' Print regression results according to STROBE
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#'
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#' Printable table of two dimensional regression analysis of group vs variable for outcome measure. By group. Includes p-value
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#' Group and variable has to be dichotomous factor.
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#' @param meas outcome measure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
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#' @param var binary exposure variable to compare against (active vs placebo). As string.
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#' @param group binary group to compare, as string.
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#' @param adj variables to adjust for, as string.
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#' @param data dataframe to subset from.
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#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.
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#' @keywords strobe
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#' @export
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#' @examples
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#' data('mtcars')
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#' mtcars$vs<-factor(mtcars$vs)
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#' mtcars$am<-factor(mtcars$am)
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#' strobe_diff_bygroup(meas="mpg",var="vs",group = "am",adj=c("disp","wt"),data=mtcars)
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strobe_diff_bygroup<-function(meas,var,group,adj,data,dec=2){
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## meas: sdmt
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## var: rtreat
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## group: genotype
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## for dichotome exposure variable (var)
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d <- data
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m <- d[, c(meas)]
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v <- d[, c(var)]
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g <- d[, c(group)]
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ads <- d[, c(adj)]
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dat <- data.frame(m, v, g, ads)
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df <- data.frame(matrix(ncol = 9))
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if (!is.factor(m)) {
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for (i in 1:length(levels(g))) {
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grp <- levels(dat$g)[i]
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di <- dat[dat$g == grp, ][, -3]
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mod <- lm(m ~ v, data = di)
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p <- coef(summary(mod))[2,4]
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p<-ifelse(p<0.001,"<0.001",round(p,3))
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p <- ifelse(p<=0.05|p=="<0.001",paste0("*",p),
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ifelse(p>0.05&p<=0.1,paste0(".",p),p))
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pv<-p
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co<-round(coef(mod),dec)[2]
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ci<-round(confint(mod),dec)[2,]
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lo<-ci[1]
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up<-ci[2]
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ci<-paste0(co," (",lo," to ",up,")")
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amod <- lm(m ~ ., data = di)
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pa <- coef(summary(amod))[2,4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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apv<-pa
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aco<-round(coef(amod),dec)[2]
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aci<-round(confint(amod),dec)[2,]
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alo<-aci[1]
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aup<-aci[2]
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aci<-paste0(aco," (",alo," to ",aup,")")
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nr <- c()
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for (r in 1:2) {
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vr <- levels(di$v)[r]
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dr <- di[di$v == vr, ]
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n <- as.numeric(nrow(dr[!is.na(dr$m), ]))
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mean <- round(mean(dr$m, na.rm = TRUE), dec -
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1)
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sd <- round(sd(dr$m, na.rm = TRUE), dec - 1)
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ms <- paste0(mean, " (", sd, ")")
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nr <- c(nr, n, ms)
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}
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irl <- c(grp, nr, ci, pv, aci, apv)
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df <- rbind(df, irl)
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names(df) <- c("grp",
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paste0("N.", substr(levels(v)[1], 1, 3)),
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paste0("M.", substr(levels(v)[1], 1, 3)),
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paste0("N.", substr(levels(v)[2], 1, 3)),
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paste0("M.", substr(levels(v)[2], 1, 3)),
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"diff",
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"pval",
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"ad.diff",
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"ad.pval")
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}
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}
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if (is.factor(m)) {
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for (i in 1:length(levels(g))) {
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grp <- levels(dat$g)[i]
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di <- dat[dat$g == grp, ][, -3]
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mod <- glm(m ~ v, family = binomial(), data = di)
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p <- coef(summary(mod))[2,4]
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p<-ifelse(p<0.001,"<0.001",round(p,3))
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p <- ifelse(p<=0.05|p=="<0.001",paste0("*",p),
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ifelse(p>0.05&p<=0.1,paste0(".",p),p))
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pv<-p
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co <- round(exp(coef(mod)[-1]), dec)
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ci<-round(exp(confint(mod)),dec)[2,]
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lo<-ci[1]
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up<-ci[2]
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ci <- paste0(co, " (", lo, " to ", up, ")")
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amod <- glm(m ~ ., family = binomial(), data = di)
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pa <- coef(summary(amod))[2,4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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apv<-pa
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aco <- round(exp(coef(amod)[2]), dec)
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aci<-suppressMessages(round(exp(confint(amod)),dec))[2,]
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alo<-aci[1]
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aup<-aci[2]
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aci <- paste0(aco, " (", alo, " to ", aup, ")")
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nr <- c()
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for (r in 1:2) {
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vr <- levels(di$v)[r]
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dr <- di[di$v == vr, ]
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n <- as.numeric(nrow(dr[!is.na(dr$m), ]))
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nl <- levels(m)[2]
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out <- nrow(dr[dr$m == nl & !is.na(dr$m), ])
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pro <- round(out/n * 100, 0)
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rt <- paste0(out, " (", pro, "%)")
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nr <- c(nr, n, rt)
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}
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irl <- c(grp, nr, ci, pv, aci, apv)
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df <- rbind(df, irl)
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names(df) <- c("grp",
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paste0("N.", substr(levels(v)[1], 1, 3)),
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paste0(nl, ".", substr(levels(v)[1], 1, 3)),
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paste0("N.", substr(levels(v)[2], 1, 3)),
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paste0(nl, ".", substr(levels(v)[2], 1, 3)),
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"OR",
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"pval",
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"ad.OR",
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"ad.pval")
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}
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}
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return(df)
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}
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144
R/print_diff_byvar.R
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144
R/print_diff_byvar.R
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#' REWRITE UNDERWAY
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#'
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#' Print regression results according to STROBE
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#'
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#' Printable table of three dimensional regression analysis of group vs var for meas. By var. Includes p-values.
