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gamlss fits flexible univariate regression models with several continuous and discrete distributions, and types of covariate
effects. The purpose of this function was only to provide, in some cases, starting values
for the simultaneous models in the package, but it has now been made available in the form of a proper function should the user wish to fit
univariate models using the general estimation approach of this package. The distributions implemented here
have been parametrised according to Rigby and Stasinopoulos (2005).
gamlss(formula, data = list(), weights = NULL, subset = NULL,
margin = "N", surv = FALSE, cens = NULL, type.cens = "R", upperB = NULL,
robust = FALSE, rc = 3, lB = NULL, uB = NULL, infl.fac = 1,
rinit = 1, rmax = 100, iterlimsp = 50, tolsp = 1e-07,
gc.l = FALSE, parscale, extra.regI = "t", gev.par = -0.25,
chunk.size = 10000, k.tvc = 0, knots = NULL,
informative = "no", inform.cov = NULL, margin2 = "PH",
fp = FALSE, sp = NULL,
drop.unused.levels = TRUE, siginit = NULL, shinit = NULL,
sp.method = "perf", hrate = NULL, d.lchrate = NULL, d.rchrate = NULL,
d.lchrate.td = NULL, d.rchrate.td = NULL, truncation.time = NULL,
min.dn = 1e-40, min.pr = 1e-16, max.pr = 0.9999999, ygrid.tol = 1e-08)The function returns an object of class gamlss as described in gamlssObject.
List of equations. This should contain one or more equations.
An optional data frame, list or environment containing the variables in the model. If not found in data, the
variables are taken from environment(formula), typically the environment from which gamlss is called.
Optional vector of prior weights to be used in fitting.
Optional vector specifying a subset of observations to be used in the fitting process.
Possible distributions are normal ("N"),
log-normal ("LN"), Gumbel ("GU"), reverse Gumbel ("rGU"), generelised Pareto ("GP"),
generelised Pareto II ("GPII") where the shape parameter is forced to be > -0.5,
generelised Pareto (with orthogonal parametrisation) ("GPo") where the shape parameter is forced to be > -0.5,
discrete generelised Pareto ("DGP"),
discrete generelised Pareto II ("DGPII") where the shape parameter is forced to be positive, discrete generelised Pareto derived
under the scenario in which shape = 0 ("DGP0"), logistic ("LO"), Weibull ("WEI"), inverse Gaussian ("iG"), gamma ("GA"), Dagum ("DAGUM"),
Singh-Maddala ("SM"), beta ("BE"), Fisk ("FISK", also known as log-logistic distribution), Poisson ("PO"), zero truncated
Poisson ("ZTP"), negative binomial - type I ("NBI"), negative
binomial - type II ("NBII"), Poisson inverse Gaussian ("PIG"), generalised extreme value link function ("GEVlink", this
is used for binary responses and is more stable and faster than the R package bgeva).
If TRUE then a survival model is fitted. Here margin can be "PH" (generalised proportional hazards), "PO" (generalised proportional odds),
"probit" (generalised probit).
This is required when surv = TRUE. When type.cens is different from mixed, this variable can be equal to 1 if the event occurred
and 0 otherwise. If type.cens = "mixed" then cens is a mixed factor variable (made up of four possible
categories: I for interval, L for left, R for right, and U for uncensored.
Type of censoring mechanism. This can be "R", "L", "I" or "mixed".
Variable name of right/upper bound when type.cens = "I" or type.cens = "mixed" and interval censoring is present.
If TRUE then the robust version of the model is fitted.
Robust constant.
Bounds for integral in robust case.
Inflation factor for the model degrees of freedom in the approximate AIC. Smoother models can be obtained setting this parameter to a value greater than 1.
Starting trust region radius. The trust region radius is adjusted as the algorithm proceeds.
Maximum allowed trust region radius. This may be set very large. If set small, the algorithm traces a steepest descent path.
A positive integer specifying the maximum number of loops to be performed before the smoothing parameter estimation step is terminated.
Tolerance to use in judging convergence of the algorithm when automatic smoothing parameter estimation is used.
This is relevant when working with big datasets. If TRUE then the garbage collector is called more often than it is
usually done. This keeps the memory footprint down but it will slow down the routine.
