Publish package

Description

This package processes results of descriptive statistcs and regression analysis into final tables and figures of a manuscript

CiTable data

Description

These data are used for testing Publish package functionality.

Format

A data frame with 27 observations on the following 9 variables.

Drug

Time

Drug.Time

Dose

Mean

SD

n

HazardRatio

lower

upper

p

Examples

data(CiTable)
labellist <- split(CiTable[,c("Dose","Mean","SD","n")],CiTable[,"Drug"])
labellist
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")], labels=labellist)

Diabetes data of Dr John Schorling

Description

These data are courtesy of Dr John Schorling, Department of Medicine, University of Virginia School of Medicine. The data consist of 19 variables on 403 subjects from 1046 subjects who were interviewed in a study to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans. According to Dr John Hong, Diabetes Mellitus Type II (adult onset diabetes) is associated most strongly with obesity. The waist/hip ratio may be a predictor in diabetes and heart disease. DM II is also agssociated with hypertension - they may both be part of "Syndrome X". The 403 subjects were the ones who were actually screened for diabetes. Glycosolated hemoglobin > 7.0 is usually taken as a positive diagnosis of diabetes.

Format

A data frame with 205 observations on the following 12 variables.

id

subject id

chol

Total Cholesterol

stab.glu

Stabilized Glucose

hdl

High Density Lipoprotein

ratio

Cholesterol/HDL Ratio

glyhb

Glycosolated Hemoglobin

location

a factor with levels (Buckingham,Louisa)

age

age (years)

gender

male or female

height

height (inches)

height.europe

height (cm)

weight

weight (pounds)

weight.europe

weight (kg)

frame

a factor with levels (small,medium,large)

bp.1s

First Systolic Blood Pressure

bp.1d

First Diastolic Blood Pressure

bp.2s

Second Diastolic Blood Pressure

bp.2d

Second Diastolic Blood Pressure

waist

waist in inches

hip

hip in inches

time.ppn

Postprandial Time when Labs were Drawn in minutes

AgeGroups

Categorized age

BMI

Categorized BMI

References

Willems JP, Saunders JT, DE Hunt, JB Schorling: Prevalence of coronary heart disease risk factors among rural blacks: A community-based study. Southern Medical Journal 90:814-820; 1997 Schorling JB, Roach J, Siegel M, Baturka N, Hunt DE, Guterbock TM, Stewart HL: A trial of church-based smoking cessation interventions for rural African Americans. Preventive Medicine 26:92-101; 1997.

Examples

data(Diabetes)

A study was made of all 26 astronauts on the first eight space shuttle flights (Bungo et.al., 1985). On a voluntary basis 17 astronauts consumed large quantities of salt and fluid prior to landing as a countermeasure to space deconditioning, while nine did not.

Description

A study was made of all 26 astronauts on the first eight space shuttle flights (Bungo et.al., 1985). On a voluntary basis 17 astronauts consumed large quantities of salt and fluid prior to landing as a countermeasure to space deconditioning, while nine did not.

Format

A data frame with 52 observations on the following 4 variables:

Status

Factor with levels Post (after flight) and Pre (before flight)

HR

Supine heart rate(beats per minute)

Treatment

Countermeasure salt/fluid (1= yes, 0=no)

ID

Person id

References

Altman, Practical statistics for medical research, Page 223, Ex. 9.1. Bungo et.al., 1985

Examples

data(SpaceT)

Add units to data set

Description

Add variable units to data.frame (or data.table).

Usage

Units(object, units)

Arguments

Details

If the object has units existing units are replaced by given units.

Value

The object augmented with attribute "units"

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

data(Diabetes)
Diabetes <- Units(Diabetes,list(BMI="kg/m^2"))
Units(Diabetes)
Diabetes <- Units(Diabetes,list(bp.1s="mm Hg",bp.2s="mm Hg"))
Units(Diabetes)

Automatic selection and formatting of breaks in cut

Description

A version of cut that easily formats the labels and places breaks by default.

Usage

acut(
  x,
  n = 5,
  type = "default",
  format = NULL,
  format.low = NULL,
  format.high = NULL,
  dig.lab = 3,
  right = TRUE,
  breaks,
  labels = TRUE,
  ...
)

Arguments

Details

The formats are supplied by specifiyng the text around the lower (%l) and upper (%l) value (see examples). If user specified breaks are supplied, the default labels from cut are used. If automatic breaks are used, the default labels are a slight modification at the end point of the default from cut All this can of course be adjusted manually through the format functionality (see below).

By default, 5 breaks are constructed according to the quantiles with of the input x. The number of breaks can be adjusted, and default specifying breaks (as in cut) can be supplied instead.

If type is changed from "default" to another option, a different formatting template is used. For now the only other option is "age", which is designed to be well suited to easily group age variables. When type="age" only the breaks argument is used, and it behaves different from otherwise. If a single number is supplied, intervals of length breaks will automatically be constructed (starting from 0). If a vector is supplied, the intervals are used as in cut but formatted differently, see examples.

Value

same as for cut. A vector of 'factors' is created, unless 'labels=FALSE'.

Author(s)

Anders Munch

Examples

data(Diabetes) # load dataset

## The default uses format similar to cut
chol.groups <- acut(Diabetes$chol)
table(chol.groups)

## The formatting can easily be changed
chol.groups <- acut(Diabetes$chol,format="%l-%u",n=5)
table(chol.groups)

## The default is to automatic place the breaks, so the number of this can easily be changed.
chol.groups <- acut(Diabetes$chol,n=7)
table(chol.groups)

## Manually setting format and breaks
age.groups <- acut(Diabetes$age,format="%l-%u",breaks=seq(0,100,by=10))
table(age.groups)

## Other variations 
age.groups <- acut(Diabetes$age,
                   format="%l-%u",
                   format.low="below %u",
                   format.high="above %l",
                   breaks=c(0, seq(20,80,by=10), Inf))
table(age.groups)

BMI.groups <- acut(Diabetes$BMI,
                   format="BMI between %l and %u",
                   format.low="BMI below %u",
                   format.high="BMI above %l")
table(BMI.groups)
org(as.data.frame(table(BMI=BMI.groups)))

## Instead of using the quantiles, we can specify equally spaced breaks,
## but still get the same formatting
BMI.grouping <-
   seq(min(Diabetes$BMI,na.rm=TRUE), max(Diabetes$BMI,na.rm=TRUE), length.out=6)
BMI.grouping[1] <- -Inf # To get all included
BMI.groups <- acut(Diabetes$BMI,
                   breaks=BMI.grouping,
                   format="BMI between %l and %u",
                   format.low="BMI below %u",
                   format.high="BMI above %l")
table(BMI.groups)
org(as.data.frame(table(BMI=BMI.groups)))

## Using type="age"
## When using type="age", categories of 10 years are constructed by default.
## The are formatted to be easier to read when the values are ages.
table(acut(Diabetes$age, type="age"))

## This can be changes with the breaks argument.
## Note that this is diffent from cut when breaks is a single number.
table(acut(Diabetes$age, type="age", breaks=20))

## Of course We can also supply the breaks manually.
## The formatting depends on whether or not all the values fall within the breaks:
## All values within the breaks
table(acut(Diabetes$age, type="age", breaks=c(0, 30, 50, 80, 100)))
## Some values below and above the breaks
table(acut(Diabetes$age, type="age", breaks=c(30, 50, 80))) 

Compute mean values with confidence intervals

Description

Compute mean values with confidence intervals

Usage

ci.mean(x, ...)

Arguments

Details

Normal approximation

Value

a list with mean values and confidence limits

Compute mean values with confidence intervals

Description

Compute mean values with confidence intervals

Usage

## Default S3 method:
ci.mean(
  x,
  alpha = 0.05,
  normal = TRUE,
  na.rm = TRUE,
  statistic = "arithmetic",
  ...
)

Arguments

Details

Normal approximation

Value

a list with mean values and confidence limits

Author(s)

Thomas Gerds

Run a series of Cox regression models

Description

Run a series of Cox regression analyses for a list of predictor variables and summarize the results in a table. The Cox models can be adjusted for a fixed set of covariates

This function runs on coxph from the survival package.

Usage

coxphSeries(formula, data, vars, ...)

Arguments

Value

matrix with results

Author(s)

Thomas Alexander Gerds

Examples

library(survival)
data(pbc)
## collect hazard ratios from three univariate Cox regression analyses
pbc$edema <- factor(pbc$edema,levels=c("0","0.5","1"),labels=c("0","0.5","1"))
uni.hr <- coxphSeries(Surv(time,status==2)~1,vars=c("edema","bili","protime"),data=pbc)
uni.hr

## control the logistic regression analyses for age and gender
## but collect only information on the variables in `vars'.
controlled.hr <- coxphSeries(Surv(time,status==2)~age+sex,vars=c("edema","bili","protime"),data=pbc)
controlled.hr

Expand regression coefficient table

Description

Expand regression coefficient table

Usage

fixRegressionTable(
  x,
  varnames,
  reference.value,
  reference.style = NULL,
  factorlevels,
  scale = NULL,
  nmiss,
  intercept
)

Arguments

Details

This function expands results from "regressionTable" with extralines and columns

For factor variables the reference group is shown. For continuous variables the units are shown and for transformed continuous variables also the scale. For all variables the numbers of missing values are added.

