glim.control | R Documentation |
Auxiliary function used for the inner iteration of gamlss
algorithm. Typically
only used when calling gamlss
function through the option i.control
.
glim.control(cc = 0.001, cyc = 50, glm.trace = FALSE,
bf.cyc = 30, bf.tol = 0.001, bf.trace = FALSE,
...)
cc |
the convergence criterion for the algorithm |
cyc |
the number of cycles of the algorithm |
glm.trace |
whether to print at each iteration (TRUE) or not (FALSE) |
bf.cyc |
the number of cycles of the backfitting algorithm |
bf.tol |
the convergence criterion (tolerance level) for the backfitting algorithm |
bf.trace |
whether to print at each iteration (TRUE) or not (FALSE, the default) |
... |
for extra arguments |
A list with the arguments as components
Mikis Stasinopoulos, Bob Rigby
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/).
gamlss
data(aids)
con<-glim.control(glm.trace=TRUE)
h<-gamlss(y~poly(x,3)+qrt, family=PO, data=aids, i.control=con) #
rm(h,con)
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