Nothing
R/gamlss-5.R
In gamlss: Generalized Additive Models for Location Scale and Shape
Defines functions
glim.control
gamlss.control
gamlss
gamlssNews
.onAttach
Documented in
gamlss
gamlss.control
gamlssNews
glim.control
## offsets are saved for the vcov() to work 1-5-13
## revisised Friday, March 19, 2010 to correct a bug reported by Dr Willem Vervoort
## major revision MS Wednesday, December 29, 2004 at 18:04
## to unify the output and tidy the input
.onAttach <- function(...)
{
packageStartupMessage(paste(paste(" ********** GAMLSS Version", packageDescription("gamlss")$Version), " ********** \n")
,appendLF = FALSE)
packageStartupMessage(paste("For more on GAMLSS look at", packageDescription("gamlss")$URL))
packageStartupMessage("Type gamlssNews() to see new features/changes/bug fixes.\n")
}
# packageStartupMessage(cat(paste(" ********** GAMLSS Version", packageDescription("gamlss")$Version), "********** \n")
# ;cat(paste("For more on GAMLSS look at", packageDescription("gamlss")$URL), "\n")
# ;cat("Type gamlssNews() to see new features/changes/bug fixes.\n")
# , appendLF = FALSE)
# cat(paste(" ********** GAMLSS Version", packageDescription("gamlss")$Version), "********** \n")
# cat(paste("For more on GAMLSS look at", packageDescription("gamlss")$URL), "\n")
# cat("Type gamlssNews() to see new features/changes/bug fixes.\n")
# }
gamlssNews <- function() file.show(system.file("doc", "NEWS.txt", package="gamlss"))# Monday, September 5, 2005 MS
#.onAttach <- function(libname, pkgname) {
# gamlssver <- if (as.numeric(R.version$major) < 2 &&
# as.numeric(R.version$minor) < 9.0)
# package.description("gamlss")["Version"] else
# packageDescription("gamlss")$Version
# cat(paste("gamlss", gamlssver), "\n")
# cat("Type gamlssNews() to see new features/changes/bug fixes.\n")
#}
##
##----------------------------------------------------------------------------------------
.gamlss.bi.list<-c("BI", "Binomial", "BB", "Beta Binomial", "ZIBI", "ZIBB",
"ZABI", "ZABB", "DBI", "BItr", "BBtr", "ZIBItr", "ZIBBtr",
"ZABItr", "ZABBtr", "DBItr") # binomial denominators
.gamlss.multin.list<-c("MULTIN", "MN3", "MN4", "MN5")
##----------------------------------------------------------------------------------------
.gamlss.sm.list<-c("cs", "scs", # smoothing cubic splines "s" vc"
"ps", "pb", "cy", "tp", "pvc", # penalised splines : ps, pb, cy tp pvc pbq
"pbm", "pbj", "pbo", "pbz", # monotone jumps and going to zero
"pbc", "pbts", "pbp", "pbf", # pb cycle pb predict
"pcat", "pba", # for categorical to reduce levels
"pbq", # pb using Qfunction
"gmrf", # Gaussian Markov random fields
"mrfa", "mrf", # Markov random fields
"so", "SOP", # seperation of Anisotropic penalties
"krig" , # kriging
"lo", # loess
"random","re", "re4", # random effect "ra","rc","rash"
"fp","pp", # fractional poly
"nl", # non-linear (.nl)
"ri", # ridge regression "ridge",
"boost", # boosting
"fk", # free knot smoothing
"own", "test", "test0","test1",# for testing new
"arma", # time series
"rw", "ar", "seas", # time series
"srw", "sar", # time series seasonal
"la", "plag", # time series penalised lags,
"tr", # decision trees
"ga", "ba", "mm", # gam - bam Simon Wood
"nn", # neural netwoks
"sv", # suppor vector machines
"ma", # mars
"pcr", # principal component Reg
"pc", # principal component regr
"h2o", # h2o project
"pa", # partial regression
"gnet", "lrs", # glmnet and lars
"rf1", # random forest ranger
"rf2", # generalised random forest
"gbo", # generalised Tree boosting
"gsel") # gamsel
# note that predict only read 2 characters Monday, May 4, 2009 at 11:27
##----------------------------------------------------------------------------------------
# .gamlss.sm.list<-c( "ar", "arma",
# "cs", "cy",
# "fk","fp",
# "ga",
# "krig",
# "la","lo",
# "mrf", "mrfa",
# "nl","nn",
# "ri", "rw",
# "pb", "pbj", "pbm", "pbo", "pbq","pbz",
# "pp","ps", "pvc",
# "random","re",# random effect
# "sap", "sap3", "seas",
# "test", "test0","test1","tr",
# "own")
##========================================================================================
## Generalised Additive Models for Location Scale and Shape
## R-functions created
## originaly created 6 Feb 2002.
## Last maijor revision Saturday, Thursday, January 9, 2003 at 09:48
##
## y the response variable
## X [1,2,3,4] design matrices
## w weights for weighed likelihood analysis (not as in GLM and GAM)
## *offset offsets in linear predictor for mu, sigma,nu, tau
## family response family see for available GAMLSS families
## *start starting values for mu, sigma, nu or tau (optional)
## *fix whether the specific parameter should be remained
## fixed in the fitting procedure
#========================================================================================
### The gamlss function contains the sub-functions
### RS() CG() and mixed()
###
#----------------------------------------------------------------------------------------
library(survival)
gamlss <- function(formula = formula(data),
sigma.formula = ~1,
nu.formula = ~1,
tau.formula = ~1,
family = NO(),
data,
weights = NULL, # for weighted likelihood analysis
# (not the same as in GLM's)
contrasts = NULL, # one type of contrasts for all parameters
method = RS(), # default algorithm
start.from = NULL, # starting from previous gamlss object
mu.start = NULL, # starting from given values
sigma.start = NULL,
nu.start = NULL,
tau.start = NULL,
mu.fix = FALSE,# whether the parameter is fixed
sigma.fix = FALSE,
nu.fix = FALSE,
tau.fix = FALSE,
control = gamlss.control(...),
i.control = glim.control(...), # the inner circle control (GLIM)
...)
{
## na.action = na.fail(), # both na.action and subset have been removed
## because while there is only one data set there are usually
## subset = NULL, four different model frames created therefore it is easier to apply
## sub-setting and na.action to the whole data set not to the
## frames
##---------------------------------------------------------------------------------------
## require(stats) Thursday, June 10, 2004 at 09:58 MS
#require(splines) # this will be removed with namespaces
#----------------------------------------------------------------------------------------
#gamlss.rc.list<-c("EX.rc","Exponential.rc") # the right censoring distribution list
#gamlss.bi.list<-c("BI", "Binomial", "BB", "Beta Binomial") # binomial denominators
#.gamlss.multin.list<-c("MULTIN", "MN3", "MN4", "MN5")
# ---------------------------------------------------------------------------------------
# this is to replicate rqres within gamlss enviroment DS Friday, March 31, 2006 at 10:30
rqres <- function (pfun = "pNO",
type = c("Continuous", "Discrete", "Mixed"),
censored = NULL,
ymin = NULL,
mass.p = NULL,
prob.mp = NULL,
y = y,
... )
{ }
body(rqres) <- eval(quote(body(rqres)), envir = getNamespace("gamlss"))
##---------------------------------------------------------------------------------------
##---------------------------------------------------------------------------------------
## first the definition of the three algorithms
##---------------------------------------------------------------------------------------
## the mixing algorithm
##---------------------------------------------------------------------------------------
##---------------------------------------------------------------------------------------
mixed <- function(n1=1,n2=20)
{
conv <- RS(n.cyc=n1, no.warn = FALSE)
conv <- CG(n.cyc=n2)
conv
}
##---------------------------------------------------------------------------------------
## the Rigby and Stasinopoulos algorithm
##---------------------------------------------------------------------------------------
RS <- function(n.cyc = control$n.cyc, no.warn = TRUE)
{
## this is to emulate the GLIM iterative algorithm
## created by Mikis Stasinopoulos Monday, February 18, 2002 at 09:23
## last change Wednesday, February 23, 2005 at 20:23
## where the step has changed to apply to the fitted values
glim.fit <- function(f,X,y,w,fv,os, step = 1,control = glim.control(),
auto, gd.tol)
{
cc <- control$cc # convergence criterion-tolerance
cyc <- control$cyc # max. no. of cycles
trace <- control$glm.trace # whether to print
bf.cyc <- control$bf.cyc
bf.tol <- control$bf.tol
bf.trace <- control$bf.trace
itn <- 0
lp <- eta <- f$linkfun(fv)
dr <- f$dr(eta) # dmu/deta
dr <- 1/dr # deta/dmu = 1 / (dmu/deta)
di <- f$G.di(fv) # deviance increment
dv <- sum(w*di) # the global deviance
olddv <- dv+1 # the old global deviance
dldp <- f$dldp(fv) # u score
d2ldp2 <- f$d2ldp2(fv) # second derivative of log-Likelihood
d2ldp2 <- ifelse(d2ldp2 < -1e-15, d2ldp2,-1e-15) # added 26-10-07
wt <- -(d2ldp2/(dr*dr))# -(d2l/dp2)/(1/(dmu/deta))^2=- (d2l/dp2)(dmu/eta)^2
# we need to stop the weights to go to Infty
wt <- ifelse(wt>1e+10,1e+10,wt) # Mikis 9-10-14
wt <- ifelse(wt<1e-10,1e-10,wt)
# wv <- (eta-os)+step*dldp/(dr*wt)
wv <- (eta-os)+dldp/(dr*wt) # eta
if (family$type=="Mixed") wv <-ifelse(is.nan(wv),0,wv) ## TEST
iterw <- FALSE
who <- f$who
smooth.frame <- f$smooth.frame
s <- f$smooth
## starting the recycling
while ( abs(olddv-dv) > cc && itn < cyc ) # MS Wednesday, June 26, 2002
{
itn <- itn+1 # the glim inner iteration number
lpold <- lp
sold <- s
if (any(is.na(wt))||any(is.na(wv)) ) stop("NA's in the working vector or weights for parameter ", names(formals(f$valid)), "\n")
if (any(!is.finite(wt))||any(!is.finite(wv)) ) stop("Inf values in the working vector or weights for parameter ", names(formals(f$valid)), "\n")
if(length(who) > 0)
{
fit <- additive.fit(x=X,y=wv,w=wt*w,s=s,who=who,smooth.frame, maxit = bf.cyc,
tol = bf.tol, trace = bf.trace)
# lp <- fit$fitted.values
lp <- if (itn==1) fit$fitted.values else step*fit$fitted.values+(1-step)*lpold
# s <- fit$smooth # test Wednesday, January 8, 2003 at 14:37
s <- if (itn==1) fit$smooth else step*fit$smooth+(1-step)*sold
}
else
{
fit <- lm.wfit(X,wv,wt*w,method="qr")
lp <- if (itn==1) fit$fitted.values else step*fit$fitted.values+(1-step)*lpold
# lp <- fit$fitted.values
}
## method 1
eta <- lp+os # fixed Wednesday, September 4, 2002 at 09:45 DS
## own link
fv <- f$linkinv(eta)
## own dist
di <- f$G.di(fv)
olddv <- dv
dv <- sum(w*di)
## new for automatic steps MS BR Friday, April 15, 2005 at 18:50
if (dv > olddv && itn >= 2 && auto==TRUE)
{
for(i in 1:5) # MS Thursday, September 22, 2005
{
lp <- (lp+lpold)/2
eta <- lp+os
fv <- f$linkinv(eta)
di <- f$G.di(fv)
dv <- sum(w*di)
# cat("try",i,"\n")
if(length(who) > 0) s <- (s+sold)/2
if ((olddv-dv) > cc) break # MS Thursday, September 22, 2005
}
}
if ((dv > olddv+gd.tol ) && itn >= 2 && iterw==FALSE)
{
warning("The deviance has increased in an inner iteration for ",
names(formals(f$valid)), "\n","Increase gd.tol and if persist, try different steps", "\n", "or model maybe inappropriate") #
iterw <-TRUE
}
if (is.na(!f$valid(fv)) ) # MS Saturday, April 6, 2002 at 18:06
stop( "fitted values in the inner iteration out of range")
dr <- f$dr(eta)
dr <- 1/dr
## method 2
dldp <- f$dldp(fv)
d2ldp2 <- f$d2ldp2(fv)
d2ldp2 <- ifelse(d2ldp2 < -1e-15, d2ldp2,-1e-15) # added 26-10-07
wt <- -(d2ldp2/(dr*dr))
wt <- ifelse(wt>1e+10,1e+10,wt) # Mikis 9-10-14
wt <- ifelse(wt<1e-10,1e-10,wt)
# wv <- (eta-os)+step*dldp/(dr*wt)
wv <- (eta-os)+dldp/(dr*wt)
if (family$type=="Mixed") wv <-ifelse(is.nan(wv),0,wv) ## TEST
# olddv <- dv
# dv <- sum(w*di)
if(trace)
cat("GLIM iteration ", itn, " for ", names(formals(f$valid)), ": Global Deviance = ",
format(round(dv, 4)), " \n", sep = "")
} #end of while
pen <- 0 # DS Thursday, November 21, 2002 at 23:04
if(length(who) > 0) {pen<- sum(eta*wt*(wv-eta)) }
c(fit, list(fv = fv, wv = wv, wt = wt, eta = eta, os = os, pen=pen)) #ms Saturday, December 4, 2004
}
##-end of GLIM.fit------------------------------------------------------------
##---start RS-----------------------------------------------------------------
## getting the contol papameters
c.crit <- control$c.crit
# n.cyc <- control$n.cyc
trace <- control$trace
autostep <- control$autostep
mu.step <- control$mu.step
sigma.step <- control$sigma.step
nu.step <- control$nu.step
tau.step <- control$tau.step
gd.tol <- control$gd.tol
iter <- control$iter
conv <- FALSE
## initial Gloval deviance
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
G.dev.old <- G.dev+1
##----------------------------------------------------------------------------
## the outer iteration starts here
while ( abs(G.dev.old-G.dev) > c.crit && iter < n.cyc )
{
# the mean submodel
if ("mu"%in%names(family$parameters))
{
if (family$parameter$mu==TRUE & mu.fix==FALSE)
{
#mu.old <- mu
mu.fit <<- glim.fit(f = mu.object, X = mu.X, y = y, w = w,
fv = mu, os = mu.offset, step = mu.step,
control = i.control, gd.tol = gd.tol,
auto = autostep)
mu <<- mu.fit$fv
mu.object$smooth <- mu.fit$smooth
}
}
# the scale-dispersion submodel
if ("sigma"%in%names(family$parameters))
{
if ( family$parameter$sigma==TRUE & sigma.fix==FALSE)
{
#sigma.old <- sigma
sigma.fit <<- glim.fit(f = sigma.object, X = sigma.X, y = y,
w = w, fv = sigma, os = sigma.offset,
step = sigma.step, control = i.control,
gd.tol = gd.tol, auto = autostep)
sigma <<- sigma.fit$fv
sigma.object$smooth <- sigma.fit$smooth
}
}
# the nu submodel
if ("nu"%in%names(family$parameters))
{
if ( family$parameter$nu==TRUE & nu.fix==FALSE)
{
# nu.old <- nu
nu.fit <<- glim.fit(f = nu.object, X = nu.X, y = y,
w = w, fv = nu, os = nu.offset,
step = nu.step, control = i.control,
gd.tol = gd.tol, auto = autostep)
nu <<- nu.fit$fv
nu.object$smooth <- nu.fit$smooth
}
}
# the tau submodel
if ("tau"%in%names(family$parameters))
{
if (family$parameter$tau ==TRUE & tau.fix==FALSE)
{
# tau.old <- tau
tau.fit <<- glim.fit(f = tau.object, X = tau.X, y = y,
w = w, fv = tau, os = tau.offset,
step = tau.step, control = i.control,
gd.tol = gd.tol, auto = autostep)
tau <<- tau.fit$fv
tau.object$smooth <- tau.fit$smooth
}
}
# the overall Global Deviance
G.dev.old <- G.dev
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
iter <- iter+1
fiter <<- iter
if(trace)
cat("GAMLSS-RS iteration ", iter, ": Global Deviance = ",
format(round(G.dev, 4)), " \n", sep = "")
if (G.dev > (G.dev.old+gd.tol) && iter >1 )
stop(paste("The global deviance is increasing", "\n",
"Try different steps for the parameters or the model maybe inappropriate"))
}
if ( abs(G.dev.old-G.dev) < c.crit ) # MS Wednesday, June 11, 2003 at 11:58
#taken out (abs((G.dev-G.dev.old)/(0.1+abs(G.dev.old)))<c.crit&&iter<=n.cyc)
conv <- TRUE else FALSE
if (!conv && no.warn ) warning("Algorithm RS has not yet converged")
conv
}
##=======================================================================================
## End of GAMLSS RS fitting algorithm
##=======================================================================================
##---------------------------------------------------------------------------------------
##---------------------------------------------------------------------------------------
#========================================================================================
## the gamlss CG (Cole and Green) fitting algorithm
##=======================================================================================
#----------------------------------------------------------------------------------------
CG<-function(n.cyc=control$n.cyc)
{
#========================================================================================
# getting the contol papameters
#========================================================================================
#----------------------------------------------------------------------------------------
c.crit <- control$c.crit
#n.cyc <- control$n.cyc
trace <- control$trace
mu.step <- control$mu.step
sigma.step <- control$sigma.step
nu.step <- control$nu.step
tau.step <- control$tau.step
gd.tol <- control$gd.tol
autostep <- control$autostep
iter <- control$iter
conv <- FALSE
i.c.crit <- i.control$cc
i.n.cyc <- i.control$cyc
i.trace <- i.control$glm.trace
# bf.cyc <- i.control$bf.cyc
bf.tol <- i.control$bf.tol
bf.trace <- i.control$bf.trace
first.iter <- TRUE #ms Thursday, November 6, 2003 at 09:04
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
G.dev.old <- G.dev+1
# initial values for the variates used in the inner iteration
w.mu.sigma<-w.mu.nu<-w.mu.tau<-w.sigma.nu<-w.sigma.tau<-w.nu.tau<-rep(0,N)
eta.mu<-eta.old.mu<-eta.sigma<-eta.old.sigma<-eta.nu<-eta.old.nu<-rep(0,N)
eta.old.tau<-eta.tau<-rep(0,N) #???
