Description Usage Arguments Author(s) References See Also Examples

The function `gamlssMX`

is design for fitting a K fold non parametric mixture of gamlss family distributions.

1 2 3 4 5 6 7 8 9 10 | ```
gamlssMX(formula = formula(data), pi.formula = ~1,
family = "NO", weights, K = 2, prob = NULL,
data, control = MX.control(...),
g.control = gamlss.control(trace = FALSE, ...),
zero.component = FALSE, ...)
gamlssMXfits(n = 5, formula = formula(data), pi.formula = ~1,
family = "NO", weights, K = 2, prob = NULL,
data, control = MX.control(),
g.control = gamlss.control(trace = FALSE),
zero.component = FALSE, ... )
``` |

`formula` |
This argument it should be a formula (or a list of formulea of length
K) for modelling the |

`pi.formula` |
This should be a formula for modelling the prior probabilities as a
function of explanatory variables. Note that no smoothing of other
additive terms are allowed here only the usual linear terms. The
modelling here is done using the |

`family` |
This should be a |

`weights` |
prior weights if needed |

`K` |
the number of finite mixtures with default |

`prob` |
prior probabilities if required for starting values |

`data` |
the data frame nedded for the fit. Note that this is compulsory if |

`control` |
This argument sets the control parameters for the EM iterations algorithm.
The default setting are given in the |

`g.control` |
This argument can be used to pass to |

`n` |
the number of fits required in |

`zero.component` |
whether zero component models exist, default is |

`...` |
for extra arguments |

Mikis Stasinopoulos and 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/).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
library(MASS)
data(geyser)
# fitting 2 finite normal mixtures
m1<-gamlssMX(waiting~1,data=geyser,family=NO, K=2)
#fitting 2 finite gamma mixtures
m2<-gamlssMX(waiting~1,data=geyser,family=GA, K=2)
# fitting a model for pi
# first create a data frame
geyser1<-matrix(0,ncol=2, nrow=298)
geyser1[,1] <-geyser$waiting[-1]
geyser1[,2] <-geyser$duration[-299]
colnames(geyser1)<- c("waiting", "duration")
geyser1 <-data.frame(geyser1)
# get the best of 5 fits
m3<-gamlssMXfits(n=5, waiting~1, pi.formula=~duration, data=geyser1,family=NO, K=2)
m3
``` |

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