tf (version 0.3.4)

functionwise: Summarize each tf in a vector

Description

These functions extract (user-specified) function-wise summary statistics from each entry in a tf-vector. To summarize a vector of functions at each argument value, see ?tfsummaries. Note that these will tend to yield lots of NAs for irregular tfd unless you set a tf_evaluator()-function that does inter- and extrapolation for them beforehand.

Usage

tf_fwise(x, .f, arg = tf_arg(x), ...)
tf_fmax(x, arg = tf_arg(x), na.rm = FALSE)
tf_fmin(x, arg = tf_arg(x), na.rm = FALSE)
tf_fmedian(x, arg = tf_arg(x), na.rm = FALSE)
tf_frange(x, arg = tf_arg(x), na.rm = FALSE, finite = FALSE)
tf_fmean(x, arg = tf_arg(x))
tf_fvar(x, arg = tf_arg(x))
tf_fsd(x, arg = tf_arg(x))
tf_crosscov(x, y, arg = tf_arg(x))
tf_crosscor(x, y, arg = tf_arg(x))

Value

a list (or vector) of the same length as x with the respective summaries

Arguments

x

a tf object

.f

a function or formula that is applied to each entry of x, see purrr::as_mapper() and Details.

arg

defaults to standard argument values of x

...

additional arguments for purrr::as_mapper()

na.rm

a logical indicating whether missing values should be removed.

finite

logical, indicating if all non-finite elements should be omitted.

y

a tf object

Functions

• tf_fwise(): User-specified function-wise summary statistics

• tf_fmax(): maximal value of each function

• tf_fmin(): minimal value of each function

• tf_fmedian(): median value of each function

• tf_frange(): range of values of each function

• tf_fmean(): mean of each function: \(\tfrac{1}{|T|}\int_T x_i(t) dt\)

• tf_fvar(): variance of each function: \(\tfrac{1}{|T|}\int_T (x_i(t) - \bar x(t))^2 dt\)

• tf_fsd(): standard deviation of each function: \(\sqrt{\tfrac{1}{|T|}\int_T (x_i(t) - \bar x(t))^2 dt}\)

• tf_crosscov(): cross-covariances between two functional vectors: \(\tfrac{1}{|T|}\int_T (x_i(t) - \bar x(t)) (y_i(t)-\bar y(t)) dt\)

• tf_crosscor(): cross-correlation between two functional vectors: tf_crosscov(x, y) / (tf_fsd(x) * tf_fsd(y))

Details

tf_fwise turns x into a list of data.frames with columns arg and values internally, so the function/formula in .f gets a data.frame .x with these columns, see examples below or source code for tf_fmin(), tf_fmax(), etc

See Also

Other tidyfun summary functions: tfsummaries

Examples

x <- tf_rgp(3)
layout(t(1:3))
plot(x, col = 1:3)
#  each function's values to [0,1]:
x_clamp <- (x - tf_fmin(x)) / (tf_fmax(x) - tf_fmin(x))
plot(x_clamp, col = 1:3)
# standardize each function to have mean / integral 0 and sd 1:
x_std <- (x - tf_fmean(x)) / tf_fsd(x)
tf_fvar(x_std) == c(1, 1, 1)
plot(x_std, col = 1:3)
# Custom functions:
# 80%tiles of each function's values:
tf_fwise(x, ~ quantile(.x$value, .8)) |> unlist()
# minimal value of each function for t >.5
tf_fwise(x, ~ min(.x$value[.x$arg > .5])) |> unlist()

tf_crosscor(x, -x)
tf_crosscov(x, x) == tf_fvar(x)