sitar (version 1.1.2)

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

Description

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.

Usage

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.

Value

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.

See Also

z2cent, LMS2z, cLMS

Examples

# NOT RUN {
## 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)

# }

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