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#' @param 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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#' @param var binary exposure variable to compare against (active vs placebo). As string.
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#' @param group groups to compare, as string.
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#' @param adj variables to adjust for, as string.
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#' @param data dataframe of data.
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#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
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#' @keywords strobe
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#' @export
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#' @examples
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#' data('mtcars')
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#' mtcars$vs<-factor(mtcars$vs)
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#' mtcars$am<-factor(mtcars$am)
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#' strobe_diff_byvar(meas="mpg",var="vs",group = "am",adj=c("disp","wt","hp"),data=mtcars)
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strobe_diff_byvar<-function(meas,var,group,adj,data,dec=2){
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## meas: sdmt
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## var: rtreat
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## group: genotype
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## for dichotome exposure variable (var)
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d <- data
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m <- d[, c(meas)]
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v <- d[, c(var)]
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g <- d[, c(group)]
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ads <- d[, c(adj)]
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dat <- data.frame(m, v, g, ads)
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df <- data.frame(grp = c(NA, as.character(levels(g))))
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if (!is.factor(m)) {
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for (i in 1:length(levels(v))) {
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grp <- levels(dat$v)[i]
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di <- dat[dat$v == grp, ][, -2]
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mod <- lm(m ~ g, data = di)
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p <- coef(summary(mod))[2:length(levels(g)),4]
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p<-ifelse(p<0.001,"<0.001",round(p,3))
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p <- ifelse(p<=0.05|p=="<0.001",paste0("*",p),
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ifelse(p>0.05&p<=0.1,paste0(".",p),p))
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pv<-c("-",p)
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co <- c("-", round(coef(mod)[-1], dec))
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ci<-round(confint(mod),dec)[2:length(levels(g)),]
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lo <- c("-", ci[,1])
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up <- c("-", ci[,2])
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ci <- paste0(co, " (", lo, " to ", up, ")")
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amod <- lm(m ~ ., data = di)
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pa <- coef(summary(amod))[2:length(levels(g)),4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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apv<-c("-",pa)
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aco <- c("-", round(coef(amod)[2:length(levels(g))],
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dec))
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aci<-round(confint(amod),dec)[2:length(levels(g)),]
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alo <- c("-", aci[,1])
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aup <- c("-", aci[,2])
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aci <- paste0(aco, " (", alo, " to ", aup, ")")
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nr <- c()
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for (r in 1:length(levels(g))) {
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vr <- levels(di$g)[r]
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dr <- di[di$g == vr, ]
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n <- as.numeric(nrow(dr[!is.na(dr$m), ]))
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mean <- round(mean(dr$m, na.rm = TRUE), dec -
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1)
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sd <- round(sd(dr$m, na.rm = TRUE), dec - 1)
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ms <- paste0(mean, " (", sd, ")")
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nr <- c(nr, n, ms)
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}
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irl <- rbind(matrix(grp, ncol = 6), cbind(matrix(nr,
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ncol = 2, byrow = TRUE), cbind(ci,pv, aci,apv)))
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colnames(irl) <- c("N",
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"Mean (SD)",
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"Difference",
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"p-value",
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"Adjusted Difference",
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"Adjusted p-value")
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df <- cbind(df, irl)
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}
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}
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if (is.factor(m)) {
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for (i in 1:length(levels(v))) {
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grp <- levels(dat$v)[i]
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di <- dat[dat$v == grp, ][, -2]
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mod <- glm(m ~ g, family = binomial(), data = di)
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p <- coef(summary(mod))[2:length(levels(g)),4]
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p<-ifelse(p<0.001,"<0.001",round(p,3))
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p <- ifelse(p<=0.05|p=="<0.001",paste0("*",p),
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ifelse(p>0.05&p<=0.1,paste0(".",p),p))
