# convolve

##### Convolution of Sequences via FFT

Use the Fast Fourier Transform to compute the several kinds of convolutions of two sequences.

##### Usage

`convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))`

##### Arguments

- x, y
- numeric sequences
*of the same length*to be convolved. - conj
- logical; if
`TRUE`

, take the complex*conjugate*before back-transforming (default, and used for usual convolution). - type
- character; partially matched to
`"circular"`

,`"open"`

,`"filter"`

. For`"circular"`

, the two sequences are treated as*circular*, i.e., periodic.For

`"open"`

and`"filter"`

, the sequences are padded with`0`

s (from left and right) first;`"filter"`

returns the middle sub-vector of`"open"`

, namely, the result of running a weighted mean of`x`

with weights`y`

.

##### Details

The Fast Fourier Transform, `fft`

, is used for efficiency.

The input sequences `x`

and `y`

must have the same length if
`circular`

is true.

Note that the usual definition of convolution of two sequences
`x`

and `y`

is given by `convolve(x, rev(y), type = "o")`

.

##### Value

- If
`r <- convolve(x, y, type = "open")`

and`n <- length(x)`

,`m <- length(y)`

, then $$r_k = \sum_{i} x_{k-m+i} y_{i}$$ where the sum is over all valid indices $i$, for $k = 1, \dots, n+m-1$.If

`type == "circular"`

, $n = m$ is required, and the above is true for $i , k = 1,\dots,n$ when $x_{j} := x_{n+j}$ for $j < 1$.

##### References

Brillinger, D. R. (1981)
*Time Series: Data Analysis and Theory*, Second Edition.
San Francisco: Holden-Day.

##### See Also

`fft`

, `nextn`

, and particularly
`filter`

(from the

##### Examples

`library(stats)`

```
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*