MX.control {gamlss.mx} | R Documentation |
The control function for gamlssMX
Description
The function sets controls for the gamlssMX
function.
Usage
MX.control(cc = 1e-04, n.cyc = 200, trace = FALSE,
seed = NULL, plot = TRUE, sample = NULL, ...)
Arguments
cc |
convergent criterion for the EM |
n.cyc |
number of cycles for EM |
trace |
whether to print the EM iterations |
seed |
a number for setting the seeds for starting values |
plot |
whether to plot the sequence of global deviance up to convergence |
sample |
how large the sample to be in the starting values |
... |
for extra arguments |
Value
Returns a list
Author(s)
Mikis Stasinopoulos and Bob Rigby
References
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.
Stasinopoulos M.D., Kneib T, Klein N, Mayr A, Heller GZ. (2024) Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications. Cambridge University Press.
(see also https://www.gamlss.com/).
See Also
gamlss
, gamlssMX
, gamlssMXfits