gamlss (version 5.2-0)

z.scores: Z-scores for lms objects

Description

This creates z-scores for new values of y and x given a fitted lms object.

Usage

z.scores(object, y, x)

Arguments

object

a lms fitted object

y

new y values

x

new x values

Value

the required z-scores

Details

This is simply a job that can be also done by centiles.pred().

References

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/).

See Also

centiles.pred

Examples

# 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))
# }

Run the code above in your browser using DataCamp Workspace