lms.yjn | R Documentation |

LMS quantile regression with the Yeo-Johnson transformation to normality. This family function is experimental and the LMS-BCN family function is recommended instead.

```
lms.yjn(percentiles = c(25, 50, 75), zero = c("lambda", "sigma"),
llambda = "identitylink", lsigma = "loglink",
idf.mu = 4, idf.sigma = 2,
ilambda = 1, isigma = NULL, rule = c(10, 5),
yoffset = NULL, diagW = FALSE, iters.diagW = 6)
lms.yjn2(percentiles = c(25, 50, 75), zero = c("lambda", "sigma"),
llambda = "identitylink", lmu = "identitylink", lsigma = "loglink",
idf.mu = 4, idf.sigma = 2, ilambda = 1.0,
isigma = NULL, yoffset = NULL, nsimEIM = 250)
```

`percentiles` |
A numerical vector containing values between 0 and 100, which are the quantiles. They will be returned as 'fitted values'. |

`zero` |
See |

`llambda, lmu, lsigma` |
See |

`idf.mu, idf.sigma` |
See |

`ilambda, isigma` |
See |

`rule` |
Number of abscissae used in the Gaussian integration scheme to work out elements of the weight matrices. The values given are the possible choices, with the first value being the default. The larger the value, the more accurate the approximation is likely to be but involving more computational expense. |

`yoffset` |
A value to be added to the response y, for the purpose
of centering the response before fitting the model to the data.
The default value, |

`diagW` |
Logical. This argument is offered because the expected information matrix may not be positive-definite. Using the diagonal elements of this matrix results in a higher chance of it being positive-definite, however convergence will be very slow. If |

`iters.diagW` |
Integer. Number of iterations in which the
diagonal elements of the expected information matrix are used.
Only used if |

`nsimEIM` |
See |

Given a value of the covariate, this function applies a
Yeo-Johnson transformation to the response to best obtain
normality. The parameters chosen to do this are estimated by
maximum likelihood or penalized maximum likelihood.
The function `lms.yjn2()`

estimates the expected information
matrices using simulation (and is consequently slower) while
`lms.yjn()`

uses numerical integration.
Try the other if one function fails.

An object of class `"vglmff"`

(see `vglmff-class`

).
The object is used by modelling functions
such as `vglm`

and `vgam`

.

The computations are not simple, therefore convergence may fail. In that case, try different starting values.

The generic function `predict`

, when applied to a
`lms.yjn`

fit, does not add back the `yoffset`

value.

As described above, this family function is experimental and the LMS-BCN family function is recommended instead.

The response may contain both positive and negative values. In contrast, the LMS-Box-Cox-normal and LMS-Box-Cox-gamma methods only handle a positive response because the Box-Cox transformation cannot handle negative values.

Some other notes can be found at `lms.bcn`

.

Thomas W. Yee

Yeo, I.-K. and Johnson, R. A. (2000).
A new family of power transformations to improve normality or
symmetry.
*Biometrika*,
**87**, 954–959.

Yee, T. W. (2004).
Quantile regression via vector generalized additive models.
*Statistics in Medicine*, **23**, 2295–2315.

Yee, T. W. (2002).
An Implementation for Regression Quantile Estimation.
Pages 3–14.
In: Haerdle, W. and Ronz, B.,
*Proceedings in Computational Statistics COMPSTAT 2002*.
Heidelberg: Physica-Verlag.

`lms.bcn`

,
`lms.bcg`

,
`qtplot.lmscreg`

,
`deplot.lmscreg`

,
`cdf.lmscreg`

,
`bmi.nz`

,
`amlnormal`

.

```
fit <- vgam(BMI ~ s(age, df = 4), lms.yjn, bmi.nz, trace = TRUE)
head(predict(fit))
head(fitted(fit))
head(bmi.nz)
# Person 1 is near the lower quartile of BMI amongst people his age
head(cdf(fit))
## Not run:
# Quantile plot
par(bty = "l", mar = c(5, 4, 4, 3) + 0.1, xpd = TRUE)
qtplot(fit, percentiles = c(5, 50, 90, 99), main = "Quantiles",
xlim = c(15, 90), las = 1, ylab = "BMI", lwd = 2, lcol = 4)
# Density plot
ygrid <- seq(15, 43, len = 100) # BMI ranges
par(mfrow = c(1, 1), lwd = 2)
(Z <- deplot(fit, x0 = 20, y = ygrid, xlab = "BMI", col = "black",
main = "PDFs at Age = 20 (black), 42 (red) and 55 (blue)"))
Z <- deplot(fit, x0 = 42, y = ygrid, add = TRUE, llty = 2, col = "red")
Z <- deplot(fit, x0 = 55, y = ygrid, add = TRUE, llty = 4, col = "blue",
Attach = TRUE)
with(Z@post, deplot) # Contains PDF values; == a@post$deplot
## End(Not run)
```

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