desbeta {detectnorm} | R Documentation |

## Calculate skewness and kurtosis based on Beta distribution in one group

### Description

This function can be used to calculate the skewness and kurtosis based on the Beta distribution. Also, this function estimate the shape parameters alpha and beta.

### Usage

```
desbeta(
vmean,
vsd,
lo,
hi,
method = "MM",
rawdata = NULL,
showFigure = FALSE,
...
)
```

### Arguments

`vmean` |
sample mean of the truncated data |

`vsd` |
sample standard deviation of the truncated data |

`lo` |
minimum possible value |

`hi` |
maximum possible value |

`method` |
when method = 'MM', the method used is the method of moments, when method = "ML', the method used to estimate the distribution is maximum likelihood |

`rawdata` |
when raw data is available, we could still use it to check it figuratively, if the data was closed to the normal distribution, or truncated normal distribution. |

`showFigure` |
when showFigure = TRUE, it will display the plots with theoretical normal curve and the truncated normal curve. |

`...` |
other arguments |

### Value

If 'showFigure = TRUE', the output will be a list with two objects: one is the data frame of shape parameters (alpha and beta), mean and standard deviation of standard beta distribution (mean and sd), and skewness and kurtosis; the other is the theoretical figures of beta and normal distributions. If 'showFigure = FALSE', the output will be only the data frame.

### References

Johnson NL, Kotz S, Balakrishnan N (1995). “Continuous univariate distributions.” In volume 289, chapter 25 Beta Distributions. John wiley & sons.

Smithson M, Verkuilen J (2006).
“A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables.”
*Psychological methods*, **11**(1), 54.

### See Also

### Examples

```
data('beta_mdat')
desbeta(vmean=beta_mdat$m2[6], vsd=beta_mdat$sd2[6],
hi = beta_mdat$hi2[6], lo = beta_mdat$lo2[6], showFigure = TRUE)
```

*detectnorm*version 1.0.0 Index]