tab (version 2.1.1)

tab-package: Functions for creating summary tables for statistical reports

Description

Description: This package contains functions for generating tables for statistical reports written in Microsoft Word or LaTeX. There are functions for I-by-J frequency tables, comparison of means across levels of a categorical variable, generalized linear models, generalized estimating equations, and Cox proportional hazards regression. The package is intended to make it easier for researchers to translate results from statistical analyses in R to their reports or manuscripts.

Details

ll{ Package: tab Type: Package Version: 2.1.1 Date: 2014-04-10 License: GPL-2 } The following functions are included in the package: tabfreq, tabmeans, tabcox, tabglm

References

1. Therneau T (2013). A Package for Survival Analysis in S. R package version 2.37-4, http://CRAN.R-project.org/package=survival. 2. Terry M. Therneau and Patricia M. Grambsch (2000). Modeling Survival Data: Extending the Cox Model. Springer, New York. ISBN 0-387-98784-3. 3. Dahl DB (2013). xtable: Export tables to LaTeX or HTML. R package version 1.7-1, http://CRAN.R-project.org/package=xtable. Acknowledgment: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903.

See Also

NA

Examples

# Load in sample dataset d and drop rows with missing values
data(d)
d <- d[complete.cases(d),]

# Create labels for treatment group, sex, and race
groups <- c("Control", "Treatment")
sexes <- c("Female", "Male")
races <- c("White", "Black", "Mexican American", "Other")

# Compare race distribution by group, with group as column variable
freqtable <- tabfreq(x = d$group, y = d$race, xlevels = groups, 
                     ylevels = races, yname = "Race")

# Compare mean BMI in control group vs. treatment group
meanstable <- tabmeans(x = d$group, y = d$bmi, xlevels = groups, yname = "BMI")

# Create a typical Table 1 for statistical report or manuscript
table1 <- rbind(tabmeans(x = d$group, y = d$age, xlevels = groups, yname = "Age"),
                tabmeans(x = d$group, y = d$bmi, xlevels = groups, yname = "BMI"),
                tabfreq(x = d$group, y = d$sex, xlevels = groups, ylevels = sexes, 
                        yname = "Sex"),
                tabfreq(x = d$group, y = d$race, xlevels = groups, ylevels = races,
                        yname = "Race"))

# Test whether age, sex, race, and treatment group are associated with BMI
glmfit1 = glm(bmi ~ age + sex + race + group, data = d)
lintable <- tabglm(glmfit = glmfit1, 
                   xlabels = c("Intercept", "Age", "Male", "Race", races, "Treatment"))

# Test whether age, sex, race, and treatment group are associated with 1-year mortality
glmfit2 <- glm(death_1yr ~ age + sex + race + group, data = d, family = binomial)
logtable <- tabglm(glmfit = glmfit2, ci.beta = FALSE,
                   xlabels = c("Intercept", "Age", "Male", "Race", races, "Treatment"))

# Test whether age, sex, race, and treatment group are associated with survival
coxtable <- tabcox(x = d[,c("age", "sex", "race", "group")], time = d$time, 
                   delta = d$delta, 
                   xlabels = c("Age", "Male", "Race", races, "Treatment"))

# Click on freqtable, meanstable, table1, lintable, logtable, and coxtable in 
# the Workspace tab of RStudio to see the tables that could be copied and pasted 
# into a Word document. Alternatively, setting the latex input to TRUE produces
# tables that can be inserted into LaTeX using the xtable package.