Z-scores for lms objects
This creates z-scores for new values of y and x given a fitted lms object.
z.scores(object, y, x)
object |
a |
y |
new y values |
x |
new x values |
This is simply a job that can be also done by centiles.pred().
the required z-scores
Mikis Stasinopoulos
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 507-554.
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/).
## Not run: IND<-sample.int(7040, 1000, replace=FALSE) db1 <- db[IND,] plot(head~age, data=db1) m0 <- lms(head, age, data=db1,trans.x=TRUE ) z.scores(m0, x=c(2,15,30,40),y=c(45,50,56,63)) ## End(Not run)
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