pdLMS function

Plot frequency distributions(s) for given L, M and S values in LMS method

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,
  ...
)

Arguments

• 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 par) may also be supplied as arguments, particularly colour col, line type lty, line width lwd and character pch.

Returns

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 zcent

  under each distribution.

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

Details

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

Examples

## 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)

See Also

z2cent, LMS2z, cLMS

Author(s)

Tim Cole tim.cole@ucl.ac.uk