Value

a table with regression coefficients

Author(s)

Thomas Alexander Gerds <tag@biostat.ku.dk>

Summary tables for a given followup time point.

Description

Summarize baseline variables in groups defined by outcome at a given followup time point

Usage

followupTable(formula, data, followup.time, compare.groups, ...)

Arguments

Details

If compare.groups!=FALSE, p-values are obtained from stopped Cox regression, i.e., all events are censored at follow-up time. A univariate Cox regression model is fitted to assess the effect of each variable on the right hand side of the formula on the event hazard and shown is the p-value of anova(fit), see anova.coxph.

Value

Summary table.

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

univariateTable

Examples

library(survival)
data(pbc)
pbc$edema <- factor(pbc$edema,levels=c("0","0.5","1"),labels=c("0","0.5","1"))
pbc$sex <- factor(pbc$sex,levels=c("m","f"),labels=c("m","f"))
followupTable(Hist(time,status)~age+edema+sex,data=pbc,followup.time=1000)

Formatting confidence intervals

Description

Format confidence intervals

Usage

formatCI(
  x,
  lower,
  upper,
  show.x = FALSE,
  handler = "sprintf",
  format = "[l;u]",
  degenerated = "asis",
  digits = 2,
  nsmall = digits,
  sep = "",
  reference.pos,
  reference.label = "",
  ...
)

Arguments

Details

The default format for confidence intervals is [lower; upper].

Value

String vector with confidence intervals

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

plot.ci ci.mean

Examples

x=ci.mean(rnorm(10))
formatCI(lower=x[3],upper=x[4])
formatCI(lower=c(0.001,-2.8413),upper=c(1,3.0008884))
# change format
formatCI(lower=c(0.001,-2.8413),upper=c(1,3.0008884),format="(l, u)")
# show x
formatCI(x=x$mean,lower=x$lower,upper=x$upper,format="(l, u)",show.x=TRUE)

# change of handler function
l <- c(-0.0890139,0.0084736,144.898333,0.000000001)
u <- c(0.03911392,0.3784706,3338944.8821221,0.00001)
cbind(format=formatCI(lower=l,upper=u,format="[l;u)",digits=2,nsmall=2,handler="format"),
      prettyNum=formatCI(lower=l,upper=u,format="[l;u)",digits=2,nsmall=2,handler="prettyNum"),
      sprintf=formatCI(lower=l,upper=u,format="[l;u)",digits=2,nsmall=2,handler="sprintf"))

Run a series of generalized linear regression analyses

Description

Run a series of generalized linear regression analyses for a list of predictor variables and summarize the results in a table. The regression models can be adjusted for a fixed set of covariates.

Usage

glmSeries(formula, data, vars, ...)

Arguments

Value

Matrix with regression coefficients, one for each element of vars.

Author(s)

Thomas Alexander Gerds

Examples

data(Diabetes)
Diabetes$hyper1 <- factor(1*(Diabetes$bp.1s>140))
## collect odds ratios from three univariate logistic regression analyses
uni.odds <- glmSeries(hyper1~1,vars=c("chol","hdl","location"),data=Diabetes,family=binomial)
uni.odds
## control the logistic regression analyses for age and gender
## but collect only information on the variables in `vars'.
controlled.odds <- glmSeries(hyper1~age+gender,
                             vars=c("chol","hdl","location"),
                             data=Diabetes, family=binomial)
controlled.odds

labelUnits

Description

Label output tables

Usage

labelUnits(x, ...)

Arguments

Details

Modify labels and values of variables in summary tables

Value

The re-labeled matrix

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

univariateTable

Examples

data(Diabetes)
tab <- summary(univariateTable(gender~AgeGroups+chol+waist,data=Diabetes))
publish(tab)
ltab <- labelUnits(tab,"chol"="Cholesterol (mg/dL)","<40"="younger than 40")
publish(ltab)

## pass labels immediately to utable
utable(gender~AgeGroups+chol+waist,data=Diabetes,
      "chol"="Cholesterol (mg/dL)","<40"="younger than 40")

## sometimes useful to state explicitly which variables value
## should be re-labelled
utable(gender~AgeGroups+chol+waist,data=Diabetes,
      "chol"="Cholesterol (mg/dL)","AgeGroups.<40"="younger than 40")

Efficient coding of date variables

Description

This function eases the process of generating date variables. All variables in a data.frame which match a regular expression are included

Usage

lazyDateCoding(data, format, pattern, varnames, testlength = 10)

Arguments

Details

The code needs to be copy-and-pasted from the R-output buffer into the R-code buffer. This can be customized for the really efficiently working people, e.g., in emacs.

Value

R-code one line for each variable.

Author(s)

Thomas Alexander Gerds

Examples

d <- data.frame(x0="190101",x1=c("12/8/2019"),x2="12-8-2019",x3="20190812",stringsAsFactors=FALSE)
lazyDateCoding(d,pattern="x")
lazyDateCoding(d,pattern="3")

Efficient coding of factor levels

Description

This function eases the process of generating factor variables with relevant labels. All variables in a data.frame with less than a user set number of levels result in a line which suggests levels and labels. The result can then be modified for use.

Usage

lazyFactorCoding(data, max.levels = 10)

Arguments

Details

The code needs to be copy-and-pasted from the R-output buffer into the R-code buffer. This can be customized for the really efficiently working people e.g. in emacs.

Value

R-code one line for each variable.

Author(s)

Thomas Alexander Gerds

Examples

data(Diabetes)
lazyFactorCoding(Diabetes)

Wrapper function for publish with output format org

Description

Wrapper for publish(...,org=TRUE)

Usage

org(x, ...)

Arguments

Value

See publish

Author(s)

Thomas Alexander Gerds

Parse interaction terms

Description

Parse interaction terms for regression tables

Usage

parseInteractionTerms(
  terms,
  xlevels,
  units,
  format.factor,
  format.contrast,
  format.scale,
  format.scale.unit,
  sep = ": ",
  ...
)

Arguments

Details

Prepare a list of contrasts which combines regression coefficients to describe statistical interactions.

Value

List of contrasts which can be passed to lava::estimate.

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

lava::estimate

Examples

tt <- terms(formula(SBP~age+sex*BMI))
xlev <- list(sex=c("male","female"),BMI=c("normal","overweight","obese"))
parseInteractionTerms(terms=tt,xlevels=xlev)
parseInteractionTerms(terms=tt,xlevels=xlev,format.factor="var level")
parseInteractionTerms(terms=tt,xlevels=xlev,format.contrast="var(level:ref)")

tt2 <- terms(formula(SBP~age*factor(sex)+BMI))
xlev2 <- list("factor(sex)"=c("male","female"))
parseInteractionTerms(terms=tt2,xlevels=xlev2)
parseInteractionTerms(terms=tt2,xlevels=xlev2,units=list(age="yrs"))


data(Diabetes)
fit <- glm(bp.2s~age*factor(gender)+BMI,data=Diabetes)
parseInteractionTerms(terms=terms(fit$formula),xlevels=fit$xlevels,
                      format.scale="var -- level:ref",units=list("age"='years'))
parseInteractionTerms(terms=terms(fit$formula),xlevels=fit$xlevels,
                      format.scale.unit="var [unit]",units=list("age"='years'))
it <- parseInteractionTerms(terms=terms(fit$formula),xlevels=fit$xlevels)
ivars <- unlist(lapply(it,function(x)attr(x,"variables")))
lava::estimate(fit,function(p)lapply(unlist(it),eval,envir=sys.parent(-1)))

Plot confidence intervals

Description

Function to plot confidence intervals

Usage

## S3 method for class 'ci'
plot(x, xlim, xlab = "", labels, ...)

Arguments

Details

Function to plot means and other point estimates with confidence intervals

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

data(Diabetes)
x=ci.mean(bp.2s~AgeGroups,data=Diabetes)
plot(x,title.labels="Age groups",xratio=c(0.4,0.3))
x=ci.mean(bp.2s/500~AgeGroups+gender,data=Diabetes)
plot(x,xratio=c(0.4,0.2))
plot(x,xratio=c(0.4,0.2),
     labels=split(x$labels[,"AgeGroups"],x$labels[,"gender"]),
     title.labels="Age groups")
## Not run: 
plot(x, leftmargin=0, rightmargin=0)
plotConfidence(x, leftmargin=0, rightmargin=0)

data(CiTable)
with(CiTable,plotConfidence(x=list(HazardRatio),
                               lower=lower,
                               upper=upper,
                               labels=CiTable[,2:6],
                               factor.reference.pos=c(1,10,19),
                               format="(u-l)",
                               points.col="blue",
                               digits=2))

with(CiTable,Publish::plot.ci(x=list(HazardRatio),
                               lower=lower,
                               upper=upper,
                               labels=CiTable[,2:6],
                               factor.reference.pos=c(1,10,19),
                               format="(u-l)",
                               points.col="blue",
                               digits=2,
                               leftmargin=-2,
                               title.labels.cex=1.1,
                               labels.cex=0.8,values.cex=0.8))