# here starts the outer iteration
#=======================================================================================
while ( abs(G.dev.old-G.dev) > c.crit && iter < n.cyc )
{
i.iter <- 0
## for mu
if ("mu"%in%names(family$parameters))
{
eta.mu <- eta.old.mu <- family$mu.linkfun(mu)
u.mu <- mu.object$dldp(mu=mu)
u2.mu <- mu.object$d2ldp2(mu=mu)
dr.mu <- family$mu.dr(eta.mu)
dr.mu <- 1/dr.mu
who.mu <- mu.object$who
smooth.frame.mu <- mu.object$smooth.frame
s.mu <- if(first.iter) mu.object$smooth else s.mu
w.mu <- -u2.mu/(dr.mu*dr.mu)
z.mu <- (eta.old.mu-mu.offset)+mu.step*u.mu/(dr.mu*w.mu)
}
## for sigma
if ("sigma"%in%names(family$parameters))
{
eta.sigma <- eta.old.sigma <- family$sigma.linkfun(sigma)
u.sigma <- sigma.object$dldp(sigma=sigma)
u2.sigma <- sigma.object$d2ldp2(sigma=sigma)
u2.mu.sigma <- family$d2ldmdd(sigma=sigma)
dr.sigma <- family$sigma.dr(eta.sigma)
dr.sigma <- 1/dr.sigma
who.sigma <- sigma.object$who
smooth.frame.sigma <- sigma.object$smooth.frame
s.sigma <- if(first.iter) sigma.object$smooth else s.sigma
w.sigma <- -u2.sigma/(dr.sigma*dr.sigma)
w.mu.sigma <- -u2.mu.sigma/(dr.mu*dr.sigma)
z.sigma <- (eta.old.sigma-sigma.offset)+sigma.step*u.sigma/
(dr.sigma*w.sigma)
}
## for nu
if ("nu"%in%names(family$parameters))
{
eta.nu <- eta.old.nu <- family$nu.linkfun(nu)
u.nu <- nu.object$dldp(nu=nu)
u2.nu <- nu.object$d2ldp2(nu=nu)
u2.mu.nu <- family$d2ldmdv(nu=nu)
u2.sigma.nu <- family$d2ldddv(nu=nu)
dr.nu <- family$nu.dr(eta.nu)
dr.nu <- 1/dr.nu
who.nu <- nu.object$who
smooth.frame.nu <- nu.object$smooth.frame
s.nu <- if(first.iter) nu.object$smooth else s.nu
w.nu <- -u2.nu/(dr.nu*dr.nu)
w.mu.nu <- -u2.mu.nu/(dr.mu*dr.nu)
w.sigma.nu <- -u2.sigma.nu/(dr.sigma*dr.nu)
z.nu <- (eta.old.nu-nu.offset)+nu.step*u.nu/
(dr.nu*w.nu)
}
## for tau
if ("tau"%in%names(family$parameters))
{
eta.tau <- eta.old.tau <- family$tau.linkfun(tau)
u.tau <- tau.object$dldp(tau=tau)
u2.tau <- tau.object$d2ldp2(tau=tau)
u2.mu.tau <- family$d2ldmdt(tau=tau)
u2.sigma.tau <- family$d2ldddt(tau=tau)
u2.nu.tau <- family$d2ldvdt(tau=tau)
dr.tau <- family$tau.dr(eta.tau)
dr.tau <- 1/dr.tau
who.tau <- tau.object$who
smooth.frame.tau <- tau.object$smooth.frame
s.tau <- if(first.iter) tau.object$smooth else s.tau
w.tau <- -u2.tau/(dr.tau*dr.tau)
w.mu.tau <- -u2.mu.tau/(dr.mu*dr.tau)
w.sigma.tau <- -u2.sigma.tau/(dr.sigma*dr.tau)
w.nu.tau <- -u2.nu.tau/(dr.nu*dr.tau)
z.tau <- (eta.old.tau-tau.offset)+tau.step*u.tau/
(dr.tau*w.tau)
}
## initial G. devinace for the inner iteration
G.dev.in <- G.dev+1
i.G.dev <- G.dev
first.iter <- FALSE
## the inner iteration starts here
##--------------------------------------------------------------------------------------
while ( abs(G.dev.in -i.G.dev) > i.c.crit && i.iter < i.n.cyc )
{
if ("mu"%in%names(family$parameters))
{
if (family$parameter$mu==TRUE & mu.fix==FALSE)
{
adj.mu <- -(w.mu.sigma*(eta.sigma-eta.old.sigma)+
w.mu.nu*(eta.nu-eta.old.nu)+w.mu.tau*(eta.tau-eta.old.tau))/w.mu
wv.mu <- z.mu+adj.mu
if(length(who.mu) > 0)
{
mu.fit <<- additive.fit(x = mu.X, y = wv.mu, w = w.mu*w,s = s.mu,
who = who.mu, smooth.frame = smooth.frame.mu, maxit = 1,
tol = bf.tol, trace = bf.trace)
mu.fit$eta <<- eta.mu <- mu.fit$fitted.values+mu.offset
mu.fit$fv <<- mu <<- mu.object$linkinv(eta.mu)
s.mu.old <- s.mu
s.mu <- mu.fit$smooth
mu.fit$pen <<- sum(eta.mu*w.mu*(wv.mu-eta.mu))
mu.fit$wv <<- wv.mu
mu.fit$wt <<- w.mu
mu.fit$os <<- mu.offset
}
else
{
mu.fit <<- lm.wfit(x=mu.X,y=wv.mu,w=w.mu*w,method="qr")
mu.fit$eta <<- eta.mu <- mu.fit$fitted.values+mu.offset
mu.fit$fv <<- mu <<- mu.object$linkinv(eta.mu)
mu.fit$wv <<- wv.mu
mu.fit$wt <<- w.mu
mu.fit$os <<- mu.offset
}
}
}
##fit sigma
if ("sigma"%in%names(family$parameters))
{
if (family$parameter$sigma==TRUE & sigma.fix==FALSE)
{
adj.sigma <- -(w.mu.sigma*(eta.mu-eta.old.mu)+
w.sigma.nu*(eta.nu-eta.old.nu)+w.sigma.tau*(eta.tau-eta.old.tau))/w.sigma
wv.sigma <- z.sigma+adj.sigma
if(length(who.sigma) > 0)
{
sigma.fit <<- additive.fit(x = sigma.X, y = wv.sigma,
w = w.sigma*w,s = s.sigma,
who = who.sigma,
smooth.frame = smooth.frame.sigma,
maxit = 1,
tol = bf.tol, trace = bf.trace)
sigma.fit$eta <<- eta.sigma <- sigma.fit$fitted.values+sigma.offset
sigma.fit$fv <<- sigma <<- sigma.object$linkinv(eta.sigma)
s.sigma.old <- s.sigma
s.sigma <- sigma.fit$smooth
sigma.fit$pen <<- sum(eta.sigma*w.sigma*(wv.sigma-eta.sigma))
sigma.fit$wv <<- wv.sigma
sigma.fit$wt <<- w.sigma
sigma.fit$os <<- sigma.offset
}
else
{
sigma.fit <<- lm.wfit(x=sigma.X,y=wv.sigma,w=w.sigma*w,method="qr")
sigma.fit$eta <<- eta.sigma <- sigma.fit$fitted.values+sigma.offset
sigma.fit$fv <<- sigma <<- sigma.object$linkinv(eta.sigma)
sigma.fit$wv <<- wv.sigma
sigma.fit$wt <<- w.sigma
sigma.fit$os <<- sigma.offset
}
}
}
##fit nu
if ("nu"%in%names(family$parameters))
{
if (family$parameter$nu==TRUE & nu.fix==FALSE)
{
adj.nu <- -(w.mu.nu*(eta.mu-eta.old.mu)+
w.sigma.nu*(eta.sigma-eta.old.sigma)+w.nu.tau*(eta.tau-eta.old.tau))/w.nu
wv.nu <- z.nu+adj.nu
if(length(who.nu) > 0)
{
nu.fit <<- additive.fit(x = nu.X,y = wv.nu, w = w.nu*w,s = s.nu,
who = who.nu, smooth.frame = smooth.frame.nu,
maxit = 1,
tol = bf.tol, trace = bf.trace)
nu.fit$eta <<- eta.nu <- nu.fit$fitted.values+nu.offset
nu.fit$fv <<- nu <<- nu.object$linkinv(eta.nu)
s.nu.old <- s.nu
s.nu <- nu.fit$smooth
nu.fit$pen <<- sum(eta.nu*w.nu*(wv.nu-eta.nu))
nu.fit$wv <<- wv.nu
nu.fit$wt <<- w.nu
nu.fit$os <<- nu.offset
}
else
{
nu.fit <<- lm.wfit(x=nu.X,y=wv.nu,w=w.nu*w,method="qr")
nu.fit$eta <<- eta.nu <- nu.fit$fitted.values+nu.offset
nu.fit$fv <<- nu <<- nu.object$linkinv(eta.nu)
nu.fit$wv <<- wv.nu
nu.fit$wt <<- w.nu
nu.fit$os <<- nu.offset
}
}
}
##fit tau
if ("tau"%in%names(family$parameters))
{
if (family$parameter$tau==TRUE & tau.fix==FALSE)
{
adj.tau <- -(w.mu.tau*(eta.mu-eta.old.mu)+
w.sigma.tau*(eta.sigma-eta.old.sigma)+w.nu.tau*(eta.nu-eta.old.nu))/w.tau
wv.tau <- z.tau+adj.tau
if(length(who.tau) > 0)
{
tau.fit <<- additive.fit(x = tau.X,y = wv.tau, w = w.tau*w,s = s.tau,
who = who.tau, smooth.frame = smooth.frame.tau,
maxit = 1,
tol = bf.tol, trace = bf.trace)
tau.fit$eta <<- eta.tau <- tau.fit$fitted.values+tau.offset
tau.fit$fv <<- tau <<- tau.object$linkinv(eta.tau)
s.tau.old <- s.tau
s.tau <- tau.fit$smooth
tau.fit$pen <<- sum(eta.tau*w.tau*(wv.tau-eta.tau))
tau.fit$wv <<- wv.tau
tau.fit$wt <<- w.tau
tau.fit$os <<- tau.offset
}
else
{
tau.fit <<- lm.wfit(x=tau.X,y=wv.tau,w=w.tau*w,method="qr")
tau.fit$eta <<- eta.tau <- tau.fit$fitted.values+tau.offset
tau.fit$fv <<- tau <<- tau.object$linkinv(eta.tau)
tau.fit$wv <<- wv.tau
tau.fit$wt <<- w.tau
tau.fit$os <<- tau.offset
}
}
}
G.dev.in <- i.G.dev
G.dev.incr <- eval(G.dev.expr)
i.G.dev <- sum(w*G.dev.incr)
i.iter <- i.iter+1
if(i.trace)
cat("CG inner iteration ", iter, ": Global Deviance = ",
format(round(i.G.dev, 4)), " \n", sep = "")
if (i.G.dev > (G.dev.in+gd.tol) && iter >1 )
stop(paste("The global deviance is increasing in the inner CG loop", "\n",
"Try different steps for the parameters or the model maybe inappropriate"))
}
## the inner iteration finish here
##--------------------------------------------------------------------------------------
G.dev.old <- G.dev
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
# autostep<-FALSE
## new for automatic steps MS BR Friday, April 15, 2005 at 18:50
if (G.dev > G.dev.old && iter >= 2 && autostep == TRUE) # && itn >= 2 )
{
for(i in 1:5)
{
if ("mu"%in%names(family$parameters))
{
eta.mu <- (eta.mu+eta.old.mu)/2
mu <<- mu.object$linkinv(eta.mu)
if(length(who.mu) > 0) s.mu <- (s.mu+s.mu.old)/2
}
if ("sigma"%in%names(family$parameters))
{
eta.sigma <- (eta.sigma+eta.old.sigma)/2
sigma <<- sigma.object$linkinv(eta.sigma)
if(length(who.sigma) > 0) s.sigma <- (s.sigma+s.sigma.old)/2
}
if ("nu"%in%names(family$parameters))
{
eta.nu <- (eta.nu+eta.old.nu)/2
nu <<- nu.object$linkinv(eta.nu)
if(length(who.nu) > 0) s.nu <- (s.nu+s.nu.old)/2
}
if ("tau"%in%names(family$parameters))
{
eta.tau <- (eta.tau+eta.old.tau)/2
tau <<- tau.object$linkinv(eta.tau)
if(length(who.tau) > 0) s.tau <- (s.tau+s.tau.old)/2
}
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
# cat("helow there \n")
# if(length(who) > 0) s <- (s+sold)/2
if (G.dev < G.dev.old) break
}
}
iter <- iter+1
fiter <<-iter
if(trace)
cat("GAMLSS-CG iteration ", iter, ": Global Deviance = ",
format(round(G.dev, 4)), " \n", sep = "")
if (G.dev > (G.dev.old+gd.tol) && iter >1 )
stop(paste("The global deviance is increasing in CG-algorithm ", "\n",
"Try different steps for the parameters or the model maybe inappropriate"))
}
if ( abs(G.dev.old-G.dev) < c.crit ) conv <- TRUE else FALSE # MS June 11, 2003
if (!conv) warning("Algorithm CG has not yet converged")
conv
##the outer iteration finish here
##=======================================================================================
}
##---------------------------------------------------------------------------------------
