estimIncub {EpiLPS} | R Documentation |

## Estimation of the incubation density based on coarse data

### Description

This function computes an estimate of the incubation density based on coarse data constructed from symptom onset times and exposure windows. It uses the Laplacian-P-splines methodology with a Langevinized Gibbs algorithm to sample from the posterior distribution.

### Usage

```
estimIncub(x, K = 10, niter = 1000, tmax = max(x), tgridlen = 500, verbose = FALSE)
```

### Arguments

`x` |
A data frame with the lower and upper bound of incubation interval. |

`K` |
Number of B-splines in the basis. |

`niter` |
The number of MCMC samples. |

`tmax` |
The upper bound for the B-spline basis. Default is the largest
data point in |

`tgridlen` |
The number of grid points on which to evaluate the density. |

`verbose` |
Should a message be printed? Default is FALSE. |

### Value

A list of class `incubestim`

containing summary values for
the estimated incubation density.

### Author(s)

Oswaldo Gressani oswaldo_gressani@hotmail.fr

### Examples

```
set.seed(123)
simdat <- incubsim(n = 30, tmax = 20) # Simulate incubation data
data <- simdat$Dobsincub # Incubation bounds
incubfit <- estimIncub(x = data, niter = 500, tmax = 20, verbose = TRUE)
```

[Package

*EpiLPS*version 1.3.0 Index]