## End(Not run)

Plotting regression coefficients with confidence limits

Description

Plotting regression coefficients with confidence limits

Usage

## S3 method for class 'regressionTable'
plot(x, xlim, xlab, style = 1, ...)

Arguments

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

regressionTable

Examples

## linear regression
data(Diabetes)
f <- glm(bp.1s~AgeGroups+chol+gender+location,data=Diabetes)
rtf <- regressionTable(f,factor.reference = "inline")
plot(rtf,cex=1.3)

## logistic regression
data(Diabetes)
f <- glm(I(BMI>25)~bp.1s+AgeGroups+chol+gender+location,data=Diabetes,family=binomial)
rtf <- regressionTable(f,factor.reference = "inline")
plot(rtf,cex=1.3)

## Poisson regression
data(trace)
fit <- glm(dead ~ smoking+ sex+ age+Time+offset(log(ObsTime)), family = poisson,data=trace)
rtab <- regressionTable(fit,factor.reference = "inline")
plot(rtab,xlim=c(0.85,1.15),cex=1.8,xaxis.cex=1.5)

## Cox regression
library(survival)
data(pbc)
coxfit <- coxph(Surv(time,status!=0)~age+log(bili)+log(albumin)+factor(edema)+sex,data=pbc)
pubcox <- publish(coxfit)
plot(pubcox,cex=1.5,xratio=c(0.4,0.2))

plot.subgroupAnalysis

Description

This function operates on a "subgroupAnalysis" object to produce a formatted table and a forest plot

Usage

## S3 method for class 'subgroupAnalysis'
plot(x, ...)

Arguments

Details

This function produces a formatted table of a subgroupAnalysis object and adds a forest plot. If further details needs attention before plotting is is advisable use adjust the table produced by the summary function and then plotting with the plotConfidence function

Author(s)

Christian Torp-Pedersen

See Also

subgroupAnalysis, plotConfidence

Examples

#load libraries
library(Publish)
library(survival)
library(data.table)
data(traceR) #get dataframe traceR
setDT(traceR)
traceR[,':='(wmi2=factor(wallMotionIndex<0.9,levels=c(TRUE,FALSE), 
                labels=c("bad","good")),
             abd2=factor(abdominalCircumference<95, levels=c(TRUE,FALSE), 
                labels=c("slim","fat")),
             sex=factor(sex))]
fit_cox <- coxph(Surv(observationTime,dead)~treatment,data=traceR)
# Selected subgroups - univariable analysis
sub_cox <- subgroupAnalysis(fit_cox,traceR,treatment="treatment",
  subgroup=c("smoking","sex","wmi2","abd2")) # subgroups as character string
plot(sub_cox)  

Plot confidence intervals

Description

Function to plot confidence intervals with their values and additional labels. One anticipated use of this function involves first the generation of a regression object, then arrangement of a result table with "regressionTable", further arrangment of table with with e.g. "fixRegressionTable" and various user defined changes - and then finally table along with forest plot using the current function.

Usage

plotConfidence(
  x,
  y.at,
  lower,
  upper,
  pch = 16,
  cex = 1,
  lwd = 1,
  col = 4,
  xlim,
  xlab,
  labels,
  title.labels,
  values,
  title.values,
  section.pos,
  section.sep,
  section.title = NULL,
  section.title.x,
  section.title.offset,
  order,
  leftmargin = 0.025,
  rightmargin = 0.025,
  stripes,
  factor.reference.pos,
  factor.reference.label = "Reference",
  factor.reference.pch = 16,
  refline = 1,
  title.line = TRUE,
  xratio,
  y.offset = 0,
  y.title.offset,
  digits = 2,
  format,
  extremearrows.length = 0.05,
  extremearrows.angle = 30,
  add = FALSE,
  layout = TRUE,
  xaxis = TRUE,
  ...
)

Arguments

Details

Function to plot means and other point estimates with confidence intervals, their values and additional labels . Horizonal margins as determined by par()$mar are ignored. Instead layout is used to divide the plotting region horizontally into two or three parts plus leftmargin and rightmargin.

When values is FALSE there are only two parts. The default order is labels on the left confidence intervals on the right. When no labels are given or labels is FALSE there are only two parts. The default order is confidence intervals on the left values on the right.

The default order of three parts from left to right is labels, confidence intervals, values. The order can be changed as shown by the examples below. The relative widths of the two or three parts need to be adapted to the actual size of the text of the labels. This depends on the plotting device and the size of the font and figures and thus has to be adjusted manually.

Oma can be used to further control horizontal margins, e.g., par(oma=c(0,4,0,4)).

If confidence limits extend beyond the range determined by xlim, then arrows are drawn at the x-lim borders to indicate that the confidence limits continue.

Value

List of dimensions and coordinates

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

library(Publish)
data(CiTable) 

## A first draft version of the plot is obtained as follows
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper","p")],
          labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")])

## if argument labels is a named list the table is subdivided:
labellist <- split(CiTable[,c("Dose","Time","Mean","SD","n")],CiTable[,"Drug"])
labellist
## the data need to be ordered accordingly
CC= data.table::rbindlist(split(CiTable[,c("HazardRatio","lower","upper")],CiTable[,"Drug"]))
plotConfidence(x=CC, labels=labellist)


## The graph consist of at most three columns:
##
## column 1: labels
## column 2: printed values of the confidence intervals
## column 3: graphical presentation of the confidence intervals
##
## NOTE: column 3 appears always, the user decides if also
##       column 1, 2 should appear
##
## The columns are arranged with the function layout
## and the default order is 1,3,2 such that the graphical
## display of the confidence intervals appears in the middle
##
## the order of appearance of the three columns can be changed as follows
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               order=c(1,3,2))
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               order=c(2,3,1))
## if there are only two columns the order is 1, 2
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               values=FALSE,
               order=c(2,1))
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               values=FALSE,
               order=c(1,2))



## The relative size of the columns needs to be controlled manually
## by using the argument xratio. If there are only two columns
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               xratio=c(0.4,0.15))

## The amount of space on the left and right margin can be controlled
## as follows:
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               xratio=c(0.4,0.15),
               leftmargin=0.1,rightmargin=0.00)

## The actual size of the current graphics device determines
## the size of the figures and the space between them.
## The sizes and line widths are increased as follows:
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               xlab="Hazard ratio",
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               points.cex=3,
               cex=2,
               lwd=3,
               xaxis.lwd=1.3,
               xaxis.cex=1.3)
## Note that 'cex' of axis ticks is controlled via 'par' but
## cex of the label via argument 'cex' of 'mtext'.
## The sizes and line widths are decreased as follows:
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               cex=0.8,
               lwd=0.8,
               xaxis.lwd=0.8,
               xaxis.cex=0.8)

## Another good news is that all figures can be controlled separately

## The size of the graphic device can be controlled in the usual way, e.g.:
## Not run: 
    pdf("~/tmp/testCI.pdf",width=8,height=8)
    plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
                   labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")])
    dev.off()

## End(Not run)

## More control of the x-axis and confidence intervals that
## stretch outside the x-range end in an arrow. 
## the argument xlab.line adjusts the distance of the x-axis
## label from the graph
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               xlab="Hazard ratio",
               xlab.line=1.8,
               xaxis.at=c(0.8,1,1.3),
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               xlim=c(0.8,1.3))

## log-scale
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               xlab="Hazard ratio",
               xlab.line=1.8,
               xaxis.at=c(0.8,1,1.3),
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               xlim=c(0.8,1.3),plot.log="x")
## More pronounced arrows
## Coloured xlab expression
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               xlab=expression(HR[1](s)),
               xlab.line=1.8,
               xlab.col="darkred",
               extremearrows.angle=50,
               extremearrows.length=0.1,
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               xlim=c(0.8,1.3))

## Controlling the labels and their titles
## and the values and their titles
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               xlab="Hazard ratio",
               title.values=expression(bold(HR (CI[95]))),
               title.labels=c("Drug/Time","Dose","Mean","St.dev.","N"),
               factor.reference.pos=c(1,10,19),
               factor.reference.pch=16,
               cex=1.3,
               xaxis.at=c(0.75,1,1.25,1.5,2))

## For factor reference groups, one may want to replace the
## confidence intervals by the word Reference, as in the previous example.
## To change the word 'Reference' we use the argument factor.reference.label:
## To change the plot symbol for the reference lines factor.reference.pch
## To remove the plot symbol in the reference lines use 'NA' as follows:
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               xlab="Hazard ratio",
               factor.reference.label="Ref",
               title.values=expression(bold(HR (CI[95]))),
               title.labels=c("Drug/Time","Dose","Mean","St.dev.","N"),
               factor.reference.pos=c(1,10,19),
               factor.reference.pch=NA,
               cex=1.3,
               xaxis.at=c(0.75,1,1.25,1.5,2))


