boxcoxlm {AID} | R Documentation |

`boxcoxlm`

performs Box-Cox transformation for linear models and provides graphical analysis of residuals after transformation.

```
boxcoxlm(x, y, method = "lse", lambda = seq(-3,3,0.01), lambda2 = NULL, plot = TRUE,
alpha = 0.05, verbose = TRUE)
```

`x` |
a nxp matrix, n is the number of observations and p is the number of variables. |

`y` |
a vector of response variable. |

`method` |
a character string to select the desired method to be used to estimate Box-Cox transformation parameter. To use Shapiro-Wilk test method should be set to "sw". For method = "ad", boxcoxnc function uses Anderson-Darling test to estimate Box-Cox transformation parameter. Similarly, method should be set to "cvm", "pt", "sf", "lt", "jb", "mle", "lse" to use Cramer-von Mises, Pearson Chi-square, Shapiro-Francia, Lilliefors and Jarque-Bera tests, maximum likelihood estimation and least square estimation, respectively. Default is set to method = "lse". |

`lambda` |
a vector which includes the sequence of candidate lambda values. Default is set to (-3,3) with increment 0.01. |

`lambda2` |
a numeric for an additional shifting parameter. Default is set to lambda2 = 0. |

`plot` |
a logical to plot histogram with its density line and qqplot of residuals before and after transformation. Defaults plot = TRUE. |

`alpha` |
the level of significance to assess the normality of residuals after transformation. Default is set to alpha = 0.05. |

`verbose` |
a logical for printing output to R console. |

Denote `y`

the variable at the original scale and `y'`

the transformed variable. The Box-Cox power transformation is defined by:

```
y' = \left\{ \begin{array}{ll}
\frac{y^\lambda - 1}{\lambda} = \beta_0 + \beta_1x_1 + ... + \epsilon \mbox{ , if $\lambda \neq 0$} \cr
log(y) = \beta_0 + \beta_1x_1 + ... + \epsilon \mbox{ , if $\lambda = 0$}
\end{array} \right.
```

If the data include any nonpositive observations, a shifting parameter `\lambda_2`

can be included in the transformation given by:

```
y' = \left\{ \begin{array}{ll}
\frac{(y + \lambda_2)^\lambda - 1}{\lambda} = \beta_0 + \beta_1x_1 + ... + \epsilon \mbox{ , if $\lambda \neq 0$} \cr
log(y + \lambda_2) = \beta_0 + \beta_1x_1 + ... + \epsilon \mbox{ , if $\lambda = 0$}
\end{array} \right.
```

Maximum likelihood estimation and least square estimation are equivalent while estimating Box-Cox power transformation parameter (Kutner et al., 2005). Therefore, these two methods return the same result.

A list with class "boxcoxlm" containing the following elements:

`method` |
method preferred to estimate Box-Cox transformation parameter |

`lambda.hat` |
estimate of Box-Cox Power transformation parameter based on corresponding method |

`lambda2` |
additional shifting parameter |

`statistic` |
statistic of normality test for residuals after transformation based on specified normality test in method. For mle and lse, statistic is obtained by Shapiro-Wilk test for residuals after transformation |

`p.value` |
p.value of normality test for residuals after transformation based on specified normality test in method. For mle and lse, p.value is obtained by Shapiro-Wilk test for residuals after transformation |

`alpha` |
the level of significance to assess normality of residuals |

`tf.y` |
transformed response variable |

`tf.residuals` |
residuals after transformation |

`y.name` |
response name |

`x.name` |
x matrix name |

Osman Dag, Ozlem Ilk

Asar, O., Ilk, O., Dag, O. (2017). Estimating Box-Cox Power Transformation Parameter via Goodness of Fit Tests. *Communications in Statistics - Simulation and Computation*, **46:1**, 91–105.

Kutner, M. H., Nachtsheim, C., Neter, J., Li, W. (2005). *Applied Linear Statistical Models. (5th ed.)*. New York: McGraw-Hill Irwin.

```
library(AID)
trees=as.matrix(trees)
boxcoxlm(x = trees[,1:2], y = trees[,3])
```

[Package *AID* version 2.9 Index]