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This creates z-scores for new values of y and x given a fitted lms
object.
z.scores(object, y, x)
the required z-scores
a lms
fitted object
new y values
new x values
Mikis Stasinopoulos
This is simply a job that can be also done by centiles.pred()
.
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/).
centiles.pred
if (FALSE) {
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))}
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