## changing the style of the graphical confidence intervals
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               xlab="Hazard ratio",
               factor.reference.pos=c(1,10,19),
               points.pch=15,
               points.col=rainbow(27),
               points.cex=2,
               arrows.col="darkblue",
               cex=1.3,
               order=c(1,3,2),
               xaxis.at=c(0.75,1,1.25,1.5))

## the values column of the graph can have multiple columns as well
## to illustrate this we create the confidence intervals
## before calling the function and then cbind them
## to the pvalues
HR <- pubformat(CiTable[,6])
CI95 <- formatCI(lower=CiTable[,7],upper=CiTable[,8],format="(l-u)")
pval <- format.pval(CiTable[,9],digits=3,eps=10^{-3})
pval[pval=="NA"] <- ""
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               values=list("HR"=HR,"CI-95"=CI95,"P-value"=pval),
               cex=1.2,
               xratio=c(0.5,0.3))

## Finally, vertical columns can be delimited with background color
## NOTE: this may slow things down and potentially create
##       large figures (many bytes)
col1 <- rep(c(prodlim::dimColor("green",density=22),
              prodlim::dimColor("green")),length.out=9)
col2 <- rep(c(prodlim::dimColor("orange",density=22),
              prodlim::dimColor("orange")),length.out=9)
col3 <- rep(c(prodlim::dimColor("blue",density=22),
              prodlim::dimColor("blue")),length.out=9)
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               stripes=c(1,0,1),
               stripes.col=c(col1,col2,col3))
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               stripes=c(1,1,1),
               stripes.col=c(col1,col2,col3))

threegreens <- c(prodlim::dimColor("green",density=55),
                 prodlim::dimColor("green",density=33),
                 prodlim::dimColor("green",density=22))
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
               labels=CiTable[,c("Drug.Time","Dose","Mean","SD","n")],
               values=FALSE,
               xlim=c(0.75,1.5),
               stripes=c(1,1,1),
               xratio=c(0.5,0.15),
               stripes.horizontal=c(0,9,18,27)+0.5,
               stripes.col=threegreens)

# combining multiple plots into one
layout(t(matrix(1:5)))
plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
       labels=CiTable[,c("Mean","n")],
       layout=FALSE)
 plotConfidence(x=CiTable[,c("HazardRatio","lower","upper")],
       layout=FALSE)

Print confidence intervals

Description

Print confidence intervals

Usage

## S3 method for class 'ci'
print(x, se = FALSE, print = TRUE, ...)

Arguments

Details

This format of the confidence intervals is user-manipulable.

Value

A string: the formatted confidence intervals

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

ci plot.ci formatCI summary.ci

Examples

library(lava)
m <- lvm(Y~X)
m <- categorical(m,Y~X,K=4)
set.seed(4)
d <- sim(m,24)
ci.mean(Y~X,data=d)
x <- ci.mean(Y~X,data=d)
print(x,format="(l,u)")

Printing univariate tables

Description

Print function for subgroupAnalysis

Usage

## S3 method for class 'subgroupAnalysis'
print(x, ...)

Arguments

Details

This function is simply calling summary.subgroupAnalysis

Value

The result of summary.subgroupAnalysis(x)

Author(s)

Christian Torp-Pedersen (ctp@heart.dk)

See Also

subgroupAnalysis

print results of 2x2 contingency table analysis

Description

print results of 2x2 contingency table analysis

Usage

## S3 method for class 'table2x2'
print(x, digits = 1, ...)

Arguments

Value

invisible x

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

table2x2

Examples

table2x2(table("marker"=rbinom(100,1,0.4),"response"=rbinom(100,1,0.1)))
table2x2(matrix(c(71,18,38,8),ncol=2),stats="table")
table2x2(matrix(c(71,18,38,8),ncol=2),stats=c("rr","fisher"))

Printing univariate tables

Description

Print function for univariate tables

Usage

## S3 method for class 'univariateTable'
print(x, ...)

Arguments

Details

This function is simply calling summary.univariateTable

Value

The result of summary.univariateTable(x)

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

univariateTable

Format numbers for publication

Description

Format numbers according to a specified handler function. Currently supported are sprintf, format and prettyNum.

Usage

pubformat(x, digits = 2, nsmall = digits, handler = "sprintf", ...)

Arguments

Value

Formatted number

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

sprintf, format, prettyNum

Examples

pubformat(c(0.000143,12.8,1))
pubformat(c(0.000143,12.8,1),handler="format")
pubformat(c(0.000143,12.8,1),handler="format",trim=TRUE)
pubformat(c(0.000143,12.8,1),handler="prettyNum")

Publishing tables and figures

Description

Publish provides summary functions for data and results of statistical analysis in ready-for-publication design

Usage

publish(object, ...)

Arguments

Details

Some warnings are currently suppressed.

Value

Tables and figures

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

publish.CauseSpecificCox publish.ci publish.coxph publish.glm publish.riskRegression publish.survdiff

Tabulizing cause-specific hazard ratio from all causes with confidence limits and Wald test p-values.

Description

Publish cause-specific Cox models

Usage

## S3 method for class 'CauseSpecificCox'
publish(
  object,
  cause,
  confint.method,
  pvalue.method,
  factor.reference = "extraline",
  units = NULL,
  print = TRUE,
  ...
)

Arguments

Details

The cause-specific hazard ratio's are combined into one table.

Value

Table with cause-specific hazard ratios, confidence limits and p-values.

Author(s)

Thomas Alexander Gerds <tab@biostat.ku.dk>

Examples

if (requireNamespace("riskRegression",quietly=TRUE)){
library(riskRegression)
library(prodlim)
library(survival)
data(Melanoma,package="riskRegression")
fit1 <- CSC(list(Hist(time,status)~sex,Hist(time,status)~invasion+epicel+age),
            data=Melanoma)
publish(fit1)
publish(fit1,pvalue.stars=TRUE)
publish(fit1,factor.reference="inline",units=list("age"="years"))

# wide format (same variables in both Cox regression formula) 
fit2 <- CSC(Hist(time,status)~invasion+epicel+age, data=Melanoma)
publish(fit2)

# with p-values
x <- publish(fit2,print=FALSE)
table <- cbind(x[[1]]$regressionTable,
           x[[2]]$regressionTable[,-c(1,2)])
}

Present logistic regression and Cox regression obtained with mitools::MIcombine based on smcfcs::smcfcs multiple imputation analysis

Description

Regression tables after multiple imputations

Usage

## S3 method for class 'MIresult'
publish(
  object,
  confint.method,
  pvalue.method,
  digits = c(2, 4),
  print = TRUE,
  factor.reference = "extraline",
  intercept,
  units = NULL,
  fit,
  data,
  ...
)

Arguments

Details

Show results of smcfcs based multiple imputations of missing covariates in publishable format

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

## Not run: 
if (requireNamespace("riskRegression",quietly=TRUE)
  & requireNamespace("mitools",quietly=TRUE)
  & requireNamespace("smcfcs",quietly=TRUE)){
library(riskRegression)
library(mitools)
library(smcfcs)
## continuous outcome: linear regression
# lava some data with missing values
set.seed(7)
d=sampleData(78)
## generate missing values
d[X1==1,X6:=NA] 
d[X2==1,X3:=NA]
d=d[,.(X8,X4,X3,X6,X7)]
sapply(d,function(x)sum(is.na(x)))

# multiple imputation (should set m to a large value)

set.seed(17)
f= smcfcs(d,smtype="lm",
           smformula=X8~X4+X3+X6+X7,
           method=c("","","logreg","norm",""),m=3)
ccfit=lm(X8~X4+X3+X6+X7,data=d)
mifit=MIcombine(with(imputationList(f$impDatasets),
                lm(X8~X4+X3+X6+X7)))
publish(mifit,fit=ccfit,data=d)
publish(ccfit)

## binary outcome
# lava some data with missing values
set.seed(7)
db=sampleData(78,outcome="binary")
## generate missing values
db[X1==1,X6:=NA] 
db[X2==1,X3:=NA]
db=db[,.(Y,X4,X3,X6,X7)]
sapply(db,function(x)sum(is.na(x)))

# multiple imputation (should set m to a large value)
set.seed(17)
fb= smcfcs(db,smtype="logistic",
           smformula=Y~X4+X3+X6+X7,
           method=c("","","logreg","norm",""),m=2)
ccfit=glm(Y~X4+X3+X6+X7,family="binomial",data=db)
mifit=MIcombine(with(imputationList(fb$impDatasets),
                glm(Y~X4+X3+X6+X7,family="binomial")))
publish(mifit,fit=ccfit)
publish(ccfit)

