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# These functions are
# Copyright (C) 2020 S. Orso, University of Geneva
# All rights reserved.
ib.vglm <- function(object, thetastart=NULL, control=list(...), extra_param = FALSE,...){
# controls
control <- do.call("ibControl",control)
# initial estimator:
pi0 <- coef(object)
if(!is.null(thetastart)){
if(is.numeric(thetastart) && length(thetastart) == length(pi0)){
t0 <- thetastart
} else {
stop("`thetastart` must be a numeric vector of the same length as
parameter of interest.", call.=FALSE)
}
} else {
t0 <- pi0
}
# test diff between thetas
p <- p0 <- length(t0)
test_theta <- control$tol + 1
# iterator
k <- 0L
# create an environment for iterative bootstrap
env_ib <- new.env(hash=F)
# prepare data and formula for fit
cl <- getCall(object)
if(length(cl$formula)==1) cl$formula <- get(paste(cl$formula)) # get formula
intercept_only <- cl$formula[[3]] == 1 # check for intercept only models
# alternatively: intercept_only <- object@misc$intercept.only
mf <- model.framevlm(object) # ? problem with mf <- model.frame(object)
mt <- attr(mf, "terms")
if(!intercept_only){
x <- if(!is.empty.model(mt)) model.matrix(mt, mf, attr(mf,"contrasts"))
# x <- model.matrixvlm(object)
# remove intercept from design
# check if model has an intercept
has_intercept <- has.intercept(object)
if(has_intercept){
# remove intercept from design
x <- x[,!grepl("Intercept",colnames(x))]
cl$formula <- quote(y~x)
} else {
cl$formula <- quote(y~x-1)
}
} else {
cl$formula <- quote(y~1)
}
cl$data <- NULL
o <- as.vector(model.offset(mf))
if(!is.null(o)) assign("o",o,env_ib)
# add an offset
if(!is.null(o)) cl$offset <- quote(o)
# FIXME: add support for subset, na.action, start,
# etastart, mustart, contrasts, constraints
n <- nrow(mf)
w <- model.weights(mf)
if(!length(w)){
w <- rep_len(1, n)
} else {
cl$weights <- quote(w)
assign("w",w,env_ib)
}
if(is.null(cl$etastart)) etastart <- NULL
# copy the object
tmp_object <- object
# initial value
diff <- rep(NA_real_, control$maxit)
# Iterative bootstrap algorithm:
while(test_theta > control$tol && k < control$maxit){
# browser()
# update initial estimator
slot(tmp_object, "coefficients") <- t0[1:p0]
sim <- simulation(tmp_object,control)
tmp_pi <- matrix(NA_real_,nrow=p,ncol=control$H)
for(h in seq_len(control$H)){
assign("y",sim[,h],env_ib)
# FIXME: deal with warnings from vglm.fitter
# fit_tmp <- eval(cl,env_ib)
fit_tmp <- tryCatch(error = function(cnd) NULL, {eval(cl,env_ib)})
if(is.null(fit_tmp)) next
tmp_pi[1:p0,h] <- coef(fit_tmp)
}
pi_star <- control$func(tmp_pi)
# update value
delta <- pi0 - pi_star
t1 <- t0 + delta
# test diff between thetas
test_theta <- sum(delta^2)
if(k>0) diff[k] <- test_theta
# initialize test
if(!k) tt_old <- test_theta+1
# Alternative stopping criteria, early stop :
if(control$early_stop){
if(tt_old <= test_theta){
warning("Algorithm stopped because the objective function does not reduce")
break
}
}
# Alternative stopping criteria, "statistically flat progress curve" :
if(k > 10L){
try1 <- diff[k:(k-10)]
try2 <- k:(k-10)
if(var(try1)<=1e-3) break
mod <- lm(try1 ~ try2)
if(summary(mod)$coefficients[2,4] > 0.2) break
}
# update increment
k <- k + 1L
# Print info
if(control$verbose){
cat("Iteration:",k,"Norm between theta_k and theta_(k-1):",test_theta,"\n")
}
# update theta
t0 <- t1
}
# warning for reaching max number of iterations
if(k>=control$maxit) warning("maximum number of iteration reached")
# update vglm object
extra <- slot(object, "extra")
fam <- slot(object, "family")
y <- slot(object, "y")
M <- slot(object,"misc")$M
