This function acts as a skeleton for a truncated distribution defined by
model type, maximum value and model parameters. It is designed to be used
with the output from `get_dist()`

.

## Arguments

- n
Numeric vector, number of samples to take (or days for the probability density).

- dist
Logical, defaults to

`FALSE`

. Should the probability density be returned rather than a number of samples.- cum
Logical, defaults to

`TRUE`

. If`dist = TRUE`

should the returned distribution be cumulative.- model
Character string, defining the model to be used. Supported options are exponential ("exp"), gamma ("gamma"), and log normal ("lognormal")

- discrete
Logical, defaults to

`FALSE`

. Should the probability distribution be discretised. In this case each entry of the probability mass function corresponds to the 2-length interval ending at the entry except for the first interval that covers (0, 1). That is, the probability mass function is a vector where the first entry corresponds to the integral over the (0,1] interval of the continuous distribution, the second entry corresponds to the (0,2] interval, the third entry corresponds to the (1, 3] interval etc.- params
A list of parameters values (by name) required for each model. For the exponential model this is a rate parameter and for the gamma model this is alpha and beta.

- max_value
Numeric, the maximum value to allow. Defaults to 120. Samples outside of this range are resampled.

## Examples

```
## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))
#> [1] 0 1 0 0 0 0 1 0 0 0
# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))
#> [1] 0.6321206 0.8646647 0.9502129 0.9816844 0.9932621 0.9975212 0.9990881
#> [8] 0.9996645 0.9998766 0.9999546
# probability density
dist_skel(1:10,
model = "exp", dist = TRUE,
cum = FALSE, params = list(rate = 1)
)
#> [1] 2.325442e-01 8.554821e-02 3.147143e-02 1.157769e-02 4.259195e-03
#> [6] 1.566870e-03 5.764193e-04 2.120528e-04 7.800987e-05 2.869823e-05
## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(shape = 1, rate = 0.5))
#> [1] 2 0 2 1 0 0 7 0 0 2
# cumulative prob density
dist_skel(0:10,
model = "gamma", dist = TRUE,
params = list(shape = 1, rate = 0.5)
)
#> [1] 0.0000000 0.3934693 0.6321206 0.7768698 0.8646647 0.9179150 0.9502129
#> [8] 0.9698026 0.9816844 0.9888910 0.9932621
# probability density
dist_skel(0:10,
model = "gamma", dist = TRUE,
cum = FALSE, params = list(shape = 2, rate = 0.5)
)
#> [1] 0.09020401 0.17403711 0.17793348 0.15181955 0.11870835 0.08814922
#> [7] 0.06326005 0.04431003 0.03047871 0.02067180 0.01386367
## Log normal model
# sample
dist_skel(10,
model = "lognormal", params = list(meanlog = log(5), sdlog = log(2))
)
#> [1] 6 9 3 6 3 15 6 3 4 3
# cumulative prob density
dist_skel(0:10,
model = "lognormal", dist = TRUE,
params = list(meanlog = log(5), sdlog = log(2))
)
#> [1] 0.00000000 0.01011843 0.09309625 0.23057213 0.37375439 0.50000107
#> [7] 0.60373926 0.68631467 0.75113856 0.80178187 0.84134655
# probability density
dist_skel(0:10,
model = "lognormal", dist = TRUE, cum = FALSE,
params = list(meanlog = log(5), sdlog = log(2))
)
#> [1] 0.01011843 0.08297782 0.13747588 0.14318225 0.12624669 0.10373819
#> [7] 0.08257540 0.06482389 0.05064331 0.03956468 0.03099139
```