tableby {arsenal} | R Documentation |
Summarize one or more variables (x) by a categorical variable (y). Variables on the right side of the formula, i.e. independent variables, are summarized by the levels of a categorical variable on the left of the formula. Optionally, an appropriate test is performed to test the distribution of the independent variables across the levels of the categorical variable.
tableby(
formula,
data,
na.action,
subset = NULL,
weights = NULL,
strata,
control = NULL,
...
)
formula |
an object of class |
data |
an optional data frame, list or environment (or object coercible by |
na.action |
a function which indicates what should happen when the data contain |
subset |
an optional vector specifying a subset of observations (rows of data) to be used in the results. Works as vector of logicals or an index. |
weights |
a vector of weights. Using weights will disable statistical tests. |
strata |
a vector of strata to separate summaries by an additional group. |
control |
control parameters to handle optional settings within |
... |
additional arguments to be passed to internal |
The group variable (if any) is categorical, which could be an integer, character,
factor, or ordered factor. tableby
makes a simple summary of
the counts within the k-levels of the independent variables on the
right side of the formula. Note that unused levels are dropped.
The data
argument allows data.frames with label attributes for the columns, and those
labels will be used in the summary methods for the tableby
class.
The independent variables are a mixture of types: categorical (discrete), numeric (continuous), and time to event (survival). These variables are split by the levels of the group variable (if any), then summarized within those levels, specific to the variable type. A statistical test is performed to compare the distribution of the independent variables across the levels of the grouping variable.
The tests differ by the independent variable type, but can be specified explicitly in the formula statement or in the control function. These tests are accepted:
anova
: analysis of variance test; the default test for continuous variables. When
LHS variable has two levels, equivalent to two-sample t-test.
kwt
: Kruskal-Wallis Rank Test, optional test for continuous
variables. When LHS variable has two levels, equivalent to Wilcoxon test.
wt
: An explicit Wilcoxon test.
medtest
: A median test.
chisq
: chi-square goodness of fit test for equal counts of a
categorical variable across categories; the default for categorical
or factor variables
fe
: Fisher's exact test for categorical variables
trend
: trend test for equal distribution of an ordered variable
across a categorical variable; the default for ordered factor variables
logrank
: log-rank, the default for time-to-event variables
notest
: no test is performed.
To perform a mixture of asymptotic and rank-based tests on two
different continuous variables, an example formula is:
formula = group ~ anova(age) + kwt(height)
. The test settings
in tableby.control
apply to all independent variables of a given type.
The summary statistics reported for each independent variable within the
group variable can be set in tableby.control
.
Finally, multiple by-variables can be set using list()
. See the examples for more details.
An object with class c("tableby", "arsenal_table")
Jason Sinnwell, Beth Atkinson, Gregory Dougherty, and Ethan Heinzen, adapted from SAS Macros written by Paul Novotny and Ryan Lennon
arsenal_table
, anova
, chisq.test
, tableby.control
,
summary.tableby
, tableby.internal
, formulize
, selectall
data(mockstudy)
tab1 <- tableby(arm ~ sex + age, data=mockstudy)
summary(tab1, text=TRUE)
mylabels <- list(sex = "SEX", age ="Age, yrs")
summary(tab1, labelTranslations = mylabels, text=TRUE)
tab3 <- tableby(arm ~ sex + age, data=mockstudy, test=FALSE, total=FALSE,
numeric.stats=c("median","q1q3"), numeric.test="kwt")
summary(tab3, text=TRUE)
# multiple LHS
summary(tableby(list(arm, sex) ~ age, data = mockstudy, strata = ps), text = TRUE)
tab.test <- tableby(arm ~ kwt(age) + anova(bmi) + kwt(ast), data=mockstudy)
tests(tab.test)