The algorithm will operate as if optimizing objfun(x / parscale, ...) where parscale is a scalar. If missing then no
rescaling is done. See the
documentation of trust for more details.
If "t" then regularization as from trust is applied to the information matrix if needed.
If different from "t" then extra regularization is applied via the options "pC" (pivoted Choleski - this
will only work when the information matrix is semi-positive or positive definite) and "sED" (symmetric eigen-decomposition).
GEV link parameter.
This is used for discrete robust models.
Experimental. Only used for tvc ps smoothers when using survival models.
Optional list containing user specified knot values to be used for basis construction.
If "yes" then informative censoring is assumed when using a survival model.
If above is "yes" then a set of informative covariates must be provided.
In the informative survival case, the margin for the censored equation can be different from that of the survival equation.
If TRUE then a fully parametric model with unpenalised regression splines if fitted.
A vector of smoothing parameters can be provided here. Smoothing parameters must be supplied in the order that the smooth terms appear in the model equation(s).
By default unused levels are dropped from factors before fitting. For some smooths involving factor variables this may have to be turned off (only use if you know what you are doing).
For the GP and DGP distributions, initial values for sigma and shape may be provided.
Multiple smoothing automatic parameter selection is perf. efs is an alternative and only sensible option for robust models.
Vector of population hazard rates computed at time of death of each uncensored patient. The length of hrate should be equal to the number of uncensored observations in the dataset. Needed in the context of excess hazard modelling when uncensored observations are present. Note that this includes left truncated uncensored observations as well.
Vector of differences of population cumulative excess hazards computed at the age of the patient when the left
censoring occurred and at the initial age of the patient. The length of d.lchrate should be equal to the number
of left and/or interval censored observations in the dataset. Needed in the context of excess hazard modelling
if left censored and/or interval censored observations are present. In the latter case, d.rchrate also need be provided.
Vector of differences of population cumulative excess hazards computed at the age of the patient when the at the right
interval censoring time and at the initial age of the patient. The length of d.rchrate should be equal to the number
of right censored and/or interval censored observations in the dataset. Needed in the context of excess hazard modelling
if right censored and/or interval censored observations are present. In the latter case, d.lchrate also need be provided.
Vector of differences of population cumulative excess hazards computed at the age of the patient when the left
censoring occurred and at the age of the patient when the truncation occurred. The length of d.lchrate.td should be
equal to the number of left truncated left censored and/or left truncated interval censored observations in
the dataset. Needed in the context of excess hazard modelling if left truncated left censored and/or left truncated
interval censored observations are present. In the latter case, d.rchrate.td also need be provided.
Vector of differences of population cumulative excess hazards computed at the age of the patient when the right
censoring occurred and at the age of the patient when the truncation occurred. The length of d.rchrate.td should be
equal to the number of left truncated right censored and/or left truncated interval censored observations in the dataset. Needed in
the context of excess hazard modelling if left truncated right censored and/or left truncated interval censored
observations are present. In the latter case, d.lchrate.td also need be provided.
Variable name of truncation time.
These values are used to set, depending on the model used for modelling, the minimum and maximum allowed
for the densities and probabilities. These
parameters are employed to avoid potential overflows/underflows in the calculations and the default
values seem to offer a good compromise. Function conv.check() provides some relevant
diagnostic information which can be used, for example, to check whether the lower bounds
of min.dn and min.pr have been reached. So based on this or if the user wishes to do some sensitivity
analysis then this can be easily carried out using these three arguments.
However, the user has to be cautious. For instance, it would not make much sense to choose for min.dn and min.pr
values bigger than the default ones. Bear in mind that the bounds can be reached for ill-defined models. For
certain distributions/models, if convergence failure occurs and the bounds have been reached then the user
can try a sensitivity analysis as mentioned above.
Tolerance used to choose grid of response values for robust discrete models. Values smaller than 1e-160 are not allowed for.
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
Convergence can be checked using conv.check which provides some
information about
the score and information matrix associated with the fitted model. The former should be close to 0 and the latter positive definite.
gamlss() will produce some warnings if there is a convergence issue.
