Functions that summarize tf objects across argument values

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 class 'tf'
mean(x, ...)

# S3 method for class 'tf'
median(x, na.rm = FALSE, depth = "MBD", ...)

sd(x, na.rm = FALSE)

# Default S3 method
sd(x, na.rm = FALSE)

# S3 method for class 'tf'
sd(x, na.rm = FALSE)

var(x, y = NULL, na.rm = FALSE, use)

# Default S3 method
var(x, y = NULL, na.rm = FALSE, use)

# S3 method for class 'tf'
var(x, y = NULL, na.rm = FALSE, use)

# S3 method for class 'tf'
summary(object, ..., depth = "MBD")

Arguments

x

a tf object.

...

optional additional arguments.

na.rm

logical. Should missing values be removed?

depth

depth method used for computing the median and central region. See tf_depth() for available methods, or pass a custom depth function. Defaults to "MBD".

y

NULL (default) or a vector, matrix or data frame with compatible dimensions to x. The default is equivalent to y = 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 functional median, the pointwise min and max of x, and the pointwise min and max of the central half of the functions in x, as defined by the chosen depth (default "MBD", see tf_depth()).

See also

tf_fwise()

Other tidyfun summary functions: fivenum(), functionwise

Examples

set.seed(123)
x <- tf_rgp(1) * 1:5
mean(x)
#> tfd[1]: [0,1] -> [-2.173123,2.158473] based on 51 evaluations each
#> interpolation by tf_approx_linear 
#> [1]: ████▇▆▅▃▂▁▁▁▁▂▃▅▆▇▇▇▇▇▇▆▆▆
median(x, depth = "pointwise")
#> tfd[1]: [0,1] -> [-2.173123,2.158473] based on 51 evaluations each
#> interpolation by tf_approx_linear 
#> [1]: ████▇▆▅▃▂▁▁▁▁▂▃▅▆▇▇▇▇▇▇▆▆▆
sd(x)
#> tfd[1]: [0,1] -> [0.02484437,1.145336] based on 51 evaluations each
#> interpolation by tf_approx_linear 
#> [1]: ███▇▅▄▁▃▆▇██▇▆▃▁▃▅▆▆▆▅▅▃▃▃
var(x)
#> tfd[1]: [0,1] -> [0.0006172427,1.311796] based on 51 evaluations each
#> interpolation by tf_approx_linear 
#> [1]: ▇██▆▄▂▁▁▄▆██▆▄▁▁▂▃▅▅▄▃▃▂▂▁
summary(x)
#> tfd[6]: [0,1] -> [-3.621872,3.597455] based on 51 evaluations each
#> interpolation by tf_approx_linear 
#> min      : ▅▅▅▅▅▅▅▃▂▁▁▁▁▂▃▅▅▅▅▅▅▅▅▅▅▅
#> lower_mid: ▆▆▆▆▆▅▅▄▂▂▁▁▂▂▃▅▅▅▆▆▆▅▅▅▅▅
#> median   : ▇▇▇▆▆▅▅▄▃▂▂▂▃▃▄▅▅▆▆▆▆▆▆▅▅▅
#> mean     : ▇▇▇▆▆▅▅▄▃▂▂▂▃▃▄▅▅▆▆▆▆▆▆▅▅▅
#> upper_mid: ▇██▇▇▆▅▄▃▃▃▃▃▃▄▅▆▆▇▇▇▆▆▆▆▆
#> max      : ████▇▆▅▄▄▄▄▄▄▄▄▅▆▇▇▇▇▇▇▆▆▆