simDegrossData {degross} | R Documentation |

## Simulation of grouped data and their sample moments to illustrate the degross density estimation procedure

### Description

Simulation of grouped data and their sample moments to illustrate the degross density estimation procedure

### Usage

```
simDegrossData(n, plotting=TRUE, choice=2, J=3)
```

### Arguments

`n` |
Desired sample size |

`plotting` |
Logical indicating whether the histogram of the simulated data should be plotted. Default: FALSE |

`choice` |
Integer in 1:3 indicating from which mixture of distributions to generate the data |

`J` |
Number of big bins |

### Value

A list containing tabulated frequencies and central moments of degrees 1 to 4 for data generated using a mixture density. This list contains :

`n`

:` `

total sample size.`J`

:` `

number of big bins.`Big.bins`

:` `

vector of length`J+1`

with the big bin limits.`freq.j`

:` `

vector of length`J`

with the observed big bin frequencies.`m.j`

:` `

`J`

by`4`

matrix with on each row the observed first four sample central moments within a given big bin.`true.density`

:` `

density of the raw data generating mechanism (to be estimated from the observed grouped data).`true.cdf`

:` `

cdf of the raw data generating mechanism (to be estimated from the observed grouped data).

### Author(s)

Philippe Lambert p.lambert@uliege.be

### References

Lambert, P. (2021) Moment-based density and risk estimation from grouped summary statistics. arXiv:2107.03883.

### See Also

### Examples

```
## Generate data
sim = simDegrossData(n=3500, plotting=TRUE, choice=2, J=3)
print(sim$true.density) ## Display density of the data generating mechanism
# Create a degrossData object
obj.data = with(sim, degrossData(Big.bins=Big.bins, freq.j=freq.j, m.j=m.j))
print(obj.data)
```

*degross*version 0.9.0 Index]