# quantile.expert: Quantiles of the Expert Aggregated Distribution In expert: Modeling without data using expert opinion

## Description

Quantile for objects of class `"expert"`.

## Usage

 ```1 2 3``` ```## S3 method for class 'expert' quantile(x, probs = seq(0, 1, 0.25), smooth = FALSE, names = TRUE, ...) ```

## Arguments

 `x` an object of class `"expert"`. `probs` numeric vector of probabilities with values in [0, 1). `smooth` logical; when `TRUE` and `x` is a step function, quantiles are linearly interpolated between knots. `names` logical; if true, the result has a `names` attribute. Set to `FALSE` for speedup with many `probs`. `...` further arguments passed to or from other methods.

## Details

The quantiles are taken directly from the cumulative distribution function defined in `x`. Linear interpolation is available for step functions.

## Value

A numeric vector, named if `names` is `TRUE`.

`expert`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```x <- list(E1 <- list(A1 <- c(0.14, 0.22, 0.28), A2 <- c(130000, 150000, 200000), X <- c(350000, 400000, 525000)), E2 <- list(A1 <- c(0.2, 0.3, 0.4), A2 <- c(165000, 205000, 250000), X <- c(550000, 600000, 650000)), E3 <- list(A1 <- c(0.2, 0.4, 0.52), A2 <- c(200000, 400000, 500000), X <- c(625000, 700000, 800000))) probs <- c(0.1, 0.5, 0.9) true.seed <- c(0.27, 210000) fit <- expert(x, "cooke", probs, true.seed, 0.03) quantile(fit) # default probs quantile(fit, probs = c(0.9, 0.95, 0.99)) # right tail ```

### Example output

```      0%      25%      50%      75%     100%
305000.0 512930.5 563423.2 563423.2 845000.0
90%    95%    99%
628864 628864 628864
```

expert documentation built on May 2, 2019, 2:27 p.m.