Find out where functional data fulfills certain conditions.

tf_where allows to define a logical expression about the function values and returns the argument values for which that condition is true.
tf_anywhere is syntactic sugar for tf_where with return = "any" to get a logical flag for each function if the condition is TRUE anywhere, see below.

Usage

tf_where(f, cond, return = c("all", "first", "last", "range", "any"), arg)

tf_anywhere(f, cond, arg)

Arguments

f: a tf object
cond: a logical expression about value (and/or arg) that defines a condition about the functions, see examples and details.
return: for each entry in f, tf_where either returns all arg for which cond is true, the first, the last or their range or logical flags whether the functions fullfill the condition anywhere. For "range", note that cond may not be true for all arg values in this range, though, this is not checked.
arg: optional arg-values on which to evaluate f and check cond, defaults to tf_arg(f).

Value

depends on return:

return = "any", i.e, anywhere: a logical vector of the same length as f.
return = "all": a list of vectors of the same length as f, with empty vectors for the functions that never fulfill the condition.
return = "range": a data frame with columns "begin" and "end".
else, a numeric vector of the same length as f with NA for entries of f that nowhere fulfill the condition.

Details

Entries in f that do not fulfill cond anywhere yield numeric(0).
cond is evaluated as a base::subset()-statement on a data.frame containing a single entry in f with columns arg and value, so most of the usual dplyr tricks are available as well, see examples.
Any condition evaluates to NA on NA-entries in f.

Examples

lin <- 1:4 * tfd(seq(-1, 1, length.out = 11), seq(-1, 1, length.out = 11))
tf_where(lin, value %inr% c(-1, 0.5))
#> [[1]]
#> [1] -1.0 -0.8 -0.6 -0.4 -0.2  0.0  0.2  0.4
#> 
#> [[2]]
#> [1] -0.4 -0.2  0.0  0.2
#> 
#> [[3]]
#> [1] -0.2  0.0
#> 
#> [[4]]
#> [1] -0.2  0.0
#> 
tf_where(lin, value %inr% c(-1, 0.5), "range")
#>   begin end
#> 1  -1.0 0.4
#> 2  -0.4 0.2
#> 3  -0.2 0.0
#> 4  -0.2 0.0
a <- 1
tf_where(lin, value > a, "first")
#> [1]  NA 0.6 0.4 0.4
tf_where(lin, value < a, "last")
#> [1] 0.8 0.4 0.2 0.2
tf_where(lin, value > 2, "any")
#> [1] FALSE FALSE  TRUE  TRUE
tf_anywhere(lin, value > 2)
#> [1] FALSE FALSE  TRUE  TRUE

set.seed(4353)
f <- tf_rgp(5, 11)
plot(f, pch = as.character(1:5), points = TRUE)

tf_where(f, value == max(value))
#> $`1`
#> [1] 0
#> 
#> $`2`
#> [1] 0.4
#> 
#> $`3`
#> [1] 0.2
#> 
#> $`4`
#> [1] 0.6
#> 
#> $`5`
#> [1] 0.1
#> 
# where is the function increasing/decreasing?
tf_where(f, value > dplyr::lag(value, 1, value[1]))
#> $`1`
#> [1] 0.4 0.5 0.6
#> 
#> $`2`
#> [1] 0.1 0.2 0.3 0.4
#> 
#> $`3`
#> [1] 0.1 0.2 0.6 0.7 0.8 0.9 1.0
#> 
#> $`4`
#> [1] 0.1 0.2 0.3 0.4 0.5 0.6 1.0
#> 
#> $`5`
#> [1] 0.1 0.7 0.8 0.9 1.0
#> 
tf_where(f, value < dplyr::lead(value, 1, tail(value, 1)))
#> $`1`
#> [1] 0.3 0.4 0.5
#> 
#> $`2`
#> [1] 0.0 0.1 0.2 0.3
#> 
#> $`3`
#> [1] 0.0 0.1 0.5 0.6 0.7 0.8 0.9
#> 
#> $`4`
#> [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.9
#> 
#> $`5`
#> [1] 0.0 0.6 0.7 0.8 0.9
#> 
# where are the (interior) extreme points (sign changes of `diff(value)`)?
tf_where(
  f,
  sign(c(diff(value)[1], diff(value))) !=
    sign(c(diff(value), tail(diff(value), 1)))
)
#> $`1`
#> [1] 0.3 0.6
#> 
#> $`2`
#> [1] 0.4
#> 
#> $`3`
#> [1] 0.2 0.5
#> 
#> $`4`
#> [1] 0.6 0.9
#> 
#> $`5`
#> [1] 0.1 0.6
#> 
# where in its second half is the function positive?
tf_where(f, arg > 0.5 & value > 0)
#> $`1`
#> numeric(0)
#> 
#> $`2`
#> [1] 0.6 0.7 0.8 0.9 1.0
#> 
#> $`3`
#> [1] 0.9 1.0
#> 
#> $`4`
#> [1] 0.6 0.7 0.8 0.9 1.0
#> 
#> $`5`
#> [1] 1
#> 
# does the function ever exceed?
tf_anywhere(f, value > 1)
#>     1     2     3     4     5 
#> FALSE  TRUE FALSE  TRUE FALSE