## the end of CG
##---------------------------------------------------------------------------------------
##=======================================================================================
##---------------------------------------------------------------------------------------
## this function is used in for outputing the parameters
##---------------------------------------------------------------------------------------
parameterOut <- function(what="mu", save)
{
out <- list()
if(save==TRUE)
{
if(family$parameter[[what]]==TRUE && eval(parse(text=paste(what,".fix",sep="")))==FALSE)
{
out$fv <- eval(parse(text=what))
out$lp <- eval(parse(text=(paste(what,".fit$eta", sep=""))))
out$wv <- eval(parse(text=(paste(what,".fit$wv", sep=""))))
out$wt <- eval(parse(text=(paste(what,".fit$wt", sep=""))))
out$link <- eval(parse(text=(paste(what,".object$link", sep=""))))
out$terms <- eval(parse(text=(paste(what,".terms", sep=""))))
out$x <- eval(parse(text=(paste(what,".X", sep=""))))
out$qr <- eval(parse(text=(paste(what,".fit$qr", sep=""))))
out$coefficients <- eval(parse(text=(paste(what,".fit$coefficients", sep=""))))
out$offset <- eval(parse(text=(paste(what,".fit$os", sep=""))))
out$xlevels <- .getXlevels( eval(parse(text=paste(what,".terms",sep="")))
, eval(parse(text=paste(what,".frame",sep=""))))
# ms Sunday, June 13 2004
out$formula <- eval(parse(text=paste(what,".formula",sep="")))
if(length(eval(parse(text=paste(what,".smoothers",sep="")))) > 0)
{ out$df <- eval(parse(text=paste(what,".fit$nl.df",sep="")))+
eval(parse(text=paste(what,".fit$rank",sep="")))
out$nl.df <- eval(parse(text=paste(what,".fit$nl.df",sep="")))
out$s <- eval(parse(text=paste(what,".fit$smooth",sep="")))
out$var <- eval(parse(text=paste(what,".fit$var",sep="")))
out$coefSmo <- eval(parse(text=paste(what,".fit$coefSmo",sep="")))
out$lambda <- eval(parse(text=paste(what,".fit$lambda",sep="")))
out$pen <- eval(parse(text=paste(what,".fit$pen",sep="")))
}
else
{ out$df <- eval(parse(text=paste(what,".fit$rank",sep="")))
out$nl.df <- 0
out$pen <- 0 #ms May 13, 2004
}
}
else
{ out$fix <- eval(parse(text=paste(what,".fix",sep="")))
out$df <- 0
out$fv <- eval(parse(text=what))
}
} # if(save== not TRUE)
else
{
if(family$parameter[[what]]==TRUE && eval(parse(text=paste(what,".fix",sep="")))==FALSE)
{
if(length(eval(parse(text=paste(what,".smoothers",sep="")))) > 0)
{ out$df <- eval(parse(text=paste(what,".fit$nl.df",sep="")))+
eval(parse(text=paste(what,".fit$rank",sep="")))
out$nl.df <- eval(parse(text=paste(what,".fit$nl.df",sep="")))
out$terms <- eval(parse(text=(paste(what,".terms", sep=""))))
out$formula <- eval(parse(text=paste(what,".formula",sep="")))
}
else
{
out$df <- eval(parse(text=paste(what,".fit$rank",sep="")))
out$nl.df <- 0
out$terms <- eval(parse(text=(paste(what,".terms", sep=""))))
out$formula <- eval(parse(text=paste(what,".formula",sep="")))
}
}
else
{
out$df <- 0
}
}
out
}
##=========================================================================================
##-----------------------------------------------------------------------------------------
## this function is used to extract the formula for the parameters other than mu
##-----------------------------------------------------------------------------------------
##=========================================================================================
other.formula <- function(form)
{
dform <- formula(form)
if (length(dform)==2)
{
dform[3] <- dform[2] # taking 1 in position [3]
dform[2] <- if (is(formula, "terms") )formula[[2]] else formula[2] # ms 31-12-08 # put y in position 2
}
dform
}
##=========================================================================================
##-----------------------------------------------------------------------------------------
## this function is getting the smoothers at each parameter
##-----------------------------------------------------------------------------------------
##=========================================================================================
get.smoothers <- function(term)
{
a <- attributes(term) #
smoothers <- a$specials #S convert variable pointers to term pointers
if(length(smoothers) > 0)
{
smoothers <- smoothers[sapply(smoothers, length) > 0]
# smoothersR <-smoothers
for(i in seq(along = smoothers))
{
tt <- smoothers[[i]]
ff <- apply(a$factors[tt, , drop = FALSE], 2, any)
smoothers[[i]] <- if(any(ff)) seq(along = ff)[a$order == 1 & ff]
else NULL
}
}
smoothers
}
##=========================================================================================
##-----------------------------------------------------------------------------------------
## this function creates the parameter objects
##-----------------------------------------------------------------------------------------
##=========================================================================================
get.object <- function(what)
{
link <- eval(parse(text=(paste("family$",what,".link", sep=""))))
linkfun <- eval(parse(text=(paste("family$",what,".linkfun", sep=""))))
linkinv <- eval(parse(text=(paste("family$",what,".linkinv", sep=""))))
dr <- eval(parse(text=(paste("family$",what,".dr", sep=""))))
dldp <- switch( what,
"mu" = family$dldm,
"sigma" = family$dldd,
"nu" = family$dldv,
"tau" = family$dldt
)
d2ldp2 <- switch( what,
"mu" = family$d2ldm2,
"sigma" = family$d2ldd2,
"nu" = family$d2ldv2,
"tau" = family$d2ldt2
)
G.di <- family$G.dev.incr
valid <- eval(parse(text=(paste("family$",what,".valid", sep=""))))
object <- list(link = link, linkfun = linkfun, linkinv = linkinv, dr = dr,
dldp = dldp, d2ldp2 = d2ldp2, G.di = G.di, valid = valid)
if(length(eval(parse(text=(paste(what,".smoothers", sep=""))))) > 0)
{ #only if smoothing
parAttrTermlevels <- eval(parse(text=(paste(what,".a$term.labels", sep=""))))
boo <- unlist(eval(parse(text=(paste(what,".smoothers", sep="")))))
who <- parAttrTermlevels[boo[ order(boo)]]
smooth.frame <- eval(parse(text=(paste(what,".frame", sep=""))))
s <- matrix(0, N, length(who))
dimnames(s) <- list(names(y), who)
object$smooth <- s
object$who <- who
object$smooth.frame <- smooth.frame
}
object
}
#==========================================================================================
##-----------------------------------------------------------------------------------------
##-----------------------------------------------------------------------------------------
##=========================================================================================
## here is where the proper gamlss function starts
##=========================================================================================
##-----------------------------------------------------------------------------------------
## Save call for future reference
gamlsscall <- match.call() # the function call
## checking for NA in the data
if(!missing(data))
{
if (any(is.na(data)))
stop("The data contains NA's, use data = na.omit(mydata)")
}
## Evaluate the model frame
mnames <- c("", "formula", "data", "weights" ) # "subset" "na.action"
cnames <- names(gamlsscall) # get the names of the arguments of the call
cnames <- cnames[match(mnames,cnames,0)] # keep only the ones that match with mnames
mcall <- gamlsscall[cnames] # get in mcall all the relevant information but remember
# that the first elenent will be NULL
mcall[[1]] <- as.name("model.frame") # replace NULL with model.frame
## Specials for smoothing
mcall$formula <- if(missing(data)) terms(formula, specials = .gamlss.sm.list)
else terms(formula, specials = .gamlss.sm.list, data = data)
mu.frame <- eval(mcall, sys.parent()) # evalute the data.frame at the model.frame
##-----------------------------------------------------------------------------------------
## This part deals with the family
family <- as.gamlss.family(family) # bring first the gamlss family
G.dev.expr <- body(family$G.dev.inc) #MS Thursday, April 11, 2002 at 10:34
# nopar <- family$nopar # the number of parameters for the family
##-----------------------------------------------------------------------------------------
## Now extract the model components using model.extra and model.matrix
## This part deals with the response variable
Y <- model.extract(mu.frame, "response") #extracting the y variable from the formula
if(is.null(dim(Y))) # if y not matrix
N <- length(Y) else N <- dim(Y)[1] # calculate the dimension for y
# .gamlss.bi.list <- if (exists("gamlss.bi.list",envir=.GlobalEnv))
# get("gamlss.bi.list", envir=.GlobalEnv) else .gamlss.bi.list
## extracting now the y and the binomial denominator in case we use BI or BB
if(any(family$family%in%.gamlss.bi.list))
{
if (NCOL(Y) == 1)
{
y <- if (is.factor(Y)) Y != levels(Y)[1] else Y
bd <- rep(1, N)
if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1")
}
else if (NCOL(Y) == 2)
{
if (any(abs(Y - round(Y)) > 0.001)) {
warning("non-integer counts in a binomial GAMLSS!")
