Ops.tf | R Documentation |
tf
These methods and operators mostly work arg
-value-wise on tf
objects, see
?groupGeneric
for implementation details.
## S3 method for class 'tf'
Ops(e1, e2)
## S3 method for class 'tfd'
e1 == e2
## S3 method for class 'tfd'
e1 != e2
## S3 method for class 'tfb'
e1 == e2
## S3 method for class 'tfb'
e1 != e2
## S3 method for class 'tfd'
Ops(e1, e2)
## S3 method for class 'tfb'
Ops(e1, e2)
## S3 method for class 'tfd'
Math(x, ...)
## S3 method for class 'tfb'
Math(x, ...)
## S3 method for class 'tf'
Summary(...)
## S3 method for class 'tfd'
cummax(...)
## S3 method for class 'tfd'
cummin(...)
## S3 method for class 'tfd'
cumsum(...)
## S3 method for class 'tfd'
cumprod(...)
## S3 method for class 'tfb'
cummax(...)
## S3 method for class 'tfb'
cummin(...)
## S3 method for class 'tfb'
cumsum(...)
## S3 method for class 'tfb'
cumprod(...)
e1 |
an |
e2 |
an |
x |
an |
... |
|
See examples below. Equality checks of functional objects are even more iffy
than usual for computer math and not very reliable. Note that max
and min
are not guaranteed to be maximal/minimal 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 the data on 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 for
small spline bases and/or very wiggly data.
a tf
- or logical
vector with the computed result
tf_fwise()
for scalar summaries of each function in a tf
-vector
set.seed(1859)
f <- tf_rgp(4)
2 * f == f + f
sum(f) == f[1] + f[2] + f[3] + f[4]
log(exp(f)) == f
plot(f, points = FALSE)
lines(range(f), col = 2, lty = 2)
f2 <- tf_rgp(5) |> exp() |> tfb(k = 25)
layout(t(1:3))
plot(f2, col = gray.colors(5))
plot(cummin(f2), col = gray.colors(5))
plot(cumsum(f2), col = gray.colors(5))
# ?tf_integrate for integrals, ?tf_fwise for scalar summaries of each function
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