These define methods and operators that mostly work argval-wise on tf objects, see ?groupGeneric for implementation details.

# S3 method for tf
Ops(e1, e2)

# S3 method for tfd
==(e1, e2)

# S3 method for tfd
!=(e1, e2)

# S3 method for tfb
==(e1, e2)

# S3 method for tfb
!=(e1, e2)

# S3 method for tfd
Ops(e1, e2)

# S3 method for tfb
Ops(e1, e2)

# S3 method for tfd
Math(x, ...)

# S3 method for tfb
Math(x, ...)

# S3 method for tfd
cummax(...)

# S3 method for tfd
cummin(...)

# S3 method for tfd
cumsum(...)

# S3 method for tfd
cumprod(...)

# S3 method for tfb
cummax(...)

# S3 method for tfb
cummin(...)

# S3 method for tfb
cumsum(...)

# S3 method for tfb
cumprod(...)

# S3 method for tf
Summary(...)

## Arguments

e1 an tf or a numeric vector an tf or a numeric vector an tf tf-objects (not used for Math group generic)

## Details

See examples below. Equality checks of functional objects are rather iffy and not very reliable at this point. Note that max and min are not guaranteed to be maximal/minmal over the entire domain, only on the evaluation grid used for computation. With the exception of addition and multiplication, operations on tfb-objects first evaluate them over their arg, perform computations on these evaluations and then convert back to an tfb- object, so a loss of precision should be expected, especially so if bases are small or data is very wiggly.

## Examples

set.seed(1859)
f <- tf_rgp(4)
2 * f == f + f#>  TRUE TRUE TRUE TRUEsum(f) == f + f + f + f#>  TRUElog(exp(f)) == f#>  TRUE TRUE TRUE TRUEplot(f, points = FALSE)lines(range(f), col = 2, lty = 2) f2 <- tfb(tf_rgp(5), k = 50)#> Percentage of input data variability preserved in basis representation
#> (per functional observation, approximate):#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
#>   99.70   99.80   99.90   99.86   99.90  100.00 layout(t(1:2))
plot(f2, col = 1:5)
plot(cumsum(f2), col = 1:5)#> Percentage of input data variability preserved in basis representation
#> (per functional observation, approximate):#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
#>     100     100     100     100     100     100 # use ?tf_integrate.tfd for "function-wise" integrals i.e., weighted cumulative sums...
lines(f2) 