These will return a tf
object containing the respective functional
statistic. See tf_fwise()
for scalar summaries
(e.g. tf_fmean
for means, tf_fmax
for max. values) of each entry
in a tf
-vector.
Usage
# S3 method for tf
mean(x, ...)
# S3 method for tf
median(x, na.rm = FALSE, depth = c("MBD", "pointwise"), ...)
sd(x, na.rm = FALSE)
# S3 method for default
sd(x, na.rm = FALSE)
# S3 method for tf
sd(x, na.rm = FALSE)
var(x, y = NULL, na.rm = FALSE, use)
# S3 method for default
var(x, y = NULL, na.rm = FALSE, use)
# S3 method for tf
var(x, y = NULL, na.rm = FALSE, use)
# S3 method for tf
summary(object, ...)
Arguments
- x
a
tf
object- ...
optional additional arguments.
- na.rm
logical. Should missing values be removed?
- depth
method used to determine the most central element in
x
, i.e., the median. One of the functional data depths available viatf_depth()
or"pointwise"
for a pointwise median function.- y
NULL
(default) or a vector, matrix or data frame with compatible dimensions tox
. The default is equivalent toy = x
(but more efficient).- use
an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings
"everything"
,"all.obs"
,"complete.obs"
,"na.or.complete"
, or"pairwise.complete.obs"
.- object
a
tfd
object
Value
a tf
object with the computed result.
summary.tf
returns a tf
-vector with the mean function, the
variance function, the functional median, and the functional range
(i.e., pointwise min/max) of the central half of the functions,
as defined by tf_depth()
.
See also
Other tidyfun summary functions:
functionwise