discretize {actuar} R Documentation

## Discretization of a Continuous Distribution

### Description

Compute a discrete probability mass function from a continuous cumulative distribution function (cdf) with various methods.

`discretise` is an alias for `discretize`.

### Usage

```discretize(cdf, from, to, step = 1,
method = c("upper", "lower", "rounding", "unbiased"),
lev, by = step, xlim = NULL)

discretise(cdf, from, to, step = 1,
method = c("upper", "lower", "rounding", "unbiased"),
lev, by = step, xlim = NULL)
```

### Arguments

 `cdf` an expression written as a function of `x`, or alternatively the name of a function, giving the cdf to discretize. `from, to` the range over which the function will be discretized. `step` numeric; the discretization step (or span, or lag). `method` discretization method to use. `lev` an expression written as a function of `x`, or alternatively the name of a function, to compute the limited expected value of the distribution corresponding to `cdf`. Used only with the `"unbiased"` method. `by` an alias for `step`. `xlim` numeric of length 2; if specified, it serves as default for `c(from, to)`.

### Details

Usage is similar to `curve`.

`discretize` returns the probability mass function (pmf) of the random variable obtained by discretization of the cdf specified in `cdf`.

Let F(x) denote the cdf, E[min(X, x)]] the limited expected value at x, h the step, p[x] the probability mass at x in the discretized distribution and set a = `from` and b = `to`.

Method `"upper"` is the forward difference of the cdf F:

p[x] = F(x + h) - F(x)

for x = a, a + h, …, b - step.

Method `"lower"` is the backward difference of the cdf F:

p[x] = F(x) - F(x - h)

for x = a + h, …, b and p[a] = F(a).

Method `"rounding"` has the true cdf pass through the midpoints of the intervals [x - h/2, x + h/2):

p[x] = F(x + h/2) - F(x - h/2)

for x = a + h, …, b - step and p[a] = F(a + h/2). The function assumes the cdf is continuous. Any adjusment necessary for discrete distributions can be done via `cdf`.

Method `"unbiased"` matches the first moment of the discretized and the true distributions. The probabilities are as follows:

p[a] = (E[min(X, a)] - E[min(X, a + h)])/h + 1 - F(a)

p[x] = (2 E[min(X, x)] - E[min(X, x - h)] - E[min(X, x + h)])/h, a < x < b

p[b] = (E[min(X, b)] - E[min(X, b - h)])/h - 1 + F(b).

### Value

A numeric vector of probabilities suitable for use in `aggregateDist`.

### Author(s)

Vincent Goulet vincent.goulet@act.ulaval.ca

### References

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.

### See Also

`aggregateDist`

### Examples

```x <- seq(0, 5, 0.5)

op <- par(mfrow = c(1, 1), col = "black")

## Upper and lower discretization
fu <- discretize(pgamma(x, 1), method = "upper",
from = 0, to = 5, step = 0.5)
fl <- discretize(pgamma(x, 1), method = "lower",
from = 0, to = 5, step = 0.5)
curve(pgamma(x, 1), xlim = c(0, 5))
par(col = "blue")
plot(stepfun(head(x, -1), diffinv(fu)), pch = 19, add = TRUE)
par(col = "green")
plot(stepfun(x, diffinv(fl)), pch = 19, add = TRUE)
par(col = "black")

## Rounding (or midpoint) discretization
fr <- discretize(pgamma(x, 1), method = "rounding",
from = 0, to = 5, step = 0.5)
curve(pgamma(x, 1), xlim = c(0, 5))
par(col = "blue")
plot(stepfun(head(x, -1), diffinv(fr)), pch = 19, add = TRUE)
par(col = "black")

## First moment matching
fb <- discretize(pgamma(x, 1), method = "unbiased",
lev = levgamma(x, 1), from = 0, to = 5, step = 0.5)
curve(pgamma(x, 1), xlim = c(0, 5))
par(col = "blue")
plot(stepfun(x, diffinv(fb)), pch = 19, add = TRUE)

par(op)
```

[Package actuar version 3.1-4 Index]