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pv<-c("-",p)
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co <- c("-", round(exp(coef(mod)[-1]), dec))
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ci <- suppressMessages(round(exp(confint(mod)),dec))[2:length(levels(g)),]
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lo <- c("-", ci[,1])
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up <- c("-", ci[,2])
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ci <- paste0(co, " (", lo, " to ", up, ")")
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amod <- glm(m ~ ., family = binomial(), data = di)
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pa <- coef(summary(amod))[2:length(levels(g)),4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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apv<-c("-",pa)
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aco <- c("-", suppressMessages(round(exp(coef(amod)[2:length(levels(g))]),
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dec)))
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aci <- suppressMessages(round(exp(confint(mod)),dec)[2:length(levels(g)),])
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alo <- c("-", aci[,1])
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aup <- c("-", aci[,2])
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aci <- paste0(aco, " (", alo, " to ", aup, ")")
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nr <- c()
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for (r in 1:length(levels(g))) {
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vr <- levels(di$g)[r]
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dr <- di[di$g == vr, ]
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n <- as.numeric(nrow(dr[!is.na(dr$m), ]))
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nl <- levels(m)[2]
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out <- nrow(dr[dr$m == nl & !is.na(dr$m), ])
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pro <- round(out/n * 100, 0)
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rt <- paste0(out, " (", pro, "%)")
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nr <- c(nr, n, rt)
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}
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irl <- rbind(matrix(grp, ncol = 4), cbind(matrix(nr,
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ncol = 2, byrow = TRUE), cbind(ci,pv, aci,apv)))
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colnames(irl) <- c("N",
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paste0("N.", nl),
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"OR",
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"p-value",
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"Adjusted OR",
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"Adjusted p-value")
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df <- cbind(df, irl)
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}
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}
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return(df)
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}
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139
R/print_log.R
Normal file
139
R/print_log.R
Normal file
@ -0,0 +1,139 @@
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#' Print regression results according to STROBE
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#'
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#' Printable table of logistic regression analysis according to STROBE.
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#' @param 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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#' @param var exposure variable to compare against (active vs placebo). As string.
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#' @param adj variables to adjust for, as string.
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#' @param data dataframe of data.
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#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
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#' @keywords logistic
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#' @export
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strobe_log<-function(meas,var,adj,data,dec=2){
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## Ønskeliste:
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##
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## - Sum af alle, der indgår (Overall N)
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## - Ryd op i kode, der der er overflødig %-regning, alternativt, så fiks at NA'er ikke skal regnes med.
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##
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require(dplyr)
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d<-data
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m<-d[,c(meas)]
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v<-d[,c(var)]
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ads<-d[,c(adj)]
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dat<-data.frame(m,v)
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df<-data.frame(matrix(ncol=4))
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mn <- glm(m ~ .,family = binomial(), data = dat)
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dat<-data.frame(dat,ads)
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ma <- glm(m ~ .,family = binomial(), data = dat)
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ctable <- coef(summary(mn))
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pa <- ctable[, 4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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pv<-c("REF",pa[2:length(coef(mn))])
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co<-round(exp(coef(mn)),dec)[-1]
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ci<-round(exp(confint(mn)),dec)[-1,]
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lo<-ci[,1]
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up<-ci[,2]
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or_ci<-c("REF",paste0(co," (",lo," to ",up,")"))
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nr<-c()
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for (r in 1:length(levels(dat[,2]))){
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vr<-levels(dat[,2])[r]
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dr<-dat[dat[,2]==vr,]
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n<-as.numeric(nrow(dr))
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## Af en eller anden grund bliver der talt for mange med.
