Accessing, evaluating, subsetting and subassigning tf vectors

These functions access, subset, replace and evaluate tf objects. For more information on creating tf objects and converting them to/from list, data.frame or matrix, see tfd() and tfb(). See Details.

Usage

# S3 method for tf
[(x, i, j, interpolate = TRUE, matrix = TRUE)

# S3 method for tf
[(x, i) <- value

Arguments

x: an tf
i: index of the observations (integerish, character or logical, usual R rules apply)
j: The arg used to evaluate the functions. A (list of) numeric vectors. NOT interpreted as a column number but as the argument value of the respective functional datum.
interpolate: should functions be evaluated (i.e., inter-/extrapolated) for values in arg for which no original data is available? Only relevant for the raw data class tfd, for which it defaults to TRUE. Basis-represented tfb are always "interpolated".
matrix: should the result be returned as a matrix or as a list of data.frames? If TRUE, j has to be a (list of a) single vector of arg. See return value.
value: tf object for subassignment. This is typed more strictly than concatenation: subassignment only happens if the common type of value and x is the same as the type of x, so subassignment never changes the type of x but may do a potentially lossy cast of value to the type of x (with a warning).

Value

If j is missing, a subset of the functions in x as given by i.
If j is given and matrix == TRUE, a numeric matrix of function evaluations in which each row represents one function and each column represents one argval as given in argument j, with an attribute arg=j and row- and column-names derived from x[i] and j.
If j is given and matrix == FALSE, a list of tbl_dfs with columns arg = j and value = evaluations at j for each observation in i.

Details

Note that these break certain (terrible) R conventions for vector-like objects:

no argument recycling,
no indexing with NA,
no indexing with names not present in x,
no indexing with integers > length(x)

All of the above will trigger errors.

Examples

x <- 1:3 * tfd(data = 0:10, arg = 0:10)
plot(x)

# this operator's 2nd argument is quite overloaded -- you can:
# 1. simply extract elements from the vector if no second arg is given:
x[1]
#> tfd[1] on (0,10) based on 11 evaluations each
#> interpolation by tf_approx_linear 
#> [1]: (0,0);(1,1);(2,2); ...
x[c(TRUE, FALSE, FALSE)]
#> tfd[1] on (0,10) based on 11 evaluations each
#> interpolation by tf_approx_linear 
#> [1]: (0,0);(1,1);(2,2); ...
x[-(2:3)]
#> tfd[1] on (0,10) based on 11 evaluations each
#> interpolation by tf_approx_linear 
#> [1]: (0,0);(1,1);(2,2); ...
# 2. use the second argument and optional additional arguments to
#    extract specific function evaluations in a number of formats:
x[1:2, c(4.5, 9)] # returns a matrix of function evaluations
#>      4.5  9
#> [1,] 4.5  9
#> [2,] 9.0 18
#> attr(,"arg")
#> [1] 4.5 9.0
x[1:2, c(4.5, 9), interpolate = FALSE] # NA for arg-values not in the original data
#> Warning: interpolate = FALSE & no evaluations for some <j>: NAs created.
#>      4.5  9
#> [1,]  NA  9
#> [2,]  NA 18
#> attr(,"arg")
#> [1] 4.5 9.0
x[-3, seq(1, 9, by = 2), matrix = FALSE] # list of data.frames for each function
#> [[1]]
#>   arg value
#> 1   1     1
#> 2   3     3
#> 3   5     5
#> 4   7     7
#> 5   9     9
#> 
#> [[2]]
#>   arg value
#> 1   1     2
#> 2   3     6
#> 3   5    10
#> 4   7    14
#> 5   9    18
#> 
# in order to evaluate a set of observed functions on a new grid and
# save them as a functional data vector again, use `tfd` or `tfb` instead:
tfd(x, arg = seq(0, 10, by = 0.01))
#> tfd[3] on (0,10) based on 1001 evaluations each
#> interpolation by tf_approx_linear 
#> [1]: (0.00,0.00);(0.01,0.01);(0.02,0.02); ...
#> [2]: (0.00,0.00);(0.01,0.02);(0.02,0.04); ...
#> [3]: (0.00,0.00);(0.01,0.03);(0.02,0.06); ...