Control settings for tableby function

Source: R/tableby.control.R

Control test and summary settings for the tableby function.

tableby.control(
  test = TRUE,
  total = TRUE,
  total.pos = c("after", "before"),
  test.pname = NULL,
  numeric.simplify = FALSE,
  cat.simplify = FALSE,
  cat.droplevels = FALSE,
  ordered.simplify = FALSE,
  date.simplify = FALSE,
  numeric.test = "anova",
  cat.test = "chisq",
  ordered.test = "trend",
  surv.test = "logrank",
  date.test = "kwt",
  selectall.test = "notest",
  test.always = FALSE,
  numeric.stats = c("Nmiss", "meansd", "range"),
  cat.stats = c("Nmiss", "countpct"),
  ordered.stats = c("Nmiss", "countpct"),
  surv.stats = c("Nmiss", "Nevents", "medSurv"),
  date.stats = c("Nmiss", "median", "range"),
  selectall.stats = c("Nmiss", "countpct"),
  stats.labels = list(),
  digits = 3L,
  digits.count = 0L,
  digits.pct = 1L,
  digits.p = 3L,
  format.p = TRUE,
  digits.n = 0L,
  conf.level = 0.95,
  wilcox.correct = FALSE,
  wilcox.exact = NULL,
  chisq.correct = FALSE,
  simulate.p.value = FALSE,
  B = 2000,
  times = 1:5,
  ...
)

Arguments

test

logical, telling tableby whether to perform tests of x variables across levels of the group variable.
total

logical, telling tableby whether to calculate a column of totals across group variable.
total.pos

One of "before" or "after", denoting where to put the total column relative to the by-variable columns.
test.pname

character string denoting the p-value column name in summary.tableby. Modifiable also with modpval.tableby.
numeric.simplify, date.simplify

logical, tell tableby whether to condense numeric/date output to a single line. NOTE: this only simplifies to one line if there is only one statistic reported, such as meansd. In particular, if Nmiss is specified and there are missings, then the output is not simplified.
cat.simplify, ordered.simplify

logical, tell tableby whether to remove the first level of the categorical/ordinal variable if binary. If TRUE, only the summary stats of the second level are reported (unless there's only one level, in which case it's reported). If "label", the second level's label is additionally appended to the label. NOTE: this only simplifies to one line if there is only one statistic reported, such as countpct. In particular, if Nmiss is specified and there are missings, then the output is not simplified.
cat.droplevels

Should levels be dropped for categorical variables? If set to true, p-values will not be displayed unless test.always = TRUE as well.
numeric.test

name of test for numeric RHS variables in tableby: anova, kwt (Kruskal-Wallis), medtest (median test). If no LHS variable exists, then a mean is required for a univariate test.
cat.test

name of test for categorical variables: chisq, fe (Fisher's Exact)
ordered.test

name of test for ordered variables: trend
surv.test

name of test for survival variables: logrank
date.test

name of test for date variables: kwt
selectall.test

name of test for date variables: notest
test.always

Should the test be performed even if one or more by-group has 0 observations? Relevant for kwt and anova.
numeric.stats, cat.stats, ordered.stats, surv.stats, date.stats, selectall.stats

summary statistics to include for the respective class of RHS variables within the levels of the group LHS variable.
stats.labels

A named list of labels for all the statistics function names, where the function name is the named element in the list and the value that goes with it is a string containing the formal name that will be printed in all printed renderings of the output, e.g., list(countpct="Count (Pct)"). Any unnamed elements will be ignored. Passing NULL will disable labels.
digits

Number of decimal places for numeric values.
digits.count

Number of decimal places for count values.
digits.pct

Number of decimal places for percents.
digits.p

Number of decimal places for p-values.
format.p

Logical, denoting whether to format p-values. See "Details", below.
digits.n

Number of decimal places for N's in the header. Set it to NA to suppress the N's.
conf.level

Numeric, denoting what confidence level to use for confidence intervals. (See, e.g., binomCI)
wilcox.correct, wilcox.exact

See wilcox.test
chisq.correct

logical, correction factor for chisq.test
simulate.p.value

logical, simulate p-value for categorical tests (fe and chisq)
B

number of simulations to perform for simulation-based p-value
times

A vector of times to use for survival summaries.
...

additional arguments.

Value

A list with settings to be used within the tableby function.

Details

All tests can be turned off by setting test to FALSE. Otherwise, test are set to default settings in this list, or set explicitly in the formula of tableby.

If format.p is FALSE, digits.p denotes the number of significant digits shown. The p-values will be in exponential notation if necessary. If format.p is TRUE, digits.p will determine the number of digits after the decimal point to show. If the p-value is less than the resulting number of places, it will be formatted to show so.

Options for statistics are described more thoroughly in the vignette and are listed in tableby.stats

See also

anova, chisq.test, tableby, summary.tableby, tableby.stats.

Author

Jason Sinnwell, Beth Atkinson, Ethan Heinzen, Terry Therneau, adapted from SAS Macros written by Paul Novotny and Ryan Lennon

Examples

set.seed(100)
## make 3+ categories for Response
mdat <- data.frame(Response=c(0,0,0,0,0,1,1,1,1,1),
                   Sex=sample(c("Male", "Female"), 10,replace=TRUE),
                   Age=round(rnorm(10,mean=40, sd=5)),
                   HtIn=round(rnorm(10,mean=65,sd=5)))

## allow default summaries in RHS variables, and pass control args to
## main function, to be picked up with ... when calling tableby.control
outResp <- tableby(Response ~ Sex + Age + HtIn, data=mdat, total=FALSE, test=TRUE)
outCtl <- tableby(Response ~ Sex + Age + HtIn, data=mdat,
                  control=tableby.control(total=TRUE, cat.simplify=TRUE,
                  cat.stats=c("Nmiss","countpct"),digits=1))
summary(outResp, text=TRUE)

#> 
#> 
#> |             |     0 (N=5)     |     1 (N=5)     | p value|
#> |:------------|:---------------:|:---------------:|-------:|
#> |Sex          |                 |                 |   1.000|
#> |-  Female    |    3 (60.0%)    |    3 (60.0%)    |        |
#> |-  Male      |    2 (40.0%)    |    2 (40.0%)    |        |
#> |Age          |                 |                 |   0.449|
#> |-  Mean (SD) | 39.400 (3.435)  | 40.800 (1.924)  |        |
#> |-  Range     | 36.000 - 44.000 | 39.000 - 44.000 |        |
#> |HtIn         |                 |                 |   0.771|
#> |-  Mean (SD) | 66.600 (6.504)  | 65.600 (3.578)  |        |
#> |-  Range     | 60.000 - 77.000 | 61.000 - 69.000 |        |
#> 
summary(outCtl, text=TRUE)

#> 
#> 
#> |             |   0 (N=5)   |   1 (N=5)   | Total (N=10) | p value|
#> |:------------|:-----------:|:-----------:|:------------:|-------:|
#> |Sex          |  2 (40.0%)  |  2 (40.0%)  |  4 (40.0%)   |   1.000|
#> |Age          |             |             |              |   0.449|
#> |-  Mean (SD) | 39.4 (3.4)  | 40.8 (1.9)  |  40.1 (2.7)  |        |
#> |-  Range     | 36.0 - 44.0 | 39.0 - 44.0 | 36.0 - 44.0  |        |
#> |HtIn         |             |             |              |   0.771|
#> |-  Mean (SD) | 66.6 (6.5)  | 65.6 (3.6)  |  66.1 (5.0)  |        |
#> |-  Range     | 60.0 - 77.0 | 61.0 - 69.0 | 60.0 - 77.0  |        |
#>