}
bd <- Y[,1] + Y[,2]
y <- Y[,1]
if (any(y < 0 | y > bd)) stop("y values must be 0 <= y <= N") # MS Monday, October 17, 2005
}
else stop(paste("For the binomial family, Y must be",
"a vector of 0 and 1's or a 2 column", "matrix where col 1 is no. successes",
"and col 2 is no. failures"))
}
# multinomial checking
else if(any(family$family%in%.gamlss.multin.list))
{
y <- if(is.factor(Y)) unclass(Y)
else Y
}
## For censoring
else if(is.Surv(Y))
{
## checking that the family is censored
if (length(grep("censored",family$family[[2]]))==0)
stop(paste("the family in not a censored distribution, use cens()"))
## checking compatability of Surv object and censored distribution
if (length(grep(attr(Y,"type"),family$family[[2]]))==0)
stop(paste("the Surv object and the censored distribution are not of the same type"))
y <- Y
#if (NCOL(Y) == 2)
# {
# #.event <- Y[,2]
# # y <- Y[,1]
# y <- Y
# }
#else if (NCOL(Y) == 2)
#stop("interval censored data are not implemented in gamlss yet")
}
else {y <- Y }
##-----------------------------------------------------------------------------------------
##checking the permissible y values
if (!family$y.valid(y)) # MS Thursday, June 20, 2002 at 16:30
stop( "response variable out of range")
##-----------------------------------------------------------------------------------------
## this part is used if start.from is used as argument
##------------start.from fitted model--------
if(!is.null(start.from))
{
if (!is.gamlss(start.from))
stop(paste("The object in start.from is not a gamlss object", "\n", ""))
mu.start <- NULL
sigma.start <- NULL
nu.start <- NULL
tau.start <- NULL
## location model
if ("mu"%in%start.from$parameters)
{
mu.start <- start.from$mu.fv
}
## scale-dispersion submodel
if ("sigma"%in%start.from$parameters)
{
sigma.start <- start.from$sigma.fv
}
## nu submodel
if ("nu"%in%start.from$parameters)
{
nu.start <- start.from$nu.fv
}
## tau submodel
if ("tau"%in%start.from$parameters)
{
tau.start <- start.from$tau.fv
}
}
##-----------------------------------------------------------------------------------------
## checking the parameter.fix
if (!is.logical(mu.fix)) stop("mu.fix should be logical TRUE or FALSE")
if (!is.logical(sigma.fix)) stop("sigma.fix should be logical TRUE or FALSE")
if (!is.logical(nu.fix)) stop("nu.fix should be logical TRUE or FALSE")
if (!is.logical(tau.fix)) stop("tau.fix should be logical TRUE or FALSE")
##-----------------------------------------------------------------------------------------
## extract the weights
w <- model.extract(mu.frame, weights) # weights for the likelihood
if(is.null(w)) w <- rep(1, N)
else if(any(w < 0)) stop("negative weights not allowed") #
# else if (!all(trunc(w)==w)) warning("weights should be integer values \n",
# " indicating number of observations with identical values \n") #
##=========================================================================================
##-----------------------------------------------------------------------------------------
## Set up location-mean submodel:
## mu.X design matrix
## mu.offset offset in linear predictor
## mu.start starting values for mu (optional)
##-----------------------------------------------------------------------------------------
mu.fit <- list() # MS Thursday, January 23, 2003 at 14:46
mu.formula <- formula # ms Wednesday, December 29, 2004
mu.terms <- attr(mu.frame, "terms") # it peeks up the terms attribute
mu.smoothers <- get.smoothers(mu.terms)
mu.a <- attributes(mu.terms) # from the model.frame
mu.X <- model.matrix(mu.terms, mu.frame, contrasts) # the mean model matrix
mu.offset <- model.extract(mu.frame, offset) # the mean-location offset
if(is.null(mu.offset)) mu.offset <- rep(0,N)
mu.object <- get.object("mu")
formals(mu.object$dldp, envir=new.env()) <- alist(mu = fv) # this is to get the right GLIM arguments
formals(mu.object$d2ldp2, envir=new.env()) <- alist(mu = fv) #
formals(mu.object$G.di, envir=new.env()) <- alist(mu = fv) #
formals(mu.object$valid, envir=new.env()) <- alist(mu = fv) #
## initial values for mu
if(!is.null(mu.start))
{ mu <- if(length(mu.start)>1) mu.start else rep(mu.start,N)}
else (eval(family$mu.initial)) #MS: Friday, March 29, 2002 at 11:27
##---------------------------------------------------------------------------------------
## Set up dispersion-scale submodel:
## sigma.X design matrix
## sigma.offset offset in linear predictor
## sigma.start starting values for sigma (optional)
##---------------------------------------------------------------------------------------
if ("sigma"%in%names(family$parameters))
{
orig.Envir <- attr(mcall$formula, ".Environment") # DS fix for Willem Thursday, March 18, 2010
sigma.fit <- list() # MS Thursday, January 23, 2003 at 14:48
form.sigma <- other.formula(form = sigma.formula)
sigma.terms <- if(missing(data)) terms(form.sigma, specials = .gamlss.sm.list)
else terms(form.sigma, specials = .gamlss.sm.list, data = data)
mcall$formula <- sigma.terms
attr(mcall$formula, ".Environment") <- orig.Envir # DS fix for Willem Thursday, March 18, 2010
sigma.frame <- eval(mcall,sys.parent())
sigma.terms <- attr(sigma.frame, "terms")
sigma.smoothers <- get.smoothers(sigma.terms)
sigma.a <- attributes(sigma.terms)
sigma.X <- model.matrix(sigma.terms, sigma.frame, contrasts)
sigma.offset <- model.extract(sigma.frame, offset)
if(is.null(sigma.offset)) sigma.offset <- rep(0,N)
sigma.object <- get.object("sigma")
formals(sigma.object$dldp, envir=new.env()) <- alist(sigma = fv) #
formals(sigma.object$d2ldp2, envir=new.env()) <- alist(sigma = fv) #
formals(sigma.object$G.di, envir=new.env()) <- alist(sigma = fv) #
formals(sigma.object$valid, envir=new.env()) <- alist(sigma = fv) #
formals(family$d2ldmdd, envir=new.env()) <- alist(sigma=sigma)# ?? I do not think is needed
## initial values for sigma
if(!is.null(sigma.start))
sigma <- if(length(sigma.start)>1) sigma.start else rep(sigma.start,N)
else eval(family$sigma.initial) #
}
##-----------------------------------------------------------------------------------------
## Set up for the 3rd parameter submodel:
## nu.X design matrix
## nu.offset offset in linear predictor
## nu.start starting values for nu (optional)
##-----------------------------------------------------------------------------------------
if ("nu"%in%names(family$parameters))
{
nu.fit <- list() # MS Thursday, January 23, 2003 at 14:48
form.nu <- other.formula(form = nu.formula)
nu.terms <- if(missing(data)) terms(form.nu, specials = .gamlss.sm.list)
else terms(form.nu, specials = .gamlss.sm.list, data = data)
mcall$formula <- nu.terms
attr(mcall$formula, ".Environment") <- orig.Envir # DS fix for Willem Thursday, March 18, 2010
nu.frame <- eval(mcall,sys.parent()) # ms Saturday, April 6, 2002 at 10:23
nu.terms <- attr(nu.frame, "terms")
nu.a <- attributes(nu.terms)
nu.smoothers <- get.smoothers(nu.terms)
nu.X <- model.matrix(form.nu, nu.frame, contrasts)
nu.offset <- model.extract(nu.frame, offset)
if(is.null(nu.offset)) nu.offset <- rep(0,N)
nu.object <- get.object("nu")
formals(nu.object$dldp, envir=new.env()) <- alist(nu = fv) # this is to get the right GLIM argument
formals(nu.object$d2ldp2, envir=new.env()) <- alist(nu = fv) #
formals(nu.object$G.di, envir=new.env()) <- alist(nu = fv) #
formals(nu.object$valid, envir=new.env()) <- alist(nu = fv) #
formals(family$d2ldmdv, envir=new.env()) <- alist(nu = nu)
formals(family$d2ldddv, envir=new.env()) <- alist(nu = nu)
## initial values for nu
if(!is.null(nu.start))
{nu <- if(length(nu.start)>1) nu.start else rep(nu.start,N)}
else { eval(family$nu.initial) } #
}
##-----------------------------------------------------------------------------------------
## Set up for the 4rd parameter submodel:
## tau.X design matrix
## tau.offset offset in linear predictor
## tau.start starting values for tau (optional)
##-----------------------------------------------------------------------------------------
if ("tau"%in%names(family$parameters))
{
tau.fit <- list() # MS Thursday, January 23, 2003 at 14:48
form.tau <- other.formula(form = tau.formula)
tau.terms <- if(missing(data)) terms(form.tau, specials = .gamlss.sm.list)
else terms(form.tau, specials = .gamlss.sm.list, data = data)
mcall$formula <- tau.terms
attr(mcall$formula, ".Environment") <- orig.Envir # DS fix for Willem Thursday, March 18, 2010
tau.frame <- eval(mcall,sys.parent())
tau.terms <- attr(tau.frame, "terms") #
tau.a <- attributes(tau.terms) #
tau.smoothers <- get.smoothers(tau.terms)
tau.X <- model.matrix(form.tau, tau.frame, contrasts)
tau.offset <- model.extract(tau.frame, offset)
if(is.null(tau.offset)) tau.offset <- rep(0,N)
tau.object <- get.object("tau")
formals(tau.object$dldp, envir=new.env()) <- alist(tau = fv) #
formals(tau.object$d2ldp2, envir=new.env()) <- alist(tau = fv) #
formals(tau.object$G.di, envir=new.env()) <- alist(tau = fv) #
formals(tau.object$valid, envir=new.env()) <- alist(tau = fv) #
formals(family$d2ldmdt, envir=new.env()) <- alist(tau = tau)
formals(family$d2ldddt, envir=new.env()) <- alist(tau = tau)
formals(family$d2ldvdt, envir=new.env()) <- alist(tau = tau)
## initial values for tau
if(!is.null(tau.start))
{tau <- if(length(tau.start)>1) tau.start else rep(tau.start,N)}
else { eval(family$tau.initial) } #
}
##-----------------------------------------------------------------------------------------
##-----------------------------------------------------------------------------------------
##=========================================================================================
## Checking whether proper algorithm (RS, CG or mixed)
##=========================================================================================
##-----------------------------------------------------------------------------------------
name.method <- substitute(method)
name.method <- deparse(name.method[1])
list.methods <- c("RS()","CG()","mixed()")
i.method <- pmatch(name.method,list.methods,nomatch=0)
if(!i.method) stop("Method must be RS(), CG() or mixed()")
##-----------------------------------------------------------------------------------------
## fitting the model
fiter <- 0
conv <- eval(substitute(method))
method <- substitute(method)
##-----------------------------------------------------------------------------------------
##-----------------------------------------------------------------------------------------
## Getting the GAMLSS object out
## ----------------------------------------------------------------------------------------
## first the general output
## calculate the Global deviance again
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
out <- list(family = family$family, parameters = names(family$parameters),
call = gamlsscall, y = y, control = control, weights = w,
G.deviance = G.dev, N = N, rqres = family$rqres, iter = fiter,
type = family$type, method = method, contrasts = contrasts)
#, na.action=na.act
out$converged <- conv
out$residuals <- eval(family$rqres)
noObs <- if(all(trunc(w)==w)) sum(w) else N
out$noObs <- noObs
## binomial denominator
if(any(family$family%in%.gamlss.bi.list)) out$bd <- bd
##-----------------------------------------------------------------------------------------
saveParam <- control$save
## Output for mean model: ----------------------------------------------------------------
if ("mu"%in%names(family$parameters))
{
out <- c(out, mu = parameterOut(what="mu", save=saveParam))
}
else
{
out$mu.df <- 0
}
## define now the degrees of freedom for the fit and residuals
out$df.fit <- out$mu.df
out$pen <- out$mu.pen
out$df.residual <- noObs-out$mu.df
## Output for dispersion model: ----------------------------------------------------------
if ("sigma"%in%names(family$parameters))
{
out <- c(out, sigma = parameterOut(what="sigma", save=saveParam) )
out$df.fit <- out$mu.df + out$sigma.df
out$pen <- out$mu.pen + out$sigma.pen
out$df.residual <- noObs-out$mu.df-out$sigma.df
}
## output for nu ------------------------------------------------------------------------
if ("nu"%in%names(family$parameters))
{
out <- c(out, nu = parameterOut(what="nu", save=saveParam) )
out$df.fit <- out$mu.df+out$sigma.df+out$nu.df
out$df.residual <- noObs-out$mu.df-out$sigma.df-out$nu.df
out$pen <- out$mu.pen + out$sigma.pen + out$nu.pen
}
## output for tau -----------------------------------------------------------------------
if ("tau"%in%names(family$parameters))
{
out <- c(out, tau = parameterOut(what="tau", save=saveParam) )
out$df.fit <- out$mu.df+out$sigma.df+out$nu.df+out$tau.df
out$pen <- out$mu.pen + out$sigma.pen + out$nu.pen + out$tau.pen
out$df.residual <- noObs-out$mu.df-out$sigma.df- out$nu.df -out$tau.df
}
##=======================================================================================
out$P.deviance <- out$G.deviance+out$pen # ms Thursday, May 13, 2004
out$aic <- G.dev+2*out$df.fit
out$sbc <- G.dev+log(noObs)*out$df.fit
#MS Thursday, April 22, 2004 at 11:26
# if ((ls(1,pattern="fiter")=="fiter")) rm(fiter, envir = as.environment(1))
#MS Thursday, January 8, 2004 at 17:52
class(out) <- c("gamlss","gam","glm","lm")
out
}
#########################################################################################
## the END of gamlss
#########################################################################################
#########################################################################################
## control gamlss
#########################################################################################
gamlss.control <- function(c.crit = 0.001, n.cyc = 20, mu.step = 1, sigma.step=1,
nu.step=1, tau.step=1, gd.tol = Inf, iter=0, trace = TRUE,
autostep = TRUE, save = TRUE, ...)
{
## Control iteration for GAMLSS
## Mikis Stasinopoulos Monday, March 25, 2002 at 16:17
## last change Thursday, October 12, 2006 save is added
##
if(c.crit <= 0) {
warning("the value of c.crit supplied is zero or negative the default value of 0.001 was used instead")
c.crit <- 0.001}
if(n.cyc < 1) {
warning("the value of no cycles supplied is zero or negative the default value of 20 was used instead")
n.cyc <- 20}
if(iter < 0) {
warning("the value of no iterations supplied is negative the default value of 0 was used instead")
iter <- 0}
if(mu.step > 1 | mu.step < 0) {
warning("the value of mu.step supplied is less than zero or more than one the default value of 1 was used instead")
mu.step <- 1}
if(sigma.step > 1 | sigma.step < 0) {
warning("the value of sigma.step supplied is less than zero or more than one the default value of 1 was used instead")
sigma.step <- 1}
if(nu.step > 1 | nu.step < 0) {
warning("the value of nu.step supplied is less than zero or more than one the default value of 1 was used instead")
nu.step <- 1}
if(tau.step > 1 | tau.step < 0) {
warning("the value of tau.step supplied is less than zero or more than one the default value of 1 was used instead")
tau.step <- 1}
if(gd.tol < 0) {
warning("the value of gd.tol supplied is less than zero the default value of Inf was used instead")
gd.tol <- Inf}
list(c.crit = c.crit, n.cyc = n.cyc, mu.step = mu.step, sigma.step = sigma.step, nu.step=nu.step,
tau.step = tau.step, gd.tol = gd.tol, iter = iter, trace = as.logical(trace)[1],
autostep = as.logical(autostep)[1], save = as.logical(save)[1] )
}
##---------------------------------------------------------------------------------------
glim.control <- function(cc = 0.001, cyc = 50, glm.trace = FALSE, bf.cyc = 30, # step = 1,
bf.tol = 0.001, bf.trace = FALSE, ...)
{
## Control iteration for GLIM
## MS Sunday, February 17, 2002 at 19:18
##
if(cc <= 0) {
warning("the value of cc supplied is zero or negative the default value of 0.001 was used instead")
cc <- 0.001}
if(bf.tol <= 0) {
warning("the value of bf.tol supplied is zero or negative the default value of 0.001 was used instead")
bf.tol <- 0.001}
if(cyc < 1) {
warning("the value of cyc supplied is zero or negative the default value of 20 was used instead")
cyc <- 20}
if(bf.cyc < 1) {
warning("the value of bf.cyc supplied is zero or negative the default value of 30 was used instead")
bf.cyc <- 30}
# if(step > 1 | step < 0) {
# warning("the value of step supplied is less than zero or more than one the default value of 1 was used instead")
# step <- 1}
list(cc = cc, cyc = cyc, glm.trace = as.logical(glm.trace)[1], bf.cyc = bf.cyc, # step = step,
bf.tol = bf.tol, bf.trace = as.logical(bf.trace)[1] )# MS Thursday, January 8, 2004 at 17:53
}
##----------------------------------------------------------------------------------------
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R Package Documentation
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Defines functions glim.control gamlss.control gamlss gamlssNews .onAttach
Documented in gamlss gamlss.control gamlssNews glim.control
## offsets are saved for the vcov() to work 1-5-13
## revisised Friday, March 19, 2010 to correct a bug reported by Dr Willem Vervoort
## major revision MS Wednesday, December 29, 2004 at 18:04
## to unify the output and tidy the input
.onAttach <- function(...)
