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 = 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 |
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.
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/).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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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