## survival: Cox regression
library(survival)
# lava some data with missing values
set.seed(7)
ds=sampleData(78,outcome="survival")
## generate missing values
ds[X5==1,X6:=NA] 
ds[X2==1,X3:=NA]
ds=ds[,.(time,event,X4,X3,X6,X7)]
sapply(ds,function(x)sum(is.na(x)))

set.seed(17)
fs= smcfcs(ds,smtype="coxph",
           smformula="Surv(time,event)~X4+X3+X6+X7",
           method=c("","","","logreg","norm",""),m=2)
ccfit=coxph(Surv(time,event)~X4+X3+X6+X7,data=ds)
mifit=MIcombine(with(imputationList(fs$impDatasets),
                coxph(Surv(time,event)~X4+X3+X6+X7)))
publish(mifit,fit=ccfit,data=ds)
publish(ccfit)

## competing risks: Cause-specific Cox regression 
library(survival)
# lava some data with missing values
set.seed(7)
dcr=sampleData(78,outcome="competing.risks")
## generate missing values
dcr[X5==1,X6:=NA] 
dcr[X2==1,X3:=NA]
dcr=dcr[,.(time,event,X4,X3,X6,X7)]
sapply(dcr,function(x)sum(is.na(x)))

set.seed(17)
fcr= smcfcs(dcr,smtype="compet",
           smformula=c("Surv(time,event==1)~X4+X3+X6+X7",
                       "Surv(time,event==2)~X4+X3+X6+X7"),
           method=c("","","","logreg","norm",""),m=2)
## cause 2 
ccfit2=coxph(Surv(time,event==2)~X4+X3+X6+X7,data=dcr)
mifit2=MIcombine(with(imputationList(fcr$impDatasets),
                coxph(Surv(time,event==2)~X4+X3+X6+X7)))
publish(mifit2,fit=ccfit2,data=dcr)
publish(ccfit2)
}

## End(Not run) 

Publish predictive accuracy results

Description

Write output of riskRegression::Score in tables

Usage

## S3 method for class 'Score'
publish(object, metrics, score = TRUE, contrasts = TRUE, level = 3, ...)

Arguments

Details

Collect prediction accuracy results in tables

Value

Results of Score in tabular form

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

if (requireNamespace("riskRegression",quietly=TRUE)){
library(riskRegression)
library(survival)
learn = sampleData(100)
val= sampleData(100)
f1=CSC(Hist(time,event)~X1+X8,data=learn)
f2=CSC(Hist(time,event)~X1+X5+X6+X8,learn)
xs=Score(list(f1,f2),data=val,formula=Hist(time,event)~1)
publish(xs)
}

Publish tables with confidence intervals

Description

Publish tables with confidence intervals

Usage

## S3 method for class 'ci'
publish(object, format = "[u;l]", se = FALSE, ...)

Arguments

Details

This function calls summary.ci with print=FALSE and then publish

Value

table with confidence intervals

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

summary.ci

Examples

data(Diabetes)
publish(ci.mean(chol~location+gender,data=Diabetes),org=TRUE)

Tabulize hazard ratios with confidence intervals and p-values.

Description

Tabulize the part of the result of a Cox regression analysis which is commonly shown in publications.

Usage

## S3 method for class 'coxph'
publish(
  object,
  confint.method,
  pvalue.method,
  print = TRUE,
  factor.reference = "extraline",
  units = NULL,
  probindex = FALSE,
  ...
)

Arguments

Details

Transforms the log hazard ratios to hazard ratios and returns them with confidence limits and p-values. If explanatory variables are log transformed or log2 transformed, a scaling factor is multiplied to both the log-hazard ratio and its standard-error.

Value

Table with hazard ratios, confidence intervals and p-values.

Author(s)

Thomas Alexander Gerds

Examples

library(survival)
data(pbc)
pbc$edema <- factor(pbc$edema,
             levels=c("0","0.5","1"), labels=c("0","0.5","1"))
fit = coxph(Surv(time,status!=0)~age+sex+edema+log(bili)+log(albumin),
            data=na.omit(pbc))
publish(fit)
## forest plot
plot(publish(fit),cex=1.3)

publish(fit,ci.digits=2,pvalue.eps=0.01,pvalue.digits=2,pvalue.stars=TRUE)
publish(fit,ci.digits=2,ci.handler="prettyNum",pvalue.eps=0.01,
        pvalue.digits=2,pvalue.stars=TRUE)
publish(fit, ci.digits=2, ci.handler="sprintf", pvalue.eps=0.01,
        pvalue.digits=2,pvalue.stars=TRUE, ci.trim=FALSE)

fit2 = coxph(Surv(time,status!=0)~age+sex+edema+log(bili,base=2)+log(albumin)+log(protime),
    data=na.omit(pbc))
publish(fit2)

# with cluster variable
fit3 = coxph(Surv(time,status!=0)~age+cluster(sex)+edema+log(bili,base=2)
                                    +log(albumin)+log(protime),
    data=na.omit(pbc))
publish(fit3)

# with strata and cluster variable
fit4 = coxph(Surv(time,status!=0)~age+cluster(sex)+strata(edema)+log(bili,base=2)
                 +log(albumin)+log(protime),
    data=pbc)
publish(fit4)

Tabulize regression coefficients with confidence intervals and p-values.

Description

Tabulate the results of a generalized linear regression analysis.

Usage

## S3 method for class 'glm'
publish(
  object,
  confint.method,
  pvalue.method,
  digits = c(2, 4),
  print = TRUE,
  factor.reference = "extraline",
  intercept = ifelse((is.null(object$family) || object$family$family == "gaussian"), 1L,
    0L),
  units = NULL,
  ...
)

Arguments

Details

The table shows changes in mean for linear regression and odds ratios for logistic regression (family = binomial).

Value

Table with regression coefficients, confidence intervals and p-values.

Author(s)

Thomas Alexander Gerds <tag@biostat.ku.dk>

Examples

data(Diabetes)
## Linear regression
f = glm(bp.2s~frame+gender+age,data=Diabetes)
publish(f)
publish(f,factor.reference="inline")
publish(f,pvalue.stars=TRUE)
publish(f,ci.format="(l,u)")

### interaction
fit = glm(bp.2s~frame+gender*age,data=Diabetes)
summary(fit)
publish(fit)

Fit = glm(bp.2s~frame*gender+age,data=Diabetes)
publish(Fit)

## Logistic regression
Diabetes$hyper1 <- factor(1*(Diabetes$bp.1s>140))
lrfit <- glm(hyper1~frame+gender+age,data=Diabetes,family=binomial)
publish(lrfit)

### interaction
lrfit1 <- glm(hyper1~frame+gender*age,data=Diabetes,family=binomial)
publish(lrfit1)

lrfit2 <- glm(hyper1~frame*gender+age,data=Diabetes,family=binomial)
publish(lrfit2)

## Poisson regression
data(trace)
trace <- Units(trace,list("age"="years"))
fit <- glm(dead ~ smoking+sex+age+Time+offset(log(ObsTime)), family="poisson",data=trace)
rtf <- regressionTable(fit,factor.reference = "inline")
summary(rtf)
publish(fit)

## gls regression
if (requireNamespace("nlme",quietly=TRUE)){
    requireNamespace("lava",quietly=TRUE)
library(lava)
library(nlme)
m <- lvm(Y ~ X1 + gender + group + Interaction)
distribution(m, ~gender) <- binomial.lvm()
distribution(m, ~group) <- binomial.lvm(size = 2)
constrain(m, Interaction ~ gender + group) <- function(x){x[,1]*x[,2]}
d <- sim(m, 1e2)
d$gender <- factor(d$gender, labels = letters[1:2])
d$group <- factor(d$group)

e.gls <- gls(Y ~ X1 + gender*group, data = d,
             weights = varIdent(form = ~1|group))
publish(e.gls)

## lme
fm1 <- lme(distance ~ age*Sex, 
            random = ~1|Subject,
            data = Orthodont) 
res <- publish(fm1)
}

Pretty printing of test results.

Description

Pretty printing of test results.