Convergence failure may sometimes occur. This is not necessarily a bad thing as it may indicate specific problems
with a fitted model. In such a situation, the user may use some extra regularisation (see extra.regI) and/or
rescaling (see parscale). However, the user should especially consider
re-specifying/simplifying the model, and/or checking that the chosen distribution fits the response well.
In our experience, we found that convergence failure typically occurs
when the model has been misspecified and/or the sample size is low compared to the complexity of the model.
It is also worth bearing in mind that the use of three parameter distributions requires the data
to be more informative than a situation in which two parameter distributions are used instead.
The underlying algorithm is described in ?gjrm.
There are many continuous/discrete/survival distributions to choose from and we plan to include more options. Get in touch if you are interested in a particular distribution.
The "GEVlink" option is used for binary response additive models and is more stable and faster than the R package bgeva.
This model has been incorporated into this package to take advantage of the richer set of smoother choices, and of the
estimation approach. Details on the model can be found in Calabrese, Marra and Osmetti (2016).
Marra G., Farcomeni A., Radice R. (2021), Link-Based Survival Additive Models under Mixed Censoring to Assess Risks of Hospital-Acquired Infections. Computational Statistics and Data Analysis, 155, 107092.
Marra G., Radice R. (2017), Bivariate Copula Additive Models for Location, Scale and Shape. Computational Statistics and Data Analysis, 112, 99-113.
Rigby R.A., Stasinopoulos D.M. (2005). Generalized additive models for location, scale and shape (with discussion). Journal of the Royal Statistical Society, Series C, 54(3), 507-554.
Calabrese R., Marra G., Osmetti SA (2016), Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model. Journal of the Operational Research Society, 67(4), 604-615.
Marincioni V., Marra G., Altamirano-Medina H. (2018), Development of Predictive Models for the Probabilistic Moisture Risk Assessment of Internal Wall Insulation. Building and Environment, 137, 5257-267.
GJRM-package, gamlssObject, conv.check, summary.gamlss
if (FALSE) {
library(GJRM)
set.seed(0)
n <- 400
x1 <- round(runif(n))
x2 <- runif(n)
x3 <- runif(n)
f1 <- function(x) cos(pi*2*x) + sin(pi*x)
y1 <- -1.55 + 2*x1 + f1(x2) + rnorm(n)
dataSim <- data.frame(y1, x1, x2, x3)
resp.check(y1, "N")
eq.mu <- y1 ~ x1 + s(x2) + s(x3)
eq.s <- ~ s(x3)
fl <- list(eq.mu, eq.s)
out <- gamlss(fl, data = dataSim)
conv.check(out)
post.check(out)
plot(out, eq = 1, scale = 0, pages = 1, seWithMean = TRUE)
plot(out, eq = 2, seWithMean = TRUE)
summary(out)
AIC(out)
BIC(out)
################
# Robust example
################
eq.mu <- y1 ~ x1 + x2 + x3
fl <- list(eq.mu)
out <- gamlss(fl, data = dataSim, margin = "N", robust = TRUE,
rc = 3, lB = -Inf, uB = Inf)
conv.check(out)
summary(out)
rob.const(out, 100)
##
eq.s <- ~ x3
fl <- list(eq.mu, eq.s)
out <- gamlss(fl, data = dataSim, margin = "N", robust = TRUE)