{
packageStartupMessage(paste(paste(" ********** GAMLSS Version", packageDescription("gamlss")$Version), " ********** \n")
,appendLF = FALSE)
packageStartupMessage(paste("For more on GAMLSS look at", packageDescription("gamlss")$URL))
packageStartupMessage("Type gamlssNews() to see new features/changes/bug fixes.\n")
}
# packageStartupMessage(cat(paste(" ********** GAMLSS Version", packageDescription("gamlss")$Version), "********** \n")
# ;cat(paste("For more on GAMLSS look at", packageDescription("gamlss")$URL), "\n")
# ;cat("Type gamlssNews() to see new features/changes/bug fixes.\n")
# , appendLF = FALSE)
# cat(paste(" ********** GAMLSS Version", packageDescription("gamlss")$Version), "********** \n")
# cat(paste("For more on GAMLSS look at", packageDescription("gamlss")$URL), "\n")
# cat("Type gamlssNews() to see new features/changes/bug fixes.\n")
# }
gamlssNews <- function() file.show(system.file("doc", "NEWS.txt", package="gamlss"))# Monday, September 5, 2005 MS
#.onAttach <- function(libname, pkgname) {
# gamlssver <- if (as.numeric(R.version$major) < 2 &&
# as.numeric(R.version$minor) < 9.0)
# package.description("gamlss")["Version"] else
# packageDescription("gamlss")$Version
# cat(paste("gamlss", gamlssver), "\n")
# cat("Type gamlssNews() to see new features/changes/bug fixes.\n")
#}
##
##----------------------------------------------------------------------------------------
.gamlss.bi.list<-c("BI", "Binomial", "BB", "Beta Binomial", "ZIBI", "ZIBB",
"ZABI", "ZABB", "DBI", "BItr", "BBtr", "ZIBItr", "ZIBBtr",
"ZABItr", "ZABBtr", "DBItr") # binomial denominators
.gamlss.multin.list<-c("MULTIN", "MN3", "MN4", "MN5")
##----------------------------------------------------------------------------------------
.gamlss.sm.list<-c("cs", "scs", # smoothing cubic splines "s" vc"
"ps", "pb", "cy", "tp", "pvc", # penalised splines : ps, pb, cy tp pvc pbq
"pbm", "pbj", "pbo", "pbz", # monotone jumps and going to zero
"pbc", "pbts", "pbp", "pbf", # pb cycle pb predict
"pcat", "pba", # for categorical to reduce levels
"pbq", # pb using Qfunction
"gmrf", # Gaussian Markov random fields
"mrfa", "mrf", # Markov random fields
"so", "SOP", # seperation of Anisotropic penalties
"krig" , # kriging
"lo", # loess
"random","re", "re4", # random effect "ra","rc","rash"
"fp","pp", # fractional poly
"nl", # non-linear (.nl)
"ri", # ridge regression "ridge",
"boost", # boosting
"fk", # free knot smoothing
"own", "test", "test0","test1",# for testing new
"arma", # time series
"rw", "ar", "seas", # time series
"srw", "sar", # time series seasonal
"la", "plag", # time series penalised lags,
"tr", # decision trees
"ga", "ba", "mm", # gam - bam Simon Wood
"nn", # neural netwoks
"sv", # suppor vector machines
"ma", # mars
"pcr", # principal component Reg
"pc", # principal component regr
"h2o", # h2o project
"pa", # partial regression
"gnet", "lrs", # glmnet and lars
"rf1", # random forest ranger
"rf2", # generalised random forest
"gbo", # generalised Tree boosting
"gsel") # gamsel
# note that predict only read 2 characters Monday, May 4, 2009 at 11:27
##----------------------------------------------------------------------------------------
# .gamlss.sm.list<-c( "ar", "arma",
# "cs", "cy",
# "fk","fp",
# "ga",
# "krig",
# "la","lo",
# "mrf", "mrfa",
# "nl","nn",
# "ri", "rw",
# "pb", "pbj", "pbm", "pbo", "pbq","pbz",
# "pp","ps", "pvc",
# "random","re",# random effect
# "sap", "sap3", "seas",
# "test", "test0","test1","tr",
# "own")
##========================================================================================
## Generalised Additive Models for Location Scale and Shape
## R-functions created
## originaly created 6 Feb 2002.
## Last maijor revision Saturday, Thursday, January 9, 2003 at 09:48
##
## y the response variable
## X [1,2,3,4] design matrices
## w weights for weighed likelihood analysis (not as in GLM and GAM)
## *offset offsets in linear predictor for mu, sigma,nu, tau
## family response family see for available GAMLSS families
## *start starting values for mu, sigma, nu or tau (optional)
## *fix whether the specific parameter should be remained
## fixed in the fitting procedure
#========================================================================================
### The gamlss function contains the sub-functions
### RS() CG() and mixed()
###
#----------------------------------------------------------------------------------------
library(survival)
gamlss <- function(formula = formula(data),
sigma.formula = ~1,
nu.formula = ~1,
tau.formula = ~1,
family = NO(),
data,
weights = NULL, # for weighted likelihood analysis
# (not the same as in GLM's)
contrasts = NULL, # one type of contrasts for all parameters
method = RS(), # default algorithm
start.from = NULL, # starting from previous gamlss object
mu.start = NULL, # starting from given values
sigma.start = NULL,
nu.start = NULL,
tau.start = NULL,
mu.fix = FALSE,# whether the parameter is fixed
sigma.fix = FALSE,
nu.fix = FALSE,
tau.fix = FALSE,
control = gamlss.control(...),
i.control = glim.control(...), # the inner circle control (GLIM)
...)
{
## na.action = na.fail(), # both na.action and subset have been removed
## because while there is only one data set there are usually
## subset = NULL, four different model frames created therefore it is easier to apply
## sub-setting and na.action to the whole data set not to the
## frames
##---------------------------------------------------------------------------------------
## require(stats) Thursday, June 10, 2004 at 09:58 MS
#require(splines) # this will be removed with namespaces
#----------------------------------------------------------------------------------------
#gamlss.rc.list<-c("EX.rc","Exponential.rc") # the right censoring distribution list
#gamlss.bi.list<-c("BI", "Binomial", "BB", "Beta Binomial") # binomial denominators
#.gamlss.multin.list<-c("MULTIN", "MN3", "MN4", "MN5")
# ---------------------------------------------------------------------------------------
# this is to replicate rqres within gamlss enviroment DS Friday, March 31, 2006 at 10:30
rqres <- function (pfun = "pNO",
type = c("Continuous", "Discrete", "Mixed"),
censored = NULL,
ymin = NULL,
mass.p = NULL,
prob.mp = NULL,
y = y,
... )
{ }
body(rqres) <- eval(quote(body(rqres)), envir = getNamespace("gamlss"))
##---------------------------------------------------------------------------------------
##---------------------------------------------------------------------------------------
## first the definition of the three algorithms
##---------------------------------------------------------------------------------------
## the mixing algorithm
##---------------------------------------------------------------------------------------
##---------------------------------------------------------------------------------------
mixed <- function(n1=1,n2=20)
{
conv <- RS(n.cyc=n1, no.warn = FALSE)
conv <- CG(n.cyc=n2)
conv
}
##---------------------------------------------------------------------------------------
## the Rigby and Stasinopoulos algorithm
##---------------------------------------------------------------------------------------
RS <- function(n.cyc = control$n.cyc, no.warn = TRUE)
{
## this is to emulate the GLIM iterative algorithm
## created by Mikis Stasinopoulos Monday, February 18, 2002 at 09:23
## last change Wednesday, February 23, 2005 at 20:23
## where the step has changed to apply to the fitted values
glim.fit <- function(f,X,y,w,fv,os, step = 1,control = glim.control(),
auto, gd.tol)
{
cc <- control$cc # convergence criterion-tolerance
cyc <- control$cyc # max. no. of cycles
trace <- control$glm.trace # whether to print
bf.cyc <- control$bf.cyc
bf.tol <- control$bf.tol
bf.trace <- control$bf.trace
itn <- 0
lp <- eta <- f$linkfun(fv)
dr <- f$dr(eta) # dmu/deta
dr <- 1/dr # deta/dmu = 1 / (dmu/deta)
di <- f$G.di(fv) # deviance increment
dv <- sum(w*di) # the global deviance
olddv <- dv+1 # the old global deviance
dldp <- f$dldp(fv) # u score
d2ldp2 <- f$d2ldp2(fv) # second derivative of log-Likelihood
d2ldp2 <- ifelse(d2ldp2 < -1e-15, d2ldp2,-1e-15) # added 26-10-07
wt <- -(d2ldp2/(dr*dr))# -(d2l/dp2)/(1/(dmu/deta))^2=- (d2l/dp2)(dmu/eta)^2
# we need to stop the weights to go to Infty
wt <- ifelse(wt>1e+10,1e+10,wt) # Mikis 9-10-14
wt <- ifelse(wt<1e-10,1e-10,wt)
# wv <- (eta-os)+step*dldp/(dr*wt)
wv <- (eta-os)+dldp/(dr*wt) # eta
if (family$type=="Mixed") wv <-ifelse(is.nan(wv),0,wv) ## TEST
iterw <- FALSE
who <- f$who
smooth.frame <- f$smooth.frame
s <- f$smooth
## starting the recycling
while ( abs(olddv-dv) > cc && itn < cyc ) # MS Wednesday, June 26, 2002
{
itn <- itn+1 # the glim inner iteration number
lpold <- lp
sold <- s
if (any(is.na(wt))||any(is.na(wv)) ) stop("NA's in the working vector or weights for parameter ", names(formals(f$valid)), "\n")
if (any(!is.finite(wt))||any(!is.finite(wv)) ) stop("Inf values in the working vector or weights for parameter ", names(formals(f$valid)), "\n")
if(length(who) > 0)
{
fit <- additive.fit(x=X,y=wv,w=wt*w,s=s,who=who,smooth.frame, maxit = bf.cyc,
tol = bf.tol, trace = bf.trace)
# lp <- fit$fitted.values
lp <- if (itn==1) fit$fitted.values else step*fit$fitted.values+(1-step)*lpold
# s <- fit$smooth # test Wednesday, January 8, 2003 at 14:37
s <- if (itn==1) fit$smooth else step*fit$smooth+(1-step)*sold
}
else
{
fit <- lm.wfit(X,wv,wt*w,method="qr")
lp <- if (itn==1) fit$fitted.values else step*fit$fitted.values+(1-step)*lpold
# lp <- fit$fitted.values
}
## method 1
eta <- lp+os # fixed Wednesday, September 4, 2002 at 09:45 DS
## own link
fv <- f$linkinv(eta)
## own dist
di <- f$G.di(fv)
olddv <- dv
dv <- sum(w*di)
## new for automatic steps MS BR Friday, April 15, 2005 at 18:50
if (dv > olddv && itn >= 2 && auto==TRUE)
{
for(i in 1:5) # MS Thursday, September 22, 2005
{
lp <- (lp+lpold)/2
eta <- lp+os
fv <- f$linkinv(eta)
di <- f$G.di(fv)
dv <- sum(w*di)
# cat("try",i,"\n")
if(length(who) > 0) s <- (s+sold)/2
if ((olddv-dv) > cc) break # MS Thursday, September 22, 2005
}
}
if ((dv > olddv+gd.tol ) && itn >= 2 && iterw==FALSE)
{
warning("The deviance has increased in an inner iteration for ",
names(formals(f$valid)), "\n","Increase gd.tol and if persist, try different steps", "\n", "or model maybe inappropriate") #
iterw <-TRUE
}
if (is.na(!f$valid(fv)) ) # MS Saturday, April 6, 2002 at 18:06
stop( "fitted values in the inner iteration out of range")
dr <- f$dr(eta)
dr <- 1/dr
## method 2
dldp <- f$dldp(fv)
d2ldp2 <- f$d2ldp2(fv)
d2ldp2 <- ifelse(d2ldp2 < -1e-15, d2ldp2,-1e-15) # added 26-10-07
wt <- -(d2ldp2/(dr*dr))
wt <- ifelse(wt>1e+10,1e+10,wt) # Mikis 9-10-14
wt <- ifelse(wt<1e-10,1e-10,wt)
# wv <- (eta-os)+step*dldp/(dr*wt)
wv <- (eta-os)+dldp/(dr*wt)
if (family$type=="Mixed") wv <-ifelse(is.nan(wv),0,wv) ## TEST
# olddv <- dv
# dv <- sum(w*di)
if(trace)
cat("GLIM iteration ", itn, " for ", names(formals(f$valid)), ": Global Deviance = ",
format(round(dv, 4)), " \n", sep = "")
} #end of while
pen <- 0 # DS Thursday, November 21, 2002 at 23:04
if(length(who) > 0) {pen<- sum(eta*wt*(wv-eta)) }
c(fit, list(fv = fv, wv = wv, wt = wt, eta = eta, os = os, pen=pen)) #ms Saturday, December 4, 2004
}
##-end of GLIM.fit------------------------------------------------------------
##---start RS-----------------------------------------------------------------
## getting the contol papameters
c.crit <- control$c.crit
# n.cyc <- control$n.cyc
trace <- control$trace
autostep <- control$autostep
mu.step <- control$mu.step
sigma.step <- control$sigma.step
nu.step <- control$nu.step
tau.step <- control$tau.step
gd.tol <- control$gd.tol
iter <- control$iter
conv <- FALSE
## initial Gloval deviance
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
G.dev.old <- G.dev+1
##----------------------------------------------------------------------------
## the outer iteration starts here
while ( abs(G.dev.old-G.dev) > c.crit && iter < n.cyc )
{
# the mean submodel
if ("mu"%in%names(family$parameters))
{
if (family$parameter$mu==TRUE & mu.fix==FALSE)
{
#mu.old <- mu
mu.fit <<- glim.fit(f = mu.object, X = mu.X, y = y, w = w,
fv = mu, os = mu.offset, step = mu.step,
control = i.control, gd.tol = gd.tol,
auto = autostep)
mu <<- mu.fit$fv
mu.object$smooth <- mu.fit$smooth
}
}
# the scale-dispersion submodel
if ("sigma"%in%names(family$parameters))
{
if ( family$parameter$sigma==TRUE & sigma.fix==FALSE)
{
#sigma.old <- sigma
sigma.fit <<- glim.fit(f = sigma.object, X = sigma.X, y = y,
w = w, fv = sigma, os = sigma.offset,
step = sigma.step, control = i.control,
gd.tol = gd.tol, auto = autostep)
sigma <<- sigma.fit$fv
sigma.object$smooth <- sigma.fit$smooth
}
}
# the nu submodel
if ("nu"%in%names(family$parameters))
{
if ( family$parameter$nu==TRUE & nu.fix==FALSE)
{
# nu.old <- nu
nu.fit <<- glim.fit(f = nu.object, X = nu.X, y = y,
w = w, fv = nu, os = nu.offset,
step = nu.step, control = i.control,
gd.tol = gd.tol, auto = autostep)
nu <<- nu.fit$fv
nu.object$smooth <- nu.fit$smooth
}
}
# the tau submodel
if ("tau"%in%names(family$parameters))
{
if (family$parameter$tau ==TRUE & tau.fix==FALSE)
{
# tau.old <- tau
tau.fit <<- glim.fit(f = tau.object, X = tau.X, y = y,
w = w, fv = tau, os = tau.offset,
step = tau.step, control = i.control,
gd.tol = gd.tol, auto = autostep)
tau <<- tau.fit$fv
tau.object$smooth <- tau.fit$smooth
}
}
# the overall Global Deviance
G.dev.old <- G.dev
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
iter <- iter+1
fiter <<- iter
if(trace)
cat("GAMLSS-RS iteration ", iter, ": Global Deviance = ",
format(round(G.dev, 4)), " \n", sep = "")
if (G.dev > (G.dev.old+gd.tol) && iter >1 )
stop(paste("The global deviance is increasing", "\n",
"Try different steps for the parameters or the model maybe inappropriate"))
}
if ( abs(G.dev.old-G.dev) < c.crit ) # MS Wednesday, June 11, 2003 at 11:58
#taken out (abs((G.dev-G.dev.old)/(0.1+abs(G.dev.old)))<c.crit&&iter<=n.cyc)
conv <- TRUE else FALSE
if (!conv && no.warn ) warning("Algorithm RS has not yet converged")
conv
}
##=======================================================================================
## End of GAMLSS RS fitting algorithm
##=======================================================================================
##---------------------------------------------------------------------------------------
##---------------------------------------------------------------------------------------
#========================================================================================
## the gamlss CG (Cole and Green) fitting algorithm
##=======================================================================================
#----------------------------------------------------------------------------------------
CG<-function(n.cyc=control$n.cyc)
{
#========================================================================================
# getting the contol papameters
#========================================================================================
#----------------------------------------------------------------------------------------
c.crit <- control$c.crit
#n.cyc <- control$n.cyc
trace <- control$trace
mu.step <- control$mu.step
sigma.step <- control$sigma.step
nu.step <- control$nu.step
tau.step <- control$tau.step
gd.tol <- control$gd.tol
autostep <- control$autostep
iter <- control$iter
conv <- FALSE
i.c.crit <- i.control$cc
i.n.cyc <- i.control$cyc
i.trace <- i.control$glm.trace
# bf.cyc <- i.control$bf.cyc
bf.tol <- i.control$bf.tol
bf.trace <- i.control$bf.trace
first.iter <- TRUE #ms Thursday, November 6, 2003 at 09:04
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
G.dev.old <- G.dev+1
# initial values for the variates used in the inner iteration
w.mu.sigma<-w.mu.nu<-w.mu.tau<-w.sigma.nu<-w.sigma.tau<-w.nu.tau<-rep(0,N)
eta.mu<-eta.old.mu<-eta.sigma<-eta.old.sigma<-eta.nu<-eta.old.nu<-rep(0,N)
eta.old.tau<-eta.tau<-rep(0,N) #???