Usage

## S3 method for class 'htest'
publish(object, title, ...)

Arguments

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

data(Diabetes)
publish(t.test(bp.2s~gender,data=Diabetes))
publish(wilcox.test(bp.2s~gender,data=Diabetes))
publish(with(Diabetes,t.test(bp.2s,bp.1s,paired=TRUE)))
publish(with(Diabetes,wilcox.test(bp.2s,bp.1s,paired=TRUE)))

Publishing a matrix in raw, org, latex, or muse format

Description

This is the heart of the Publish package

Usage

## S3 method for class 'matrix'
publish(
  object,
  title,
  colnames = TRUE,
  rownames = TRUE,
  col1name = "",
  digits = 4,
  try.convert = TRUE,
  sep = " ",
  endhead,
  endrow,
  style,
  inter.lines,
  latex = FALSE,
  wiki = FALSE,
  org = FALSE,
  markdown = FALSE,
  tabular = TRUE,
  latex.table.format = NA,
  latex.hline = 1,
  latex.nodollar = FALSE,
  ...
)

Arguments

Examples

x <- matrix(1:12,ncol=3)
publish(x)

# rounding the numeric part of data mixtures 
y <- cbind(matrix(letters[1:12],ncol=3),x,matrix(rnorm(12),ncol=3))
publish(y,digits=1)

publish(x,latex=TRUE,
inter.lines=list("1"="text between line 1 and line 2",
                          "3"="text between line 3 and line 4"))

Publishing results of riskRegression

Description

Preparing a publishable table from riskRegression results

Usage

## S3 method for class 'riskRegression'
publish(object, digits = c(2, 4), print = TRUE, ...)

Arguments

Value

Table with regression coefficients, confidence intervals and p-values

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

ARR LRR

Examples

if (requireNamespace("riskRegression",quietly=TRUE)){
library(riskRegression)
library(prodlim)
library(lava)
library(survival)
set.seed(20)
d <- SimCompRisk(20)
f <- ARR(Hist(time,event)~X1+X2,data=d,cause=1)
publish(f)
publish(f,digits=c(1,3))
}

Format summary table of aov results

Description

Format summary table of aov results

Usage

## S3 method for class 'summary.aov'
publish(
  object,
  print = TRUE,
  handler = "sprintf",
  digits = c(2, 4),
  nsmall = digits,
  ...
)

Arguments

Examples

 
data(Diabetes)
f <- glm(bp.1s~age+chol+gender+location,data=Diabetes)
publish(summary(aov(f)),digits=c(1,2))

Alternative summary of survdiff results

Description

Alternative summary of survdiff results

Usage

## S3 method for class 'survdiff'
publish(object, digits = c(2, 4), print = TRUE, ...)

Arguments

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

library(survival)
data(pbc)
sd <- survdiff(Surv(time,status!=0)~sex,data=pbc)
publish(sd)
publish(sd,digits=c(3,2))

Regression table

Description

Tabulate the results of a regression analysis.

Usage

regressionTable(
  object,
  param.method = "coef",
  confint.method = c("default", "profile", "robust", "simultaneous"),
  pvalue.method = c("default", "robust", "simultaneous"),
  factor.reference = "extraline",
  intercept = 0L,
  units = NULL,
  noterms = NULL,
  probindex = 0L,
  ...
)

Arguments

Details

The basic use of this function is to generate a near publication worthy table from a regression object. As with summary(object) reference levels of factor variables are not included. Expansion of the table with such values can be performed using the "fixRegressionTable" function. Forest plot can be added to the output with "plotRegressionTable".

regressionTable produces an object (list) with the parameters deriveds. The summary function creates a data frame which can be used as a (near) publication ready table.

The table shows changes in mean for linear regression, odds ratios for logistic regression (family = binomial) and hazard ratios for Cox regression.

Value

List of regression blocks

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

# linear regression
data(Diabetes)
f1 <- glm(bp.1s~age+gender+frame+chol,data=Diabetes)
summary(regressionTable(f1))
summary(regressionTable(f1,units=list("chol"="mmol/L","age"="years")))
## with interaction
f2 <- glm(bp.1s~age*gender+frame+chol,data=Diabetes)
summary(regressionTable(f2))
#Add reference values
summary(regressionTable(f2))
f3 <- glm(bp.1s~age+gender*frame+chol,data=Diabetes)
publish(f3)
regressionTable(f3)

# logistic regression
Diabetes$hyp1 <- factor(1*(Diabetes$bp.1s>140))
l1 <- glm(hyp1~age+gender+frame+chol,data=Diabetes,family="binomial")
regressionTable(l1)
publish(l1)
plot(regressionTable(l1))

## with interaction
l2 <- glm(hyp1~age+gender+frame*chol,data=Diabetes,family="binomial")
regressionTable(l2)
l3 <- glm(hyp1~age*gender+frame*chol,data=Diabetes,family="binomial")
regressionTable(l3)

# Cox regression
library(survival)
data(pbc)
pbc$edema <- factor(pbc$edema,levels=c("0","0.5","1"),labels=c("0","0.5","1"))
c1 <- coxph(Surv(time,status!=0)~log(bili)+age+protime+sex+edema,data=pbc)
regressionTable(c1)
# with interaction
c2 <- coxph(Surv(time,status!=0)~log(bili)+age+protime*sex+edema,data=pbc)
regressionTable(c2)
c3 <- coxph(Surv(time,status!=0)~edema*log(bili)+age+protime+sex+edema+edema:sex,data=pbc)
regressionTable(c3)


if (requireNamespace("nlme",quietly=TRUE)){ 
## gls regression
library(lava)
library(nlme)
m <- lvm(Y ~ X1 + gender + group + Interaction)
distribution(m, ~gender) <- binomial.lvm()
distribution(m, ~group) <- binomial.lvm(size = 2)
constrain(m, Interaction ~ gender + group) <- function(x){x[,1]*x[,2]}
d <- sim(m, 1e2)
d$gender <- factor(d$gender, labels = letters[1:2])
d$group <- factor(d$group)

e.gls <- gls(Y ~ X1 + gender*group, data = d,
             weights = varIdent(form = ~1|group))
regressionTable(e.gls)
summary(regressionTable(e.gls))
}

Spaghettiogram

Description

A spaghettiogram is showing repeated measures (longitudinal data)

Usage

spaghettiogram(
  formula,
  data,
  xlim,
  ylim,
  xlab = "",
  ylab = "",
  axes = TRUE,
  col,
  lwd,
  lty,
  pch,
  legend = FALSE,
  add = FALSE,
  background = TRUE,
  ...
)

Arguments

Value

List with data of each subject

Examples

data(SpaceT)
Spaghettiogram(HR~Status+id(ID),
               data=SpaceT)

Special frame

Description

Extract data and design matrix including specials from call

Usage

specialFrame(
  formula,
  data,
  unspecials.design = TRUE,
  specials,
  specials.factor = TRUE,
  specials.design = FALSE,
  strip.specials = TRUE,
  strip.arguments = NULL,
  strip.alias = NULL,
  strip.unspecials = NULL,
  drop.intercept = TRUE,
  response = TRUE,
  na.action = options()$na.action
)

Arguments

Details

Obtain a list with the data used for event history regression analysis. This function cannot be used directly on the user level but inside a function to prepare data for survival analysis.

Value

A list which contains - the response - the design matrix (see model.design) - one entry for each special (see model.design)

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

model.frame model.design Hist

Examples

## Here are some data with an event time and no competing risks
## and two covariates X1 and X2.
## Suppose we want to declare that variable X1 is treated differently
## than variable X2. For example, X1 could be a cluster variable, or
## X1 should have a proportional effect on the outcome.
d <- data.frame(y=1:7,
                X2=c(2.24,3.22,9.59,4.4,3.54,6.81,5.05),
                X3=c(1,1,1,1,0,0,1),
                X4=c(44.69,37.41,68.54,38.85,35.9,27.02,41.84),
                X1=factor(c("a","b","a","c","c","a","b"),
                    levels=c("c","a","b")))
## define special functions prop and cluster
prop <- function(x)x
cluster <- function(x)x
## We pass a formula and the data
e <- specialFrame(y~prop(X1)+X2+cluster(X3)+X4,
                  data=d,
                  specials=c("prop","cluster"))
## The first element is the response
e$response
## The other elements are the design, i.e., model.matrix for the non-special covariates
e$design
## and a data.frame for the special covariates
e$prop
## The special covariates can be returned as a model.matrix 
e2 <- specialFrame(y~prop(X1)+X2+cluster(X3)+X4,
                   data=d,
                   specials=c("prop","cluster"),
                   specials.design=TRUE)
e2$prop
## and the non-special covariates can be returned as a data.frame
e3 <- specialFrame(y~prop(X1)+X2+cluster(X3)+X4,
                   data=d,
                   specials=c("prop","cluster"),
                   specials.design=TRUE,
                   unspecials.design=FALSE)
e3$design

Plot predictions of logistic regression

Description

Plotting the prediction of a logistic regression model with confidence bands against one continuous variable.

Usage

splinePlot.lrm(
  object,
  xvar,
  xvalues,
  xlim = range(xvalues),
  ylim,
  xlab = xvar,
  ylab = scale[[1]],
  col = 1,
  lty = 1,
  lwd = 3,
  confint = TRUE,
  newdata = NULL,
  scale = c("risk", "odds"),
  add = FALSE,
  ...
)

Arguments

Details

Function which extracts from a logistic regression model fitted with rms::lrm the predicted risks or odds.

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

data(Diabetes)
Diabetes$hypertension=  1*(Diabetes$bp.1s>140)
library(rms)
uu <- datadist(Diabetes)
options(datadist="uu")
fit=lrm(hypertension~rcs(age)+gender+hdl,data=Diabetes)
splinePlot.lrm(fit,xvar="age",xvalues=seq(30,50,1))

Background and grid color control.

Description

Some users like background colors, and it may be helpful to have grid lines to read off e.g. probabilities from a Kaplan-Meier graph. Both things can be controlled with this function. However, it mainly serves plot.prodlim.

