# ewcdf

From spatstat v1.18-1
by Adrian Baddeley

##### Weighted Empirical Cumulative Distribution Function

Compute a weighted version of the empirical cumulative distribution function.

- Keywords
- nonparametric, univar

##### Usage

`ewcdf(x, weights = rep(1/length(x), length(x)))`

##### Arguments

- x
- Numeric vector of observations.
- weights
- Numeric vector of non-negative weights
for
`x`

.

##### Details

This is a modification of the standard function `ecdf`

allowing the observations `x`

to have weights.

The weighted e.c.d.f. (empirical cumulative distribution function)
`Fn`

is defined so that, for any real number `y`

, the value of
`Fn(y)`

is equal to the total weight of all entries of
`x`

that are less than or equal to `y`

. That is
`Fn(y) = sum(weights[x <= y])<="" code="">.`

Thus `Fn`

is a step function which jumps at the
values of `x`

. The height of the jump at a point `y`

is the total weight of all entries in `x`

number of tied observations at that value. Missing values are
ignored.

If `weights`

is omitted, the default is equivalent to
`ecdf(x)`

.

##### Value

- A function, of class
`"ecdf"`

, inheriting from`"stepfun"`

.

##### See Also

##### Examples

```
x <- rnorm(100)
w <- runif(100)
plot(ewcdf(x,w))
```

*Documentation reproduced from package spatstat, version 1.18-1, License: GPL (>= 2)*

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