# here starts the outer iteration
#=======================================================================================
while ( abs(G.dev.old-G.dev) > c.crit && iter < n.cyc )
{
i.iter <- 0
## for mu
if ("mu"%in%names(family$parameters))
{
eta.mu <- eta.old.mu <- family$mu.linkfun(mu)
u.mu <- mu.object$dldp(mu=mu)
u2.mu <- mu.object$d2ldp2(mu=mu)
dr.mu <- family$mu.dr(eta.mu)
dr.mu <- 1/dr.mu
who.mu <- mu.object$who
smooth.frame.mu <- mu.object$smooth.frame
s.mu <- if(first.iter) mu.object$smooth else s.mu
w.mu <- -u2.mu/(dr.mu*dr.mu)
z.mu <- (eta.old.mu-mu.offset)+mu.step*u.mu/(dr.mu*w.mu)
}
## for sigma
if ("sigma"%in%names(family$parameters))
{
eta.sigma <- eta.old.sigma <- family$sigma.linkfun(sigma)
u.sigma <- sigma.object$dldp(sigma=sigma)
u2.sigma <- sigma.object$d2ldp2(sigma=sigma)
u2.mu.sigma <- family$d2ldmdd(sigma=sigma)
dr.sigma <- family$sigma.dr(eta.sigma)
dr.sigma <- 1/dr.sigma
who.sigma <- sigma.object$who
smooth.frame.sigma <- sigma.object$smooth.frame
s.sigma <- if(first.iter) sigma.object$smooth else s.sigma
w.sigma <- -u2.sigma/(dr.sigma*dr.sigma)
w.mu.sigma <- -u2.mu.sigma/(dr.mu*dr.sigma)
z.sigma <- (eta.old.sigma-sigma.offset)+sigma.step*u.sigma/
(dr.sigma*w.sigma)
}
## for nu
if ("nu"%in%names(family$parameters))
{
eta.nu <- eta.old.nu <- family$nu.linkfun(nu)
u.nu <- nu.object$dldp(nu=nu)
u2.nu <- nu.object$d2ldp2(nu=nu)
u2.mu.nu <- family$d2ldmdv(nu=nu)
u2.sigma.nu <- family$d2ldddv(nu=nu)
dr.nu <- family$nu.dr(eta.nu)
dr.nu <- 1/dr.nu
who.nu <- nu.object$who
smooth.frame.nu <- nu.object$smooth.frame
s.nu <- if(first.iter) nu.object$smooth else s.nu
w.nu <- -u2.nu/(dr.nu*dr.nu)
w.mu.nu <- -u2.mu.nu/(dr.mu*dr.nu)
w.sigma.nu <- -u2.sigma.nu/(dr.sigma*dr.nu)
z.nu <- (eta.old.nu-nu.offset)+nu.step*u.nu/
(dr.nu*w.nu)
}
## for tau
if ("tau"%in%names(family$parameters))
{
eta.tau <- eta.old.tau <- family$tau.linkfun(tau)
u.tau <- tau.object$dldp(tau=tau)
u2.tau <- tau.object$d2ldp2(tau=tau)
u2.mu.tau <- family$d2ldmdt(tau=tau)
u2.sigma.tau <- family$d2ldddt(tau=tau)
u2.nu.tau <- family$d2ldvdt(tau=tau)
dr.tau <- family$tau.dr(eta.tau)
dr.tau <- 1/dr.tau
who.tau <- tau.object$who
smooth.frame.tau <- tau.object$smooth.frame
s.tau <- if(first.iter) tau.object$smooth else s.tau
w.tau <- -u2.tau/(dr.tau*dr.tau)
w.mu.tau <- -u2.mu.tau/(dr.mu*dr.tau)
w.sigma.tau <- -u2.sigma.tau/(dr.sigma*dr.tau)
w.nu.tau <- -u2.nu.tau/(dr.nu*dr.tau)
z.tau <- (eta.old.tau-tau.offset)+tau.step*u.tau/
(dr.tau*w.tau)
}
## initial G. devinace for the inner iteration
G.dev.in <- G.dev+1
i.G.dev <- G.dev
first.iter <- FALSE
## the inner iteration starts here
##--------------------------------------------------------------------------------------
while ( abs(G.dev.in -i.G.dev) > i.c.crit && i.iter < i.n.cyc )
{
if ("mu"%in%names(family$parameters))
{
if (family$parameter$mu==TRUE & mu.fix==FALSE)
{
adj.mu <- -(w.mu.sigma*(eta.sigma-eta.old.sigma)+
w.mu.nu*(eta.nu-eta.old.nu)+w.mu.tau*(eta.tau-eta.old.tau))/w.mu
wv.mu <- z.mu+adj.mu
if(length(who.mu) > 0)
{
mu.fit <<- additive.fit(x = mu.X, y = wv.mu, w = w.mu*w,s = s.mu,
who = who.mu, smooth.frame = smooth.frame.mu, maxit = 1,
tol = bf.tol, trace = bf.trace)
mu.fit$eta <<- eta.mu <- mu.fit$fitted.values+mu.offset
mu.fit$fv <<- mu <<- mu.object$linkinv(eta.mu)
s.mu.old <- s.mu
s.mu <- mu.fit$smooth
mu.fit$pen <<- sum(eta.mu*w.mu*(wv.mu-eta.mu))
mu.fit$wv <<- wv.mu
mu.fit$wt <<- w.mu
mu.fit$os <<- mu.offset
}
else
{
mu.fit <<- lm.wfit(x=mu.X,y=wv.mu,w=w.mu*w,method="qr")
mu.fit$eta <<- eta.mu <- mu.fit$fitted.values+mu.offset
mu.fit$fv <<- mu <<- mu.object$linkinv(eta.mu)
mu.fit$wv <<- wv.mu
mu.fit$wt <<- w.mu
mu.fit$os <<- mu.offset
}
}
}
##fit sigma
if ("sigma"%in%names(family$parameters))
{
if (family$parameter$sigma==TRUE & sigma.fix==FALSE)
{
adj.sigma <- -(w.mu.sigma*(eta.mu-eta.old.mu)+
w.sigma.nu*(eta.nu-eta.old.nu)+w.sigma.tau*(eta.tau-eta.old.tau))/w.sigma
wv.sigma <- z.sigma+adj.sigma
if(length(who.sigma) > 0)
{
sigma.fit <<- additive.fit(x = sigma.X, y = wv.sigma,
w = w.sigma*w,s = s.sigma,
who = who.sigma,
smooth.frame = smooth.frame.sigma,
maxit = 1,
tol = bf.tol, trace = bf.trace)
sigma.fit$eta <<- eta.sigma <- sigma.fit$fitted.values+sigma.offset
sigma.fit$fv <<- sigma <<- sigma.object$linkinv(eta.sigma)
s.sigma.old <- s.sigma
s.sigma <- sigma.fit$smooth
sigma.fit$pen <<- sum(eta.sigma*w.sigma*(wv.sigma-eta.sigma))
sigma.fit$wv <<- wv.sigma
sigma.fit$wt <<- w.sigma
sigma.fit$os <<- sigma.offset
}
else
{
sigma.fit <<- lm.wfit(x=sigma.X,y=wv.sigma,w=w.sigma*w,method="qr")
sigma.fit$eta <<- eta.sigma <- sigma.fit$fitted.values+sigma.offset
sigma.fit$fv <<- sigma <<- sigma.object$linkinv(eta.sigma)
sigma.fit$wv <<- wv.sigma
sigma.fit$wt <<- w.sigma
sigma.fit$os <<- sigma.offset
}
}
}
##fit nu
if ("nu"%in%names(family$parameters))
{
if (family$parameter$nu==TRUE & nu.fix==FALSE)
{
adj.nu <- -(w.mu.nu*(eta.mu-eta.old.mu)+
w.sigma.nu*(eta.sigma-eta.old.sigma)+w.nu.tau*(eta.tau-eta.old.tau))/w.nu
wv.nu <- z.nu+adj.nu
if(length(who.nu) > 0)
{
nu.fit <<- additive.fit(x = nu.X,y = wv.nu, w = w.nu*w,s = s.nu,
who = who.nu, smooth.frame = smooth.frame.nu,
maxit = 1,
tol = bf.tol, trace = bf.trace)
nu.fit$eta <<- eta.nu <- nu.fit$fitted.values+nu.offset
nu.fit$fv <<- nu <<- nu.object$linkinv(eta.nu)
s.nu.old <- s.nu
s.nu <- nu.fit$smooth
nu.fit$pen <<- sum(eta.nu*w.nu*(wv.nu-eta.nu))
nu.fit$wv <<- wv.nu
nu.fit$wt <<- w.nu
nu.fit$os <<- nu.offset
}
else
{
nu.fit <<- lm.wfit(x=nu.X,y=wv.nu,w=w.nu*w,method="qr")
nu.fit$eta <<- eta.nu <- nu.fit$fitted.values+nu.offset
nu.fit$fv <<- nu <<- nu.object$linkinv(eta.nu)
nu.fit$wv <<- wv.nu
nu.fit$wt <<- w.nu
nu.fit$os <<- nu.offset
}
}
}
##fit tau
if ("tau"%in%names(family$parameters))
{
if (family$parameter$tau==TRUE & tau.fix==FALSE)
{
adj.tau <- -(w.mu.tau*(eta.mu-eta.old.mu)+
w.sigma.tau*(eta.sigma-eta.old.sigma)+w.nu.tau*(eta.nu-eta.old.nu))/w.tau
wv.tau <- z.tau+adj.tau
if(length(who.tau) > 0)
{
tau.fit <<- additive.fit(x = tau.X,y = wv.tau, w = w.tau*w,s = s.tau,
who = who.tau, smooth.frame = smooth.frame.tau,
maxit = 1,
tol = bf.tol, trace = bf.trace)
tau.fit$eta <<- eta.tau <- tau.fit$fitted.values+tau.offset
tau.fit$fv <<- tau <<- tau.object$linkinv(eta.tau)
s.tau.old <- s.tau
s.tau <- tau.fit$smooth
tau.fit$pen <<- sum(eta.tau*w.tau*(wv.tau-eta.tau))
tau.fit$wv <<- wv.tau
tau.fit$wt <<- w.tau
tau.fit$os <<- tau.offset
}
else
{
tau.fit <<- lm.wfit(x=tau.X,y=wv.tau,w=w.tau*w,method="qr")
tau.fit$eta <<- eta.tau <- tau.fit$fitted.values+tau.offset
tau.fit$fv <<- tau <<- tau.object$linkinv(eta.tau)
tau.fit$wv <<- wv.tau
tau.fit$wt <<- w.tau
tau.fit$os <<- tau.offset
}
}
}
G.dev.in <- i.G.dev
G.dev.incr <- eval(G.dev.expr)
i.G.dev <- sum(w*G.dev.incr)
i.iter <- i.iter+1
if(i.trace)
cat("CG inner iteration ", iter, ": Global Deviance = ",
format(round(i.G.dev, 4)), " \n", sep = "")
if (i.G.dev > (G.dev.in+gd.tol) && iter >1 )
stop(paste("The global deviance is increasing in the inner CG loop", "\n",
"Try different steps for the parameters or the model maybe inappropriate"))
}
## the inner iteration finish here
##--------------------------------------------------------------------------------------
G.dev.old <- G.dev
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
# autostep<-FALSE
## new for automatic steps MS BR Friday, April 15, 2005 at 18:50
if (G.dev > G.dev.old && iter >= 2 && autostep == TRUE) # && itn >= 2 )
{
for(i in 1:5)
{
if ("mu"%in%names(family$parameters))
{
eta.mu <- (eta.mu+eta.old.mu)/2
mu <<- mu.object$linkinv(eta.mu)
if(length(who.mu) > 0) s.mu <- (s.mu+s.mu.old)/2
}
if ("sigma"%in%names(family$parameters))
{
eta.sigma <- (eta.sigma+eta.old.sigma)/2
sigma <<- sigma.object$linkinv(eta.sigma)
if(length(who.sigma) > 0) s.sigma <- (s.sigma+s.sigma.old)/2
}
if ("nu"%in%names(family$parameters))
{
eta.nu <- (eta.nu+eta.old.nu)/2
nu <<- nu.object$linkinv(eta.nu)
if(length(who.nu) > 0) s.nu <- (s.nu+s.nu.old)/2
}
if ("tau"%in%names(family$parameters))
{
eta.tau <- (eta.tau+eta.old.tau)/2
tau <<- tau.object$linkinv(eta.tau)
if(length(who.tau) > 0) s.tau <- (s.tau+s.tau.old)/2
}
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
# cat("helow there \n")
# if(length(who) > 0) s <- (s+sold)/2
if (G.dev < G.dev.old) break
}
}
iter <- iter+1
fiter <<-iter
if(trace)
cat("GAMLSS-CG iteration ", iter, ": Global Deviance = ",
format(round(G.dev, 4)), " \n", sep = "")
if (G.dev > (G.dev.old+gd.tol) && iter >1 )
stop(paste("The global deviance is increasing in CG-algorithm ", "\n",
"Try different steps for the parameters or the model maybe inappropriate"))
}
if ( abs(G.dev.old-G.dev) < c.crit ) conv <- TRUE else FALSE # MS June 11, 2003
if (!conv) warning("Algorithm CG has not yet converged")
conv
##the outer iteration finish here
##=======================================================================================
}
##---------------------------------------------------------------------------------------
