Description Usage Arguments Value Note Author(s) See Also Examples
A quick and easy function for describing datasets.
1 2 3 4 5 6 7 8 9 10 11 12 | cross(formula = cbind(...) ~ ., data = NULL, funs = c(` ` =
mysummary), ..., margin = 0:2, total = FALSE, digits = 2,
showNA = c("no", "ifany", "always"), method = c("pearson", "kendall",
"spearman"), times = NULL, followup = FALSE, test = FALSE,
test.summarize = test.summarize.auto,
test.survival = test.survival.logrank,
test.tabular = test.tabular.auto, show.test = display.test,
plim = 4, show.method = TRUE, effect = FALSE,
effect.summarize = effect.diff.mean.auto,
effect.tabular = effect.or.row.by.col,
effect.survival = effect.survival.coxph, conf.level = 0.95,
label = FALSE, regroup = FALSE)
|
formula |
a formula (see Details). |
data |
a data.frame. |
funs |
Functions used for describing numeric
variables. Vector (named or not): |
... |
further arguments, all passed to funs. For example na.rm = TRUE |
margin |
index, or vector of indices to indicate which proportions should be computed in frequency tables (0: cell, 1: row, 2: col). |
total |
whether to add margins. Integers ( |
digits |
number of digits |
showNA |
whether to show NA ( |
method |
a character string indicating which correlation
coefficient is to be used. One of |
times |
vector of times (see |
followup |
whether to display follow-up time. |
test |
whether to perform tests |
test.summarize |
a function of two arguments (continuous
variable and grouping variable), used to compare continuous
variable. Returns a list of two components : |
test.survival |
a function of one argument (a formula), used
to compare survival estimations. Returns the same components as
created by |
test.tabular |
a function of two arguments (two categorical
variables), used to test association between two factors. Returns
the same components as created by |
show.test |
function used to display the test result. See
|
plim |
number of digits for the p value |
show.method |
wether to display the test name (logical) |
effect |
whether to compute a effect measure |
effect.summarize |
a function of three arguments (continuous
variable, grouping variable and conf.level), used to compare continuous
variable. Returns a list of five components : |
effect.tabular |
a function of three arguments (two categorical variables and conf.level) used to measure the associations between two factors.
Returns a list of five components : |
effect.survival |
a function of two argument (a formula and conf.level), used
to measure the association between a consored and a factor. Returns the same components as
created by |
conf.level |
The desired confidence interval level |
label |
whether to display labels of variables) |
regroup |
whether to regroup numerics with numerics and
factors with factors in |
A data.frame, or a list of data.frames.
The formula has the following format: x_1 + x_2 +
... ~ y_1 + y_2 + ...
There are a couple of special variables: ...
represents all
other variables not used in the formula and .
represents
no variable, so you can do formula = var1 ~ .
.
If var1
is numeric, var1 ~ .
produce a summary table
using funs
. If var1
is a factor, var1 ~ .
produce a frequency table. If var1
is of class Surv
,
var1 ~ .
produce a table with the estimates of survival at
times
. If var1
is numeric and var2
is
numeric, var1 ~ var2
produces a correlation correlation
coefficient. if var1
is numeric and var2
is a
factor, var1 ~ var2
produce a summary table (using
functions in funs
) according to the levels of
var2
. If var1
is a factor and var2
is a
factor, var1 ~ var2
produce a contingency table. If
var1
is of class Surv
and var2
is a factor,
var1 ~ var2
produce a table with the estimates of survival
for each level of var2
.
You can group several variables together with cbind(var1,
var2, var3)
: var1
, var2
and var3
will be
grouped in the same table. cbind(...)
works (ie regroups
all variables of the data.frame together). When a cbind
is
in both sides of the formula, cross
will do its best to
group everything in the same table, but only if it is possible...
David Hajage, inspired by the design and the code of
summary.formula
(Hmisc
package, FE Harrell) and
cast
(reshape
package, H Wickham).
cast
(reshape) and summary.formula
(Hmisc).
1 2 3 4 5 6 7 8 9 10 | library(biostat2)
cross(data = iris)
cross(cbind(...) ~ ., iris[, sapply(iris, is.numeric)], funs = c(median, mad, min, max))
cross(cbind(Sepal.Length, I(Sepal.Width^2)) ~ Species, iris, funs = quantile, probs = c(1/3, 2/3))
cross(Sepal.Length + Sepal.Width ~ Petal.Length + Petal.Width, iris)
cross(cbind(Sepal.Length, Sepal.Width) ~ cbind(Petal.Length, Petal.Width), iris)
cross(... ~ ., esoph)
cross(alcgp ~ tobgp, esoph, test = TRUE)
library(survival)
cross(Surv(time, status) ~ x, data = aml)
|
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