Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function is design to help the user to easily construct growth curve centile estimation. It is applicable when only "one" explanatory variable is available (usually age).
1 2 3 4 5 6 7 8  lms(y, x, families = LMS, data = NULL, k = 2,
cent = c(0.4, 2, 10, 25, 50, 75, 90, 98, 99.6),
calibration = TRUE, trans.x = FALSE,
fix.power = NULL, lim.trans = c(0, 1.5),
prof = FALSE, step = 0.1, legend = FALSE,
mu.df = NULL, sigma.df = NULL, nu.df = NULL,
tau.df = NULL, c.crit = 0.01,
method.pb = c("ML", "GAIC"), ...)

y 
The response variable 
x 
The unique explanatory variable 
families 
a list of 
data 
the data frame 
k 
the penalty to be used in the GAIC 
cent 
a vector with elements the % centile values for which the centile curves have to be evaluated 
calibration 
whether calibration is required with default 
trans.x 
whether to check for transformation in x with default 
fix.power 
if set it fix the power of the transformation for x 
lim.trans 
the limits for the search of the power parameter for x 
prof 
whether to use the profile GAIC of the power tranformation 
step 
if codeprof=TRUE is used this determine the step for the profile GAIC 
legend 
whether a legend is required in the plot with default 
mu.df 

sigma.df 

nu.df 

tau.df 

c.crit 
the convergence critetion to be pass to 
method.pb 
the method used in the 
... 
extra argument which can be passed to 
This function should be used if the construction of the centile curves involves only one explanatory variable.
The model assumes that the response variable has a flexible distribution i.e. y ~ D(μ, σ, ν, τ) where the parameters of the distribution are smooth functions of the explanatory variable i.e. g(μ)= s(x), where g() is a link function and s() is a smooth function. Occasionally a power transformation in the xaxis helps the construction of the centile curves. That is, in this case the parameters are modelled by x^p rather than just x, i.e.g(μ)= s(x^p). The function lms()
uses Psplines (pb()
) as a smoother.
If a transformation is needed for x
the function lms()
starts by finding an optimum value for p
using the simple model NO(μ=s(x^p)). (Note that this value of p
is not the optimum for the final chosen model but it works well in practice.)
After fitting a Normal error model for staring values the function proceeds by fitting several "appropriate" distributions for the response variable.
The set of gamlss.family
distributions to fit is specified by the argument families
.
The default families
arguments is LMS=c("BCCGo", "BCPEo", "BCTo")
that is the LMS class of distributions, Cole and Green (1992).
Note that this class is only appropriate when y is positive (with no zeros). If the response variable contains negative values and zeros then use the argument families=theSHASH
where theSHASH < c("NO", "SHASHo")
or add any other list of distributions which you may think is appropriate.
Justification of using the specific centile (0.38 2.27 9.1211220 25.25, 50, 74.75, 90.88, 97.72, 99.62) is given in Cole (1994).
It returns a gamlss
fitted object
The function is fitting several models and for large data can be slow
Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby and Vlasios Voudouris vlasios.voudouris@abmanalytics.com
Cole, T. J. (1994) Do growth chart centiles need a face lift? BMJ, 308–641.
Cole, T. J. and Green, P. J. (1992) Smoothing reference centile curves: the LMS method and penalized likelihood, Statist. Med. 11, 1305–1319
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507554.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
(see also https://www.gamlss.com/).
1 2 3 4 5 6 7 8 9 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.