## the end of CG
##---------------------------------------------------------------------------------------
##=======================================================================================
##---------------------------------------------------------------------------------------
## this function is used in for outputing the parameters
##---------------------------------------------------------------------------------------
parameterOut <- function(what="mu", save)
{
out <- list()
if(save==TRUE)
{
if(family$parameter[[what]]==TRUE && eval(parse(text=paste(what,".fix",sep="")))==FALSE)
{
out$fv <- eval(parse(text=what))
out$lp <- eval(parse(text=(paste(what,".fit$eta", sep=""))))
out$wv <- eval(parse(text=(paste(what,".fit$wv", sep=""))))
out$wt <- eval(parse(text=(paste(what,".fit$wt", sep=""))))
out$link <- eval(parse(text=(paste(what,".object$link", sep=""))))
out$terms <- eval(parse(text=(paste(what,".terms", sep=""))))
out$x <- eval(parse(text=(paste(what,".X", sep=""))))
out$qr <- eval(parse(text=(paste(what,".fit$qr", sep=""))))
out$coefficients <- eval(parse(text=(paste(what,".fit$coefficients", sep=""))))
out$offset <- eval(parse(text=(paste(what,".fit$os", sep=""))))
out$xlevels <- .getXlevels( eval(parse(text=paste(what,".terms",sep="")))
, eval(parse(text=paste(what,".frame",sep=""))))
# ms Sunday, June 13 2004
out$formula <- eval(parse(text=paste(what,".formula",sep="")))
if(length(eval(parse(text=paste(what,".smoothers",sep="")))) > 0)
{ out$df <- eval(parse(text=paste(what,".fit$nl.df",sep="")))+
eval(parse(text=paste(what,".fit$rank",sep="")))
out$nl.df <- eval(parse(text=paste(what,".fit$nl.df",sep="")))
out$s <- eval(parse(text=paste(what,".fit$smooth",sep="")))
out$var <- eval(parse(text=paste(what,".fit$var",sep="")))
out$coefSmo <- eval(parse(text=paste(what,".fit$coefSmo",sep="")))
out$lambda <- eval(parse(text=paste(what,".fit$lambda",sep="")))
out$pen <- eval(parse(text=paste(what,".fit$pen",sep="")))
}
else
{ out$df <- eval(parse(text=paste(what,".fit$rank",sep="")))
out$nl.df <- 0
out$pen <- 0 #ms May 13, 2004
}
}
else
{ out$fix <- eval(parse(text=paste(what,".fix",sep="")))
out$df <- 0
out$fv <- eval(parse(text=what))
}
} # if(save== not TRUE)
else
{
if(family$parameter[[what]]==TRUE && eval(parse(text=paste(what,".fix",sep="")))==FALSE)
{
if(length(eval(parse(text=paste(what,".smoothers",sep="")))) > 0)
{ out$df <- eval(parse(text=paste(what,".fit$nl.df",sep="")))+
eval(parse(text=paste(what,".fit$rank",sep="")))
out$nl.df <- eval(parse(text=paste(what,".fit$nl.df",sep="")))
out$terms <- eval(parse(text=(paste(what,".terms", sep=""))))
out$formula <- eval(parse(text=paste(what,".formula",sep="")))
}
else
{
out$df <- eval(parse(text=paste(what,".fit$rank",sep="")))
out$nl.df <- 0
out$terms <- eval(parse(text=(paste(what,".terms", sep=""))))
out$formula <- eval(parse(text=paste(what,".formula",sep="")))
}
}
else
{
out$df <- 0
}
}
out
}
##=========================================================================================
##-----------------------------------------------------------------------------------------
## this function is used to extract the formula for the parameters other than mu
##-----------------------------------------------------------------------------------------
##=========================================================================================
other.formula <- function(form)
{
dform <- formula(form)
if (length(dform)==2)
{
dform[3] <- dform[2] # taking 1 in position [3]
dform[2] <- if (is(formula, "terms") )formula[[2]] else formula[2] # ms 31-12-08 # put y in position 2
}
dform
}
##=========================================================================================
##-----------------------------------------------------------------------------------------
## this function is getting the smoothers at each parameter
##-----------------------------------------------------------------------------------------
##=========================================================================================
get.smoothers <- function(term)
{
a <- attributes(term) #
smoothers <- a$specials #S convert variable pointers to term pointers
if(length(smoothers) > 0)
{
smoothers <- smoothers[sapply(smoothers, length) > 0]
# smoothersR <-smoothers
for(i in seq(along = smoothers))
{
tt <- smoothers[[i]]
ff <- apply(a$factors[tt, , drop = FALSE], 2, any)
smoothers[[i]] <- if(any(ff)) seq(along = ff)[a$order == 1 & ff]
else NULL
}
}
smoothers
}
##=========================================================================================
##-----------------------------------------------------------------------------------------
## this function creates the parameter objects
##-----------------------------------------------------------------------------------------
##=========================================================================================
get.object <- function(what)
{
link <- eval(parse(text=(paste("family$",what,".link", sep=""))))
linkfun <- eval(parse(text=(paste("family$",what,".linkfun", sep=""))))
linkinv <- eval(parse(text=(paste("family$",what,".linkinv", sep=""))))
dr <- eval(parse(text=(paste("family$",what,".dr", sep=""))))
dldp <- switch( what,
"mu" = family$dldm,
"sigma" = family$dldd,
"nu" = family$dldv,
"tau" = family$dldt
)
d2ldp2 <- switch( what,
"mu" = family$d2ldm2,
"sigma" = family$d2ldd2,
"nu" = family$d2ldv2,
"tau" = family$d2ldt2
)
G.di <- family$G.dev.incr
valid <- eval(parse(text=(paste("family$",what,".valid", sep=""))))
object <- list(link = link, linkfun = linkfun, linkinv = linkinv, dr = dr,
dldp = dldp, d2ldp2 = d2ldp2, G.di = G.di, valid = valid)
if(length(eval(parse(text=(paste(what,".smoothers", sep=""))))) > 0)
{ #only if smoothing
parAttrTermlevels <- eval(parse(text=(paste(what,".a$term.labels", sep=""))))
boo <- unlist(eval(parse(text=(paste(what,".smoothers", sep="")))))
who <- parAttrTermlevels[boo[ order(boo)]]
smooth.frame <- eval(parse(text=(paste(what,".frame", sep=""))))
s <- matrix(0, N, length(who))
dimnames(s) <- list(names(y), who)
object$smooth <- s
object$who <- who
object$smooth.frame <- smooth.frame
}
object
}
#==========================================================================================
##-----------------------------------------------------------------------------------------
##-----------------------------------------------------------------------------------------
##=========================================================================================
## here is where the proper gamlss function starts
##=========================================================================================
##-----------------------------------------------------------------------------------------
## Save call for future reference
gamlsscall <- match.call() # the function call
## checking for NA in the data
if(!missing(data))
{
if (any(is.na(data)))
stop("The data contains NA's, use data = na.omit(mydata)")
}
## Evaluate the model frame
mnames <- c("", "formula", "data", "weights" ) # "subset" "na.action"
cnames <- names(gamlsscall) # get the names of the arguments of the call
cnames <- cnames[match(mnames,cnames,0)] # keep only the ones that match with mnames
mcall <- gamlsscall[cnames] # get in mcall all the relevant information but remember
# that the first elenent will be NULL
mcall[[1]] <- as.name("model.frame") # replace NULL with model.frame
## Specials for smoothing
mcall$formula <- if(missing(data)) terms(formula, specials = .gamlss.sm.list)
else terms(formula, specials = .gamlss.sm.list, data = data)
mu.frame <- eval(mcall, sys.parent()) # evalute the data.frame at the model.frame
##-----------------------------------------------------------------------------------------
## This part deals with the family
family <- as.gamlss.family(family) # bring first the gamlss family
G.dev.expr <- body(family$G.dev.inc) #MS Thursday, April 11, 2002 at 10:34
# nopar <- family$nopar # the number of parameters for the family
##-----------------------------------------------------------------------------------------
## Now extract the model components using model.extra and model.matrix
## This part deals with the response variable
Y <- model.extract(mu.frame, "response") #extracting the y variable from the formula
if(is.null(dim(Y))) # if y not matrix
N <- length(Y) else N <- dim(Y)[1] # calculate the dimension for y
# .gamlss.bi.list <- if (exists("gamlss.bi.list",envir=.GlobalEnv))
# get("gamlss.bi.list", envir=.GlobalEnv) else .gamlss.bi.list
## extracting now the y and the binomial denominator in case we use BI or BB
if(any(family$family%in%.gamlss.bi.list))
{
if (NCOL(Y) == 1)
{
y <- if (is.factor(Y)) Y != levels(Y)[1] else Y
bd <- rep(1, N)
if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1")
}
else if (NCOL(Y) == 2)
{
if (any(abs(Y - round(Y)) > 0.001)) {
warning("non-integer counts in a binomial GAMLSS!")
