histosmooth {EpiLPS} | R Documentation |

## Histogram smoothing with Laplacian-P-splines

### Description

This function provides a smooth density estimate to a histogram using Laplacian-P-splines. The B-spline basis is computed on the midpoints of the histogram bins. The default number of (cubic) B-splines is 30 and a third-order penalty is specified. The negative binomial distribution is used to model the number of observations falling in each bin.

### Usage

```
histosmooth(x, xl = min(x), xr = max(x), K = 30)
```

### Arguments

`x` |
A vector of real numbers from which the histogram will be constructed. |

`xl` |
The left bound for the domain of |

`xr` |
The right bound for the domain of |

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

### Value

A list containing the left (`xl`

)
and right (`xr`

) bounds of the domain of the estimated density, the
binwidth and a function to be evaluated between `xl`

and `xr`

.

### Author(s)

Oswaldo Gressani oswaldo_gressani@hotmail.fr

### References

Gressani, O. and Lambert, P. (2018). Fast Bayesian inference
using Laplace approximations in a flexible promotion time cure model based
on P-splines. *Computational Statistical & Data Analysis* **124**:
151-167.

Gressani, O., Wallinga, J., Althaus, C. L., Hens, N. and Faes, C.
(2022). EpiLPS: A fast and flexible Bayesian tool for estimation of the
time-varying reproduction number. *Plos Computational Biology*,
**18**(10): e1010618.

### Examples

```
# Old Faithful geyser application
data(eruptions)
x <- eruptions
ffit <- histosmooth(x, xl = 1, xr = 6)
tt <- seq(ffit$xl, ffit$xr, length = 500)
dtt <- tt[2] - tt[1]
graphics::hist(x, breaks = seq(ffit$xl, ffit$xr, by = ffit$binwidth),
freq = FALSE, ylim = c(0, 0.8), main = "Old Faithful Geyser",
xlab = "Eruption time (minutes)")
densfit <- sapply(tt, ffit$fdens)
densfit <- densfit / (sum(densfit * dtt))
graphics::lines(tt, densfit, col = "red", lwd = 2)
```

*EpiLPS*version 1.3.0 Index]