gamlssMX {gamlss.mx} | R Documentation |
The function gamlssMX
is design for fitting a K fold non parametric mixture of gamlss family distributions.
gamlssMX(formula = formula(data), pi.formula = ~1, family = "NO", weights, K = 2, prob = NULL, data = sys.parent(), 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 = sys.parent(), 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 $μ$ parameter of the model. Note that
modelling the rest of the distributional parameters it can be done
by using the usual ... which passes the arguments to
gamlss() |
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 multinom() function from
package nnet |
family |
This should be a gamlss.family distribution (or a list of
distributions). Note that if different distributions are used here
their parameters should be comparable for ease of interpretation. |
weights |
prior weights if needed |
K |
the number of finite mixtures with default K=2 |
prob |
prior probabilities if required for starting values |
data |
the data frame nedded for the fit. Note that this is compulsory if pi.formula is used. |
control |
This argument sets the control parameters for the EM iterations algorithm.
The default setting are given in the MX.control function |
g.control |
This argument can be used to pass to gamlss() control parameters, as in
gamlss.control |
n |
the number of fits required in gamlssMXfits() |
zero.component |
whether zero component models exist, default is FALSE |
... |
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.
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2003) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.com/).
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