Usage

stripes(
  xlim,
  ylim,
  col = "white",
  lwd = 1,
  gridcol = "gray77",
  fill = "white",
  horizontal = NULL,
  vertical = NULL,
  border = "black",
  xpd = FALSE
)

Arguments

Author(s)

Thomas Alexander Gerds <tag@biostat.ku.dk>

Examples

plot(0,0)
backGround(bg="beige",fg="red",vertical=0,horizontal=0)

plot(0,0)
stripes(col=c("yellow","green"),gridcol="red",xlim=c(-1,1),horizontal=seq(0,1,.1))
stripes(col=c("yellow","green"),gridcol="red",horizontal=seq(0,1,.1))

Subgroup Analysis - Interactions and estimates

Description

The function can examine Cox regression, logistic regression and Poisson regression (Poisson regression for survival analysis) where the effect of one variable is of particular interest. This function systematically checks for effect modification with a list of other variables.

In randomised studies the main regression analysis is often univariate and includes only the exposure of interest. In observational studies the main regression analysis can readily be adjusted for other variables including those which may modify the effect of the variable of interest.

Usage

subgroupAnalysis(
  object,
  data,
  treatment,
  subgroups,
  confint.method = "default",
  factor.reference = "extraline",
  ...
)

Arguments

Details

The function can only handle a bivariate treatment, which MUST coded as zero or one. The p-value for interaction is obtained with a likelihood ratio test comparing the main regression analysis with the interaction model.

There are plot and print functions available for the function see helppages for plot.subgroupAnalysis and print.subgroupAnalysis

Value

A data.frame with subsgroup specifications, number in each subgroup, parameter estimates and p-value for interaction. A forest plot can be obtained with "plotConfidence".

Author(s)

Christian Torp-Pedersen

See Also

coxph, glm, plotConfidence

Examples

#load libraries
library(data.table)
library(Publish)
library(survival)
data(traceR) #get dataframe traceR
data.table::setDT(traceR)
traceR[,':='(wmi2=factor(wallMotionIndex<0.9,levels=c(TRUE,FALSE), 
                labels=c("bad","good")),
             abd2=factor(abdominalCircumference<95, levels=c(TRUE,FALSE), 
                labels=c("slim","fat")))]
traceR[,sex:=as.factor(sex)] # all subgroup variables needs to be factor                
traceR[observationTime==0,observationTime:=1]
# remove missing covariate values
traceR=na.omit(traceR)
# univariate analysis of smoking in subgroups of age and sex
# Main regression analysis is a simple/univariate Cox regression
fit_cox <- coxph(Surv(observationTime,dead)~treatment,data=traceR)
sub_cox <- subgroupAnalysis(fit_cox,traceR,treatment="treatment", 
  subgroups=c("smoking","sex","wmi2","abd2"))
sub_cox

# to see how the results are obtained consider the variable: smoking
fit_cox_smoke <- coxph(Surv(observationTime,dead)~treatment*smoking,data=traceR)
# the last three rows of the following output:
publish(fit_cox_smoke)
# are included in the first 3 rows of the result of the sub group analysis:
sub_cox[1:3,]
# the p-value is obtained as:
fit_cox_smoke_add <- coxph(Surv(observationTime,dead)~treatment+smoking,data=traceR)
anova(fit_cox_smoke_add,fit_cox_smoke,test="Chisq")

# Note that a real subgroup analysis would be to subset the data
fit_cox1a <- coxph(Surv(observationTime,dead)~treatment,data=traceR[smoking=="never"])
fit_cox1b <- coxph(Surv(observationTime,dead)~treatment,data=traceR[smoking=="current"])
fit_cox1c <- coxph(Surv(observationTime,dead)~treatment,data=traceR[smoking=="prior"])


## when the main analysis is already adjusted 
fit_cox_adj <- coxph(Surv(observationTime,dead)~treatment+smoking+sex+wmi2+abd2,
                 data=traceR)
sub_cox_adj <- subgroupAnalysis(fit_cox_adj,traceR,treatment="treatment",
  subgroups=c("smoking","sex","wmi2","abd2")) # subgroups as character string
sub_cox_adj

# When both start and end are in the Surv statement:
traceR[,null:=0]
fit_cox2 <- coxph(Surv(null,observationTime,dead)~treatment+smoking+sex+wmi2+abd2,data=traceR)
summary(regressionTable(fit_cox))
sub_cox2 <- subgroupAnalysis(fit_cox2,traceR,treatment="treatment",
  subgroups=c("smoking","sex","wmi2","abd2")) 
# Analysis with Poisson - and the unrealistic assumption of constant hazard
# and adjusted for age in all subgroups
fit_p <- glm(dead~treatment+age+offset(log(observationTime)),family="poisson",
           data=traceR)
sub_pois <- subgroupAnalysis(fit_p,traceR,treatment="treatment",
  subgroups=~smoking+sex+wmi2+abd2) 
# Analysis with logistic regression - and very wrongly ignoring censoring
fit_log <- glm(dead~treatment+age,family="binomial",data=traceR)
sub_log <- subgroupAnalysis(fit_log,traceR,treatment="treatment",
   subgroups=~smoking+sex+wmi2+abd2, factor.reference="inline")

Summarize confidence intervals

Description

Summarize confidence intervals

Usage

## S3 method for class 'ci'
summary(object, format = "[u;l]", se = FALSE, print = TRUE, ...)

Arguments

Details

This format of the confidence intervals is user-manipulable.

Value

Formatted confidence intervals

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

ci plot.ci format.ci

Examples

library(lava)
m <- lvm(Y~X)
m <- categorical(m,Y~X,K=4)
set.seed(4)
d <- sim(m,24)
ci.mean(Y~X,data=d)
x <- summary(ci.mean(Y~X,data=d),digits=2)
x
x <- summary(ci.mean(Y~X,data=d),format="(u,l)",digits=2)
x <- summary(ci.mean(Y~X,data=d),format="(u,l)",digits=1,se=TRUE)
x <- summary(ci.mean(Y~X,data=d),format="(u,l)",digits=1,handler="format")
x <- summary(ci.mean(Y~X,data=d),format="(u,l)",digits=1,handler="prettyNum")

Formatting regression tables

Description

Preparing regression results for publication

Usage

## S3 method for class 'regressionTable'
summary(object, show.missing = "ifany", print = TRUE, ...)

Arguments

Value

List with two elements:

• regressionTable: the formatted regression table (a data.frame)

• rawTable: table with the unformatted values (a data.frame)

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

publish.glm publish.coxph

Examples

library(survival)
data(pbc)
pbc$edema <- factor(pbc$edema,levels=c("0","0.5","1"),labels=c("0","0.5","1"))
fit = coxph(Surv(time,status!=0)~age+sex+edema+log(bili)+log(albumin)+log(protime),
            data=pbc)
u=summary(regressionTable(fit))
u$regressionTable
u$rawTable
summary(regressionTable(fit),handler="prettyNum")
summary(regressionTable(fit),handler="format")
summary(regressionTable(fit),handler="sprintf",digits=c(2,2),pValue.stars=TRUE)
summary(regressionTable(fit),handler="sprintf",digits=c(2,2),pValue.stars=TRUE,ci.format="(l,u)")

summary.subgroupAnalysis

Description

This function operates on a "subgroupAnalysis" object to produce a formatted table.

Usage

## S3 method for class 'subgroupAnalysis'
summary(
  object,
  digits = 3,
  eps = 0.001,
  subgroup.p = FALSE,
  keep.digital = FALSE,
  ...
)

Arguments

Details

This function produces a formatted or unformatted table of a subgroupAnalysis object. A forest plot can be added with the plot function.

Value

A data.frame with formatted values for subgroups

Author(s)

Christian Torp-Pedersen

See Also

subgroupAnalysis

Examples

#load libraries
library(Publish)
library(survival)
library(data.table)
data(traceR) #get dataframe traceR
setDT(traceR)
traceR[,':='(wmi2=factor(wallMotionIndex<0.9,levels=c(TRUE,FALSE), 
                labels=c("bad","good")),
             abd2=factor(abdominalCircumference<95, levels=c(TRUE,FALSE), 
                labels=c("slim","fat")))]
traceR[,sex:=as.factor(sex)] # all subgroup variables needs to be factor                
traceR[observationTime==0,observationTime:=1]           
# univariate analysis of smoking in subgroups of age and sex
# Basic model from randomised study - but observed for 12 years
fit_cox <- coxph(Surv(observationTime,dead)~treatment,data=traceR)
sub_cox <- subgroupAnalysis(fit_cox,traceR,treatment="treatment",
  subgroup=c("smoking","sex","wmi2","abd2")) # subgroups as character string
summary(sub_cox)   

Preparing univariate tables for publication

Description

Summary function for univariate table

Usage

## S3 method for class 'univariateTable'
summary(
  object,
  n = "inNames",
  drop.reference = FALSE,
  pvalue.stars = FALSE,
  pvalue.digits = 4,
  show.missing = c("ifany", "always", "never"),
  show.pvalues,
  show.totals,
  ...
)

Arguments

Details

Collects results of univariate table in a matrix.