conv.check(out)
summary(out)
##
eq.mu <- y1 ~ x1 + s(x2) + s(x3)
eq.s <- ~ s(x3)
fl <- list(eq.mu, eq.s)
out1 <- gamlss(fl, data = dataSim, margin = "N", robust = TRUE,
sp.method = "efs")
conv.check(out1)
summary(out1)
AIC(out, out1)
plot(out1, eq = 1, all.terms = TRUE, pages = 1, seWithMean = TRUE)
plot(out1, eq = 2, seWithMean = TRUE)
##########################
## GEV link binary example
##########################
# this incorporates the bgeva
# model implemented in the bgeva package
# however this implementation is more general
# stable and efficient
set.seed(0)
n <- 400
x1 <- round(runif(n)); x2 <- runif(n); x3 <- runif(n)
f1 <- function(x) cos(pi*2*x) + sin(pi*x)
f2 <- function(x) x+exp(-30*(x-0.5)^2)
y <- ifelse(-3.55 + 2*x1 + f1(x2) + rnorm(n) > 0, 1, 0)
dataSim <- data.frame(y, x1, x2, x3)
out1 <- gamlss(list(y ~ x1 + x2 + x3), margin = "GEVlink", data = dataSim)
out2 <- gamlss(list(y ~ x1 + s(x2) + s(x3)), margin = "GEVlink", data = dataSim)
conv.check(out1)
conv.check(out2)
summary(out1)
summary(out2)
AIC(out1, out2)
BIC(out1, out2)
plot(out2, eq = 1, all.terms = TRUE, pages = 1, seWithMean = TRUE)
##################
# prediction of Pr
##################
# Calculate eta (that is, X*model.coef)
# For a new data set the argument newdata should be used
eta <- predict(out2, eq = 1, type = "link")
# extract gev tail parameter
gev.par <- out2$gev.par
# multiply gev tail parameter by eta
gevpeta <- gev.par*eta
# establish for which values the model is defined
gevpetaIND <- ifelse(gevpeta < -1, FALSE, TRUE)
gevpeta <- gevpeta[gevpetaIND]
# estimate probabilities
pr <- exp(-(1 + gevpeta)^(-1/gev.par))
###################################
## Flexible survival model examples
###################################
library(GJRM)
########################################
## Simulate proportional hazards data ##
########################################
set.seed(0)
n <- 2000
c <- runif(n, 3, 8)
u <- runif(n, 0, 1)
z1 <- rbinom(n, 1, 0.5)
z2 <- runif(n, 0, 1)
t <- rep(NA, n)
beta_0 <- -0.2357
beta_1 <- 1
f <- function(t, beta_0, beta_1, u, z1, z2){
S_0 <- 0.7 * exp(-0.03*t^1.9) + 0.3*exp(-0.3*t^2.5)
exp(-exp(log(-log(S_0))+beta_0*z1 + beta_1*z2))-u
}
for (i in 1:n){
t[i] <- uniroot(f, c(0, 8), tol = .Machine$double.eps^0.5,
beta_0 = beta_0, beta_1 = beta_1, u = u[i],
z1 = z1[i], z2 = z2[i], extendInt = "yes" )$root
}
delta <- ifelse(t < c, 1, 0)
u <- apply(cbind(t, c), 1, min)
dataSim <- data.frame(u, delta, z1, z2)
1-mean(delta) # average censoring rate
# log(u) helps obtaining smoother hazards
out <- gamlss(list(u ~ s(log(u), bs = "mpi") + z1 + s(z2) ), data = dataSim,
surv = TRUE, margin = "PH", cens = delta)
post.check(out)
summary(out)
AIC(out)
BIC(out)
plot(out, eq = 1, scale = 0, pages = 1)
hazsurv.plot(out, newdata = data.frame(z1 = 0, z2 = 0), shade = TRUE,
n.sim = 1000, baseline = TRUE)
hazsurv.plot(out, type = "hazard", newdata = data.frame(z1 = 0, z2 = 0),
shade = TRUE, n.sim = 1000, baseline = TRUE)
out1 <- gam(u ~ z1 + s(z2), family = cox.ph(),
data = dataSim, weights = delta)
summary(out1)
# estimates of z1 and s(z2) are
# nearly identical between out and out1
# note that the Weibull is implemented as AFT
# as using the PH parametrisation makes
# computation unstable
out2 <- gamlss(list(u ~ z1 + s(z2) ), data = dataSim, surv = TRUE,
margin = "WEI", cens = delta)
#####################################
## Simulate proportional odds data ##
#####################################
set.seed(0)
n <- 2000
c <- runif(n, 4, 8)
u <- runif(n, 0, 1)
z <- rbinom(n, 1, 0.5)
beta_0 <- -1.05
t <- rep(NA, n)
f <- function(t, beta_0, u, z){
S_0 <- 0.7 * exp(-0.03*t^1.9) + 0.3*exp(-0.3*t^2.5)
1/(1 + exp(log((1-S_0)/S_0)+beta_0*z))-u
}
for (i in 1:n){
t[i] <- uniroot(f, c(0, 8), tol = .Machine$double.eps^0.5,
beta_0 = beta_0, u = u[i], z = z[i],
extendInt="yes" )$root
}
delta <- ifelse(t < c,1, 0)
u <- apply(cbind(t, c), 1, min)
dataSim <- data.frame(u, delta, z)
1-mean(delta) # average censoring rate
out <- gamlss(list(u ~ s(log(u), bs = "mpi") + z ), data = dataSim, surv = TRUE,
margin = "PO", cens = delta)
post.check(out)
summary(out)
AIC(out)
BIC(out)
plot(out, eq = 1, scale = 0)
hazsurv.plot(out, newdata = data.frame(z = 0), shade = TRUE, n.sim = 1000,
baseline = TRUE)
hazsurv.plot(out, type = "hazard", newdata = data.frame(z = 0),
shade = TRUE, n.sim = 1000)
#############################
## Mixed censoring example ##
#############################
f1 <- function(t, u, z1, z2, z3, z4, s1, s2){
S_0 <- 0.7 * exp(-0.03*t^1.8) + 0.3*exp(-0.3*t^2.5)
exp( -exp(log(-log(S_0)) + 1.3*z1 + 0.5*z2 + s1(z3) + s2(z4) ) ) - u
}
datagen <- function(n, z1, z2, z3, z4, s1, s2, f1){
u <- runif(n, 0, 1)
t <- rep(NA, n)
for (i in 1:n) t[i] <- uniroot(f1, c(0, 100), tol = .Machine$double.eps^0.5,
u = u[i], s1 = s1, s2 = s2, z1 = z1[i], z2 = z2[i],
z3 = z3[i], z4 = z4[i], extendInt = "yes")$root
c1 <- runif(n, 0, 2)
c2 <- c1 + runif(n, 0, 6)
df <- data.frame(u1 = t, u2 = t, cens = character(n), stringsAsFactors = FALSE)
for (i in 1:n){
if(t[i] <= c1[i]) {
df[i, 1] <- c1[i]
df[i, 2] <- NA
df[i, 3] <- "L"
}else if(c1[i] < t[i] && t[i] <= c2[i]){
df[i, 1] <- c1[i]
df[i, 2] <- c2[i]
df[i, 3] <- "I"
}else if(t[i] > c2[i]){
df[i, 1] <- c2[i]
df[i, 2] <- NA
df[i, 3] <- "R"}
}
uncens <- (df[, 3] %in% c("L", "I")) + (rbinom(n, 1, 0.2) == 1) == 2
df[uncens, 1] <- t[uncens]
df[uncens, 2] <- NA
df[uncens, 3] <- "U"
dataSim <- data.frame(u1 = df$u1, u2 = df$u2, cens = as.factor(df$cens), z1, z2, z3, z4, t)
dataSim
}
set.seed(0)
n <- 1000
SigmaC <- matrix(0.5, 4, 4); diag(SigmaC) <- 1
cov <- rMVN(n, rep(0,4), SigmaC)
cov <- pnorm(cov)
z1 <- round(cov[, 1])
z2 <- round(cov[, 2])
z3 <- cov[, 3]
z4 <- cov[, 4]
s1 <- function(x) -0.075*exp(3.2 * x)
s2 <- function(x) sin(2*pi*x)
eq1 <- u1 ~ s(log(u1), bs = "mpi") + z1 + z2 + s(z3) + s(z4)
dataSim <- datagen(n, z1, z2, z3, z4, s1, s2, f1)
out <- gamlss(list(eq1), data = dataSim, surv = TRUE, margin = "PH",
cens = cens, type.cen = "mixed", upperB = "u2")
conv.check(out)
summary(out)
plot(out, eq = 1, scale = 0, pages = 1)
ndf <- data.frame(z1 = 1, z2 = 0, z3 = 0.2, z4 = 0.5)
hazsurv.plot(out, eq = 1, newdata = ndf, type = "surv")
hazsurv.plot(out, eq = 1, newdata = ndf, type = "hazard", n.sim = 1000)
}
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