# ewcdf

##### Weighted Empirical Cumulative Distribution Function

Compute a weighted version of the empirical cumulative distribution function.

- Keywords
- nonparametric, univar

##### Usage

`ewcdf(x, weights = NULL, normalise=TRUE, adjust=1)`

##### Arguments

- x
Numeric vector of observations.

- weights
Optional. Numeric vector of non-negative weights for

`x`

. Defaults to equal weight 1 for each entry of`x`

.- normalise
Logical value indicating whether the weights should be rescaled so that they sum to 1.

- adjust
Numeric value. Adjustment factor. The weights will be multiplied by

`adjust`

.

##### 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])`

.

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)`

except for the class membership.

The result of `ewcdf`

is a function, of class `"ewcdf"`

,
inheriting from the classes `"ecdf"`

(if `normalise=TRUE`

)
and `"stepfun"`

.
The class `ewcdf`

has methods for `print`

and `quantile`

.
The inherited classes `ecdf`

and `stepfun`

have methods for `plot`

and `summary`

.

##### Value

A function, of class `"ewcdf"`

, inheriting from
`"ecdf"`

(if `normalise=TRUE`

) and `"stepfun"`

.

##### See Also

`ecdf`

.

##### Examples

```
# NOT RUN {
x <- rnorm(100)
w <- runif(100)
plot(e <- ewcdf(x,w))
e
# }
```

*Documentation reproduced from package spatstat, version 1.61-0, License: GPL (>= 2)*