Value

Summary table

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

data(Diabetes)
u <- univariateTable(gender~age+location+Q(BMI)+height+weight,
                data=Diabetes)
summary(u)
summary(u,n=NULL)
summary(u,pvalue.digits=2,"age"="Age (years)","height"="Body height (cm)")

u2 <- univariateTable(location~age+AgeGroups+gender+height+weight,
                data=Diabetes)
summary(u2)
summary(u2,drop.reference=TRUE)
## same but more flexible
summary(u2,drop.reference=c("binary"))
## same but even more flexible
summary(u2,drop.reference=c("gender"))

Fast summary of a univariate table

Description

First apply univariateTable then call summary.

Usage

sutable(...)

Arguments

Value

Summary table

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

See Also

summary.univariateTable univariateTable

Examples

data(Diabetes)
sutable(gender~age+location+Q(BMI)+height+weight,data=Diabetes,BMI="Body mass index (kg/m^2)")

2x2 table calculus for teaching

Description

2x2 table calculus for teaching

Usage

table2x2(
  x,
  digits = 1,
  conf.level = 0.95,
  stats = c("table", "rd", "rr", "or", "chisq", "fisher")
)

Arguments

Details

2x2 table calculus for teaching

Value

see example

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

table2x2(table("marker"=rbinom(100,1,0.4),"response"=rbinom(100,1,0.1)))
table2x2(matrix(c(71,18,38,8),ncol=2),stats="table")
table2x2(matrix(c(71,18,38,8),ncol=2),stats=c("rr","fisher"))

trace data

Description

These data are from screening to the TRACE study, a comparison between the angiotensin converting enzyme inhibitor trandolapril and placebo ford large myocardial infarctions. A total of 6676 patients were screened for the study. Survival has been followed for the screened population for 16 years. The current data has been prepared for a poisson regression to examine survival. The data has been "split" in 0.5 year intervals (plitLexis function from Epi package) and then collapsed on all variables (aggregate function).

Format

A data frame with 1832 observations on the following 6 variables.

Time

Time after myocardial infarction, in 6 months intervals

smoking

Smoking status. A factor with levels (Never, Current, Previous)

sex

A factor with levels (Female, Male)

age

Age in years at the time of myocardial infarction

ObsTime

Cumulative risk time in each split

dead

Count of deaths

References

Kober et al 1995 Am. J. Cardiol 76,1-5

Examples

data(trace)
Units(trace,list("age"="years"))
fit <- glm(dead ~ smoking+sex+age+Time+offset(log(ObsTime)), family="poisson",data=trace)
rtf <- regressionTable(fit,factor.reference = "inline")
summary(rtf)
publish(fit)

traceR data

Description

These data are from the TRACE randomised trial, a comparison between the angiotensin converting enzyme inhibitor trandolapril and placebo ford large myocardial infarctions. In all, 1749 patients were randomised. The current data are from a 15 year follow-up.

Format

A data frame with 1749 observations on the following variables.

weight

Weight in kilo

height

Height in meters

abdominalCircumference

in centimeters

seCreatinine

in mmol per liter

wallMotionIndex

left ventricular function 0-2, 0 worst, 2 normal

observationTime

time to death or censor

age

age in years

sex

0=female,1=male

smoking

0=never,1=prior,2=current

dead

0=censor,1=dead

treatment

placebo or trandolapril

References

Kober et al 1995 NEJM 333,1670

Examples

data(trace)
Units(trace,list("age"="years"))
fit <- glm(dead ~ smoking+sex+age+Time+offset(log(ObsTime)), family="poisson",data=trace)
rtf <- regressionTable(fit,factor.reference = "inline")
summary(rtf)
publish(fit)

Univariate table

Description

Categorical variables are summarized using counts and frequencies and compared .

Usage

univariateTable(
  formula,
  data = parent.frame(),
  summary.format = "mean(x) (sd(x))",
  Q.format = "median(x) [iqr(x)]",
  freq.format = "count(x) (percent(x))",
  column.percent = TRUE,
  digits = c(1, 1, 3),
  big.mark = ",",
  short.groupnames,
  compare.groups = TRUE,
  show.totals = TRUE,
  n = "inNames",
  outcome = NULL,
  ...
)

Arguments

Details

This function can generate the baseline demographic characteristics that forms table 1 in many publications. It is also useful for generating other tables of univariate statistics.

The result of the function is an object (list) which containe the various data generated. In most applications the summary function should be applied which generates a data.frame with a (nearly) publication ready table. Standard manipulation can be used to modify, add or remove columns/rows and for users not accustomed to R the table generated can be exported to a text file which can be read by other software, e.g., via write.csv(table,file="path/to/results/table.csv")

By default, continuous variables are summarized by means and standard deviations and compared with t-tests. When continuous variables are summarized by medians and interquartile ranges the Deviations from the above defaults are obtained when the arguments summary.format and freq.format are combined with suitable summary functions.

Value

List with one summary table element for each variable on the right hand side of formula. The summary tables can be combined with rbind. The function summary.univariateTable combines the tables, and shows p-values in custom format.

Author(s)

Thomas A. Gerds

See Also

summary.univariateTable, publish.univariateTable

Examples

data(Diabetes)
library(data.table)
univariateTable(~age,data=Diabetes)
univariateTable(~gender,data=Diabetes)
univariateTable(~age+gender+ height+weight,data=Diabetes)
## same thing but less typing
utable(~age+gender+ height+weight,data=Diabetes)

## summary by location: 
univariateTable(location~Q(age)+gender+height+weight,data=Diabetes)
## continuous variables marked with Q() are (by default) summarized
## with median (IQR) and kruskal.test (with two groups equivalent to wilcox.test)
## variables not marked with Q() are (by default) summarized
## with mean (sd) and anova.glm(...,test="Chisq")
## the p-value of anova(glm()) with only two groups is similar
## but not exactly equal to that of a t.test
## categorical variables are (by default) summarized by count
## (percent) and chi-square tests (\code{chisq.test}). When \code{compare.groups ='logistic'}
## anova(glm(...,family=binomial,test="Chisq")) is used to calculate p-values.

## export result to csv
table1 = summary(univariateTable(location~age+gender+height+weight,data=Diabetes),
show.pvalues=FALSE)
# write.csv(table1,file="~/table1.csv",rownames=FALSE)

## change labels and values
utable(location~age+gender+height+weight,data=Diabetes,
       age="Age (years)",gender="Sex",
       gender.female="Female",
       gender.male="Male",
       height="Body height (inches)",
       weight="Body weight (pounds)")

## Use quantiles and rank tests for some variables and mean and standard deviation for others
univariateTable(gender~Q(age)+location+Q(BMI)+height+weight,
                data=Diabetes)

## Factor with more than 2 levels
Diabetes$AgeGroups <- cut(Diabetes$age,
                          c(19,29,39,49,59,69,92),
                          include.lowest=TRUE)
univariateTable(location~AgeGroups+gender+height+weight,
                data=Diabetes)

## Row percent
univariateTable(location~gender+age+AgeGroups,
                data=Diabetes,
                column.percent=FALSE)

## change of frequency format
univariateTable(location~gender+age+AgeGroups,
                data=Diabetes,
                column.percent=FALSE,
                freq.format="percent(x) (n=count(x))")

## changing Labels
u <- univariateTable(location~gender+AgeGroups+ height + weight,
                     data=Diabetes,
                     column.percent=TRUE,
                     freq.format="count(x) (percent(x))")
summary(u,"AgeGroups"="Age (years)","height"="Height (inches)")

## more than two groups
Diabetes$frame=factor(Diabetes$frame,levels=c("small","medium","large"))
univariateTable(frame~gender+BMI+age,data=Diabetes)

Diabetes$sex=as.numeric(Diabetes$gender)
univariateTable(frame~sex+gender+BMI+age,
                data=Diabetes,freq.format="count(x) (percent(x))")

## multiple summary formats
## suppose we want for some reason mean (range) for age
## and median (range) for BMI.
## method 1:
univariateTable(frame~Q(age)+BMI,
                data=Diabetes,
                Q.format="mean(x) (range(x))",
                summary.format="median(x) (range(x))")
## method 2:
u1 <- summary(univariateTable(frame~age,
                              data=na.omit(Diabetes),
                              summary.format="mean(x) (range(x))"))
u2 <- summary(univariateTable(frame~BMI,
                              data=na.omit(Diabetes),
                              summary.format="median(x) (range(x))"))
publish(rbind(u1,u2),digits=2)

## Large number format (big.mark)
n=100000
dat=data.frame(id=1:n,z=rbinom(n,1,.3),x=factor(sample(1:8,size=n,replace=TRUE)))
u3 <- summary(univariateTable(z~x,
                              data=dat,big.mark=","))
u3