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# nall<-as.numeric(nrow(dat[!is.na(dat[,2]),]))
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nl<-levels(m)[r]
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# pro<-round(n/nall*100,0)
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# rt<-paste0(n," (",pro,"%)")
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nr<-rbind(nr,cbind(nl,n))
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}
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mms<-data.frame(cbind(nr,or_ci,pv))
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header<-data.frame(matrix(var,ncol = ncol(mms)))
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names(header)<-names(mms)
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ls<-list(unadjusted=data.frame(rbind(header,mms)))
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actable <- coef(summary(ma))
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pa <- actable[,4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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apv<-pa[1:length(coef(ma))]
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aco<-round(exp(coef(ma)),dec)
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aci<-round(exp(confint(ma)),dec)
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alo<-aci[,1]
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aup<-aci[,2]
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aor_ci<-paste0(aco," (",alo," to ",aup,")")
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dat2<-dat[,-1]
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# names(dat2)<-c(var,names(ads))
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nq<-c()
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for (i in 1:ncol(dat2)){
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if (is.factor(dat2[,i])){
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vec<-dat2[,i]
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ns<-names(dat2)[i]
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for (r in 1:length(levels(vec))){
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vr<-levels(vec)[r]
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dr<-vec[vec==vr]
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n<-as.numeric(length(dr))
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# nall<-as.numeric(nrow(dat[!is.na(dat2[,c(ns)]),]))
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nl<-paste0(ns,levels(vec)[r])
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||||
# 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$adjusted)<-fnames
|
||||
|
||||
return(ls)
|
||||
}
|
||||
174
R/print_olr.R
Normal file
174
R/print_olr.R
Normal file
@ -0,0 +1,174 @@
|
||||
#' Print ordinal logistic regression results according to STROBE
|
||||
#'
|
||||
#' Printable table of ordinal logistic regression with bivariate and multivariate analyses.
|
||||
#' Table according to STROBE. Uses polr() funtion of the MASS-package.
|
||||
#' Formula analysed is the most simple m~v1+v2+vn. The is no significance test. Results are point estimates with 95 percent CI.
|
||||
#' @param meas outcome meassure variable name or response in data-data.frame as a string. Should be factor, preferably ordered.
|
||||
#' @param vars variables to compare against. As vector of columnnames.
|
||||
#' @param data dataframe of data.
|
||||
#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
|
||||
#' @param n.by.adj flag to indicate wether to count number of patients in adjusted model or overall for outcome meassure not NA.
|
||||
#' @keywords olr
|
||||
#' @export
|
||||
|
||||
strobe_olr<-function(meas,vars,data,dec=2,n.by.adj=FALSE){
|
||||
## For calculation of p-value from t-value see rep_olr()
|
||||
|
||||
require(MASS)
|
||||
require(dplyr)
|
||||
|
||||
d<-data
|
||||
m<-d[,c(meas)]
|
||||
|
||||
ads<-d[,c(vars)]
|
||||
|
||||
if(!is.factor(m)){stop("'meas' should be a factor, preferably ordered.")}
|
||||
|
||||
if(is.factor(m)){
|
||||
|
||||
## Crude ORs
|
||||
|
||||
dfcr<-data.frame(matrix(NA,ncol = 2))
|
||||
names(dfcr)<-c("pred","or_ci")
|
||||
n.mn<-c()
|
||||
nref<-c()
|
||||
|
||||
for(i in 1:ncol(ads)){
|
||||
dat<-data.frame(m=m,ads[,i])
|
||||
names(dat)<-c("m",names(ads)[i])
|
||||
mn<-polr(m ~ ., data = dat, Hess=TRUE)
|
||||
n.mn<-c(n.mn,nrow(mn$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)
|
||||
}
|
||||
358
R/print_pred.R
Normal file
358
R/print_pred.R
Normal file
@ -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)
|
||||
}
|
||||
@ -1,4 +1,4 @@
|
||||
#' REWRITE UNDERWAY
|
||||
#' REWRITE UNDERWAY - replaced by 'print_diff_bygroup'
|
||||
#'
|
||||
#' Print regression results according to STROBE
|
||||
#'
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
#' REWRITE UNDERWAY
|
||||
#' REWRITE UNDERWAY - replaced by 'print_diff_byvar'
|
||||
#'
|
||||
#' Print regression results according to STROBE
|
||||
#'
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
#' OBSOLETE - use 'print_log'
|
||||
#'
|
||||
#' Print regression results according to STROBE
|
||||
#'
|
||||
#' Printable table of logistic regression analysis according to STROBE.
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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}
|
||||
|
||||
@ -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}
|
||||
|
||||
@ -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}
|
||||
|
||||
@ -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}
|
||||
|
||||
@ -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}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user