pdLMS | R Documentation |

The LMS method defines frequency distributions in terms of L, M and S parameters.
`pdLMS`

plots one or more LMS distributions and optionally returns specified
centiles on each distribution.

```
pdLMS(
L = 1,
M = 1,
S = 0.2,
zcent = NULL,
zlim = 3.5,
N = 1000,
plot = TRUE,
...
)
```

`L` |
vector of Box-Cox transformation (lambda) values, L in the LMS method (default 1 corresponding to the Normal distribution). |

`M` |
vector of medians (mu), M in the LMS method (default 1). |

`S` |
vector of coefficients of variation (sigma), S in the LMS method (default 0.2). |

`zcent` |
optional vector of z-scores for conversion to the measurement scale under each distribution. |

`zlim` |
scalar defining z-score limits underlying x-axis (default 3.5). |

`N` |
number of points per distribution curve (default 1000). |

`plot` |
logical for plotting (default TRUE). |

`...` |
Further graphical parameters (see |

L, M and S should all be the same length, recycled if necessary.

An invisible list with the following components:

`x` |
vector of x values for plotting. |

`density` |
matrix of densities for each distribution. |

`centile` |
matrix of measurement centiles corresponding to |

The distributions can be plotted with `matplot(x, density, type='l')`

.

Tim Cole tim.cole@ucl.ac.uk

`z2cent`

, `LMS2z`

, `cLMS`

```
## plot normal distribution
pdLMS()
## compare variety of distributions
## with centiles corresponding to +3 z-scores
pdLMS(L=-2:3, M=2:3, S=1:3/10, zcent=3, lty=1)
```

sitar documentation built on July 9, 2023, 6:51 p.m.

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