#' @title Gamma Pseudo Maximum Likelihood, GPML
#'
#' @description \code{GPML} estimates gravity models in their
#' multiplicative form via Gamma Pseudo Maximum Likelihood.
#'
#' @details \code{GPML} is an estimation method for gravity models
#' belonging to generalized linear models.
#' It is estimated via \code{\link[glm2]{glm2}} using the gamma distribution and a log-link.
#' To execute the function a square gravity dataset with all pairs of
#' countries, ISO-codes for the country of origin and destination, a measure of
#' distance between the bilateral partners as well as all
#' information that should be considered as dependent an independent
#' variables is needed.
#' Make sure the ISO-codes are of type "character".
#' Missing bilateral flows as well as incomplete rows should be
#' excluded from the dataset.
#' Flows equal to zero have to be excluded as the gamma distribution is used.
#' For similar functions, utilizing the multiplicative form via the log-link,
#' but different distributions, see \code{\link[gravity]{PPML}}, \code{\link[gravity]{NLS}}, and \code{\link[gravity]{NBPML}}.
#'
#' \code{GPML} estimation can be used for both, cross-sectional as well as
#' panel data.
#' It is up to the user to ensure that the functions can be applied
#' to panel data.
#' Depending on the panel dataset and the variables -
#' specifically the type of fixed effects -
#' included in the model, it may easily occur that the model is not computable.
#' Also, note that by including bilateral fixed effects such as country-pair
#' effects, the coefficients of time-invariant observables such as distance
#' can no longer be estimated.
#' Depending on the specific model, the code of the
#' respective function may has to be changed in order to exclude the distance
#' variable from the estimation.
#' At the very least, the user should take special
#' care with respect to the meaning of the estimated coefficients and variances
#' as well as the decision about which effects to include in the estimation.
#' When using panel data, the parameter and variance estimation of the models
#' may have to be changed accordingly.
#' For a comprehensive overview of gravity models for panel data
#' see Egger and Pfaffermayr (2003), Gomez-Herrera (2013) and Head, Mayer and
#' Ries (2010) as well as the references therein.
#'
#' @param y name (type: character) of the dependent variable in the dataset
#' \code{data}, e.g. trade flows.
#'
#' @param dist name (type: character) of the distance variable in the dataset
#' \code{data} containing a measure of distance between all pairs of bilateral
#' partners. It is logged automatically when the function is executed.
#'
#' @param x vector of names (type: character) of those bilateral variables in
#' the dataset \code{data} that should be taken as the independent variables
#' in the estimation. If an independent variable is a dummy variable,
#' it should be of type numeric (0/1) in the dataset. If an independent variable
#' is defined as a ratio, it should be logged.
#' Unilateral variables such as country dummies or incomes can be added.
#' If unilateral metric variables such as GDPs should be used as independent
#' variables, those variables have to be logged first and the
#' logged variable can be used in \code{x}.
#' Interaction terms can be added.
#'
#' @param vce_robust robust (type: logic) determines whether a robust
#' variance-covariance matrix should be used. The default is set to \code{TRUE}.
#'
#' @param data name of the dataset to be used (type: character).
#' To estimate gravity equations, a square gravity dataset including bilateral
#' flows defined by the argument \code{y}, ISO-codes of type character
#' (called \code{iso_o} for the country of origin and \code{iso_d} for the
#' destination country), a distance measure defined by the argument \code{dist}
#' and other potential influences given as a vector in \code{x} are required.
#' All dummy variables should be of type numeric (0/1). Missing trade flows as
#' well as incomplete rows should be excluded from the dataset.
#' Flows equal to zero have to be excluded as the gamma distribution
#' is used.
#' When using panel data, a variable for the time may be included in the
#' dataset. Note that the variable for the time dimension should be of
#' type: factor. See the references for more information on panel data.
#'
#' @param ... additional arguments to be passed to functions used by
#' \code{GPML}.
#'
#' @references
#' For more information on the estimation of gravity equations via Gamma
#' Pseudo maximum Likelihood see
#'
#' Santos-Silva, J. M. C. and Tenreyro, S. (2006) <DOI:10.1162/rest.88.4.641>
#'
#' For more information on gravity models, theoretical foundations and
#' estimation methods in general see
#'
#' Anderson, J. E. (1979) <DOI:10.12691/wjssh-2-2-5>
#'
#' Anderson, J. E. (2010) <DOI:10.3386/w16576>
#'
#' Anderson, J. E. and van Wincoop, E. (2003) <DOI:10.3386/w8079>
#'
#' Baier, S. L. and Bergstrand, J. H. (2009) <DOI:10.1016/j.jinteco.2008.10.004>
#'
#' Baier, S. L. and Bergstrand, J. H. (2010) in Van Bergeijk, P. A., & Brakman, S. (Eds.) (2010) chapter 4 <DOI:10.1111/j.1467-9396.2011.01000.x>
#'
#' Head, K., Mayer, T., & Ries, J. (2010) <DOI:10.1016/j.jinteco.2010.01.002>
#'
#' Head, K. and Mayer, T. (2014) <DOI:10.1016/B978-0-444-54314-1.00003-3>
#'
#' and the citations therein.
#'
#'
#' See \href{https://sites.google.com/site/hiegravity/}{Gravity Equations: Workhorse, Toolkit, and Cookbook} for gravity datasets and Stata code for estimating gravity models.
#'
#'
#' For estimating gravity equations using panel data see
#'
#' Egger, P., & Pfaffermayr, M. (2003) <DOI:10.1007/s001810200146>
#'
#' Gomez-Herrera, E. (2013) <DOI:10.1007/s00181-012-0576-2>
#'
#' and the references therein.
#'
#' @examples
#' \dontrun{
#' data(Gravity_no_zeros)
#'
#' Gravity_no_zeros$lgdp_o <- log(Gravity_no_zeros$gdp_o)
#' Gravity_no_zeros$lgdp_