}
bd <- Y[,1] + Y[,2]
y <- Y[,1]
if (any(y < 0 | y > bd)) stop("y values must be 0 <= y <= N") # MS Monday, October 17, 2005
}
else stop(paste("For the binomial family, Y must be",
"a vector of 0 and 1's or a 2 column", "matrix where col 1 is no. successes",
"and col 2 is no. failures"))
}
# multinomial checking
else if(any(family$family%in%.gamlss.multin.list))
{
y <- if(is.factor(Y)) unclass(Y)
else Y
}
## For censoring
else if(is.Surv(Y))
{
## checking that the family is censored
if (length(grep("censored",family$family[[2]]))==0)
stop(paste("the family in not a censored distribution, use cens()"))
## checking compatability of Surv object and censored distribution
if (length(grep(attr(Y,"type"),family$family[[2]]))==0)
stop(paste("the Surv object and the censored distribution are not of the same type"))
y <- Y
#if (NCOL(Y) == 2)
# {
# #.event <- Y[,2]
# # y <- Y[,1]
# y <- Y
# }
#else if (NCOL(Y) == 2)
#stop("interval censored data are not implemented in gamlss yet")
}
else {y <- Y }
##-----------------------------------------------------------------------------------------
##checking the permissible y values
if (!family$y.valid(y)) # MS Thursday, June 20, 2002 at 16:30
stop( "response variable out of range")
##-----------------------------------------------------------------------------------------
## this part is used if start.from is used as argument
##------------start.from fitted model--------
if(!is.null(start.from))
{
if (!is.gamlss(start.from))
stop(paste("The object in start.from is not a gamlss object", "\n", ""))
mu.start <- NULL
sigma.start <- NULL
nu.start <- NULL
tau.start <- NULL
## location model
if ("mu"%in%start.from$parameters)
{
mu.start <- start.from$mu.fv
}
## scale-dispersion submodel
if ("sigma"%in%start.from$parameters)
{
sigma.start <- start.from$sigma.fv
}
## nu submodel
if ("nu"%in%start.from$parameters)
{
nu.start <- start.from$nu.fv
}
## tau submodel
if ("tau"%in%start.from$parameters)
{
tau.start <- start.from$tau.fv
}
}
##-----------------------------------------------------------------------------------------
## checking the parameter.fix
if (!is.logical(mu.fix)) stop("mu.fix should be logical TRUE or FALSE")
if (!is.logical(sigma.fix)) stop("sigma.fix should be logical TRUE or FALSE")
if (!is.logical(nu.fix)) stop("nu.fix should be logical TRUE or FALSE")
if (!is.logical(tau.fix)) stop("tau.fix should be logical TRUE or FALSE")
##-----------------------------------------------------------------------------------------
## extract the weights
w <- model.extract(mu.frame, weights) # weights for the likelihood
if(is.null(w)) w <- rep(1, N)
else if(any(w < 0)) stop("negative weights not allowed") #
# else if (!all(trunc(w)==w)) warning("weights should be integer values \n",
# " indicating number of observations with identical values \n") #
##=========================================================================================
##-----------------------------------------------------------------------------------------
## Set up location-mean submodel:
## mu.X design matrix
## mu.offset offset in linear predictor
## mu.start starting values for mu (optional)
##-----------------------------------------------------------------------------------------
mu.fit <- list() # MS Thursday, January 23, 2003 at 14:46
mu.formula <- formula # ms Wednesday, December 29, 2004
mu.terms <- attr(mu.frame, "terms") # it peeks up the terms attribute
mu.smoothers <- get.smoothers(mu.terms)
mu.a <- attributes(mu.terms) # from the model.frame
mu.X <- model.matrix(mu.terms, mu.frame, contrasts) # the mean model matrix
mu.offset <- model.extract(mu.frame, offset) # the mean-location offset
if(is.null(mu.offset)) mu.offset <- rep(0,N)
mu.object <- get.object("mu")
formals(mu.object$dldp, envir=new.env()) <- alist(mu = fv) # this is to get the right GLIM arguments
formals(mu.object$d2ldp2, envir=new.env()) <- alist(mu = fv) #
formals(mu.object$G.di, envir=new.env()) <- alist(mu = fv) #
formals(mu.object$valid, envir=new.env()) <- alist(mu = fv) #
## initial values for mu
if(!is.null(mu.start))
{ mu <- if(length(mu.start)>1) mu.start else rep(mu.start,N)}
else (eval(family$mu.initial)) #MS: Friday, March 29, 2002 at 11:27
##---------------------------------------------------------------------------------------
## Set up dispersion-scale submodel:
## sigma.X design matrix
## sigma.offset offset in linear predictor
## sigma.start starting values for sigma (optional)
##---------------------------------------------------------------------------------------
if ("sigma"%in%names(family$parameters))
{
orig.Envir <- attr(mcall$formula, ".Environment") # DS fix for Willem Thursday, March 18, 2010
sigma.fit <- list() # MS Thursday, January 23, 2003 at 14:48
form.sigma <- other.formula(form = sigma.formula)
sigma.terms <- if(missing(data)) terms(form.sigma, specials = .gamlss.sm.list)
else terms(form.sigma, specials = .gamlss.sm.list, data = data)
mcall$formula <- sigma.terms
attr(mcall$formula, ".Environment") <- orig.Envir # DS fix for Willem Thursday, March 18, 2010
sigma.frame <- eval(mcall,sys.parent())
sigma.terms <- attr(sigma.frame, "terms")
sigma.smoothers <- get.smoothers(sigma.terms)
sigma.a <- attributes(sigma.terms)
sigma.X <- model.matrix(sigma.terms, sigma.frame, contrasts)
sigma.offset <- model.extract(sigma.frame, offset)
if(is.null(sigma.offset)) sigma.offset <- rep(0,N)
sigma.object <- get.object("sigma")
formals(sigma.object$dldp, envir=new.env()) <- alist(sigma = fv) #
formals(sigma.object$d2ldp2, envir=new.env()) <- alist(sigma = fv) #
formals(sigma.object$G.di, envir=new.env()) <- alist(sigma = fv) #
formals(sigma.object$valid, envir=new.env()) <- alist(sigma = fv) #
formals(family$d2ldmdd, envir=new.env()) <- alist(sigma=sigma)# ?? I do not think is needed
## initial values for sigma
if(!is.null(sigma.start))
sigma <- if(length(sigma.start)>1) sigma.start else rep(sigma.start,N)
else eval(family$sigma.initial) #
}
##-----------------------------------------------------------------------------------------
## Set up for the 3rd parameter submodel:
## nu.X design matrix
## nu.offset offset in linear predictor
## nu.start starting values for nu (optional)
##-----------------------------------------------------------------------------------------
if ("nu"%in%names(family$parameters))
{
nu.fit <- list() # MS Thursday, January 23, 2003 at 14:48
form.nu <- other.formula(form = nu.formula)
nu.terms <- if(missing(data)) terms(form.nu, specials = .gamlss.sm.list)
else terms(form.nu, specials = .gamlss.sm.list, data = data)
mcall$formula <- nu.terms
attr(mcall$formula, ".Environment") <- orig.Envir # DS fix for Willem Thursday, March 18, 2010
nu.frame <- eval(mcall,sys.parent()) # ms Saturday, April 6, 2002 at 10:23
nu.terms <- attr(nu.frame, "terms")
nu.a <- attributes(nu.terms)
nu.smoothers <- get.smoothers(nu.terms)
nu.X <- model.matrix(form.nu, nu.frame, contrasts)
nu.offset <- model.extract(nu.frame, offset)
if(is.null(nu.offset)) nu.offset <- rep(0,N)
nu.object <- get.object("nu")
formals(nu.object$dldp, envir=new.env()) <- alist(nu = fv) # this is to get the right GLIM argument
formals(nu.object$d2ldp2, envir=new.env()) <- alist(nu = fv) #
formals(nu.object$G.di, envir=new.env()) <- alist(nu = fv) #
formals(nu.object$valid, envir=new.env()) <- alist(nu = fv) #
formals(family$d2ldmdv, envir=new.env()) <- alist(nu = nu)
formals(family$d2ldddv, envir=new.env()) <- alist(nu = nu)
## initial values for nu
if(!is.null(nu.start))
{nu <- if(length(nu.start)>1) nu.start else rep(nu.start,N)}
else { eval(family$nu.initial) } #
}
##-----------------------------------------------------------------------------------------
## Set up for the 4rd parameter submodel:
## tau.X design matrix
## tau.offset offset in linear predictor
## tau.start starting values for tau (optional)
##-----------------------------------------------------------------------------------------
if ("tau"%in%names(family$parameters))
{
tau.fit <- list() # MS Thursday, January 23, 2003 at 14:48
form.tau <- other.formula(form = tau.formula)
tau.terms <- if(missing(data)) terms(form.tau, specials = .gamlss.sm.list)
else terms(form.tau, specials = .gamlss.sm.list, data = data)
mcall$formula <- tau.terms
attr(mcall$formula, ".Environment") <- orig.Envir # DS fix for Willem Thursday, March 18, 2010
tau.frame <- eval(mcall,sys.parent())
tau.terms <- attr(tau.frame, "terms") #
tau.a <- attributes(tau.terms) #
tau.smoothers <- get.smoothers(tau.terms)
tau.X <- model.matrix(form.tau, tau.frame, contrasts)
tau.offset <- model.extract(tau.frame, offset)
if(is.null(tau.offset)) tau.offset <- rep(0,N)
tau.object <- get.object("tau")
formals(tau.object$dldp, envir=new.env()) <- alist(tau = fv) #
formals(tau.object$d2ldp2, envir=new.env()) <- alist(tau = fv) #
formals(tau.object$G.di, envir=new.env()) <- alist(tau = fv) #
formals(tau.object$valid, envir=new.env()) <- alist(tau = fv) #
formals(family$d2ldmdt, envir=new.env()) <- alist(tau = tau)
formals(family$d2ldddt, envir=new.env()) <- alist(tau = tau)
formals(family$d2ldvdt, envir=new.env()) <- alist(tau = tau)
## initial values for tau
if(!is.null(tau.start))
{tau <- if(length(tau.start)>1) tau.start else rep(tau.start,N)}
else { eval(family$tau.initial) } #
}
##-----------------------------------------------------------------------------------------
##-----------------------------------------------------------------------------------------
##=========================================================================================
## Checking whether proper algorithm (RS, CG or mixed)
##=========================================================================================
##-----------------------------------------------------------------------------------------
name.method <- substitute(method)
name.method <- deparse(name.method[1])
list.methods <- c("RS()","CG()","mixed()")
i.method <- pmatch(name.method,list.methods,nomatch=0)
if(!i.method) stop("Method must be RS(), CG() or mixed()")
##-----------------------------------------------------------------------------------------
## fitting the model
fiter <- 0
conv <- eval(substitute(method))
method <- substitute(method)
##-----------------------------------------------------------------------------------------
##-----------------------------------------------------------------------------------------
## Getting the GAMLSS object out
## ----------------------------------------------------------------------------------------
## first the general output
## calculate the Global deviance again
G.dev.incr <- eval(G.dev.expr)
G.dev <- sum(w*G.dev.incr)
out <- list(family = family$family, parameters = names(family$parameters),
call = gamlsscall, y = y, control = control, weights = w,
G.deviance = G.dev, N = N, rqres = family$rqres, iter = fiter,
type = family$type, method = method, contrasts = contrasts)
#, na.action=na.act
out$converged <- conv
out$residuals <- eval(family$rqres)
noObs <- if(all(trunc(w)==w)) sum(w) else N
out$noObs <- noObs
## binomial denominator
if(any(family$family%in%.gamlss.bi.list)) out$bd <- bd
##-----------------------------------------------------------------------------------------
saveParam <- control$save
## Output for mean model: ----------------------------------------------------------------
if ("mu"%in%names(family$parameters))
{
out <- c(out, mu = parameterOut(what="mu", save=saveParam))
}
else
{
out$mu.df <- 0
}
## define now the degrees of freedom for the fit and residuals
out$df.fit <- out$mu.df
out$pen <- out$mu.pen
out$df.residual <- noObs-out$mu.df
## Output for dispersion model: ----------------------------------------------------------
if ("sigma"%in%names(family$parameters))
{
out <- c(out, sigma = parameterOut(what="sigma", save=saveParam) )
out$df.fit <- out$mu.df + out$sigma.df
out$pen <- out$mu.pen + out$sigma.pen
out$df.residual <- noObs-out$mu.df-out$sigma.df
}
## output for nu ------------------------------------------------------------------------
if ("nu"%in%names(family$parameters))
{
out <- c(out, nu = parameterOut(what="nu", save=saveParam) )
out$df.fit <- out$mu.df+out$sigma.df+out$nu.df
out$df.residual <- noObs-out$mu.df-out$sigma.df-out$nu.df
out$pen <- out$mu.pen + out$sigma.pen + out$nu.pen
}
## output for tau -----------------------------------------------------------------------
if ("tau"%in%names(family$parameters))
{
out <- c(out, tau = parameterOut(what="tau", save=saveParam) )
out$df.fit <- out$mu.df+out$sigma.df+out$nu.df+out$tau.df
out$pen <- out$mu.pen + out$sigma.pen + out$nu.pen + out$tau.pen
out$df.residual <- noObs-out$mu.df-out$sigma.df- out$nu.df -out$tau.df
}
##=======================================================================================
out$P.deviance <- out$G.deviance+out$pen # ms Thursday, May 13, 2004
out$aic <- G.dev+2*out$df.fit
out$sbc <- G.dev+log(noObs)*out$df.fit
#MS Thursday, April 22, 2004 at 11:26
# if ((ls(1,pattern="fiter")=="fiter")) rm(fiter, envir = as.environment(1))
#MS Thursday, January 8, 2004 at 17:52
class(out) <- c("gamlss","gam","glm","lm")
out
}
#########################################################################################
## the END of gamlss
#########################################################################################
#########################################################################################
## control gamlss
#########################################################################################
gamlss.control <- function(c.crit = 0.001, n.cyc = 20, mu.step = 1, sigma.step=1,
nu.step=1, tau.step=1, gd.tol = Inf, iter=0, trace = TRUE,
autostep = TRUE, save = TRUE, ...)
{
## Control iteration for GAMLSS
## Mikis Stasinopoulos Monday, March 25, 2002 at 16:17
## last change Thursday, October 12, 2006 save is added
##
if(c.crit <= 0) {
warning("the value of c.crit supplied is zero or negative the default value of 0.001 was used instead")
c.crit <- 0.001}
if(n.cyc < 1) {
warning("the value of no cycles supplied is zero or negative the default value of 20 was used instead")
n.cyc <- 20}
if(iter < 0) {
warning("the value of no iterations supplied is negative the default value of 0 was used instead")
iter <- 0}
if(mu.step > 1 | mu.step < 0) {
warning("the value of mu.step supplied is less than zero or more than one the default value of 1 was used instead")
mu.step <- 1}
if(sigma.step > 1 | sigma.step < 0) {
warning("the value of sigma.step supplied is less than zero or more than one the default value of 1 was used instead")
sigma.step <- 1}
if(nu.step > 1 | nu.step < 0) {
warning("the value of nu.step supplied is less than zero or more than one the default value of 1 was used instead")
nu.step <- 1}
if(tau.step > 1 | tau.step < 0) {
warning("the value of tau.step supplied is less than zero or more than one the default value of 1 was used instead")
tau.step <- 1}
if(gd.tol < 0) {
warning("the value of gd.tol supplied is less than zero the default value of Inf was used instead")
gd.tol <- Inf}
list(c.crit = c.crit, n.cyc = n.cyc, mu.step = mu.step, sigma.step = sigma.step, nu.step=nu.step,
tau.step = tau.step, gd.tol = gd.tol, iter = iter, trace = as.logical(trace)[1],
autostep = as.logical(autostep)[1], save = as.logical(save)[1] )
}
##---------------------------------------------------------------------------------------
glim.control <- function(cc = 0.001, cyc = 50, glm.trace = FALSE, bf.cyc = 30, # step = 1,
bf.tol = 0.001, bf.trace = FALSE, ...)
{
## Control iteration for GLIM
## MS Sunday, February 17, 2002 at 19:18
##
if(cc <= 0) {
warning("the value of cc supplied is zero or negative the default value of 0.001 was used instead")
cc <- 0.001}
if(bf.tol <= 0) {
warning("the value of bf.tol supplied is zero or negative the default value of 0.001 was used instead")
bf.tol <- 0.001}
if(cyc < 1) {
warning("the value of cyc supplied is zero or negative the default value of 20 was used instead")
cyc <- 20}
if(bf.cyc < 1) {
warning("the value of bf.cyc supplied is zero or negative the default value of 30 was used instead")
bf.cyc <- 30}
# if(step > 1 | step < 0) {
# warning("the value of step supplied is less than zero or more than one the default value of 1 was used instead")
# step <- 1}
list(cc = cc, cyc = cyc, glm.trace = as.logical(glm.trace)[1], bf.cyc = bf.cyc, # step = step,
bf.tol = bf.tol, bf.trace = as.logical(bf.trace)[1] )# MS Thursday, January 8, 2004 at 17:53
}
##----------------------------------------------------------------------------------------
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