# w <- c(slot(object, "prior.weights"))
# w <- drop(weights(object, "prior"))
# if(!length(w)==0) w <- rep_len(1,n)
eval(slot(fam,"initialize")) # initialize different parameters (among which M)
eta <- predictvglm(tmp_object)
mu <- slot(fam,"linkinv")(eta, extra)
u <- eval(slot(fam,"deriv"))
W <- eval(slot(fam,"weight"))
U <- vchol(W, M, n, silent = TRUE)
tvfor <- vforsub(U, as.matrix(u), M, n)
res <- vbacksub(U, tvfor, M, n)
if(.hasSlot(fam, "deviance") && !is.null(body(slot(fam,"deviance"))))
tmp_object@criterion$deviance <- slot(fam,"deviance")(mu,y,w,residuals=FALSE,eta,extra)
if(.hasSlot(fam, "loglikelihood") && !is.null(body(slot(fam,"loglikelihood"))))
tmp_object@criterion$loglikelihood <- slot(fam,"loglikelihood")(mu,y,w,residuals=FALSE,eta,extra)
slot(tmp_object, "predictors") <- as.matrix(eta)
slot(tmp_object, "fitted.values") <- as.matrix(mu)
slot(tmp_object, "residuals") <- as.matrix(res)
slot(tmp_object, "call") <- slot(object,"call")
# additional metadata
ib_extra <- list(
iteration = k,
of = sqrt(drop(crossprod(delta))),
estimate = t0,
test_theta = test_theta,
boot = tmp_pi)
new("IbVglm",
object = tmp_object,
ib_extra = ib_extra)
}
#' @rdname ib
#' @details
#' For \link[VGAM]{vglm}, \code{extra_param} is currently not used.
#' Indeed, the philosophy of a vector generalized linear model is to
#' potentially model all parameters of a distribution with a linear predictor.
#' Hence, what would be considered as an extra parameter in \code{\link[stats]{glm}}
#' for instance, may already be captured by the default \code{coefficients}.
#' However, correcting the bias of a \code{coefficients} does not imply
#' that the bias of the parameter of the distribution is corrected
#' (by \href{https://en.wikipedia.org/wiki/Jensen's_inequality}{Jensen's inequality}),
#' so we may use this feature in a future version of the package.
#' Note that we currently only support distributions
#' with a \code{simslot} (see \code{\link[VGAM]{simulate.vlm}}).
#' @example /inst/examples/eg_vglm.R
#' @seealso \code{\link[VGAM]{vglm}}
#' @importFrom VGAM Coef has.intercept model.framevlm predictvglm vbacksub vchol vglm vforsub
#' @importFrom methods slot `slot<-` .hasSlot
#' @importFrom stats model.weights
#' @export
setMethod("ib", className("vglm", "VGAM"),
definition = ib.vglm)
# inspired from VGAM::simulate.vlm
#' @importFrom VGAM familyname
simulation.vglm <- function(object, control=list(...), extra_param = NULL, ...){
control <- do.call("ibControl",control)
fam <- slot(object, "family")
if(is.null(body(slot(fam, "simslot"))))
stop(paste0("simulation not implemented for family ", familyname(object)), call.=FALSE)
set.seed(control$seed)
if(!exists(".Random.seed", envir = .GlobalEnv)) runif(1)
# user-defined simulation method
if(!is.null(control$sim)){
sim <- control$sim(object, control, extra_param, ...)
return(sim)
}
sim <- matrix(slot(fam, "simslot")(object,control$H), ncol = control$H)
if(control$cens) sim <- censoring(sim,control$right,control$left)
if(control$mis) sim <- missing_at_random(sim, control$prop)
if(control$out) sim <- outliers(sim, control$eps, control$G)
sim
}
#' @title Simulation for vector generalized linear model regression
#' @description simulation method for class \linkS4class{IbVglm}
#' @param object an object of class \linkS4class{IbVglm}
#' @param control a \code{list} of parameters for controlling the iterative procedure
#' (see \code{\link{ibControl}}).
#' @param extra_param \code{NULL} by default; extra parameters to pass to simulation.
#' @param ... further arguments
#' @export
setMethod("simulation", signature = className("vglm","VGAM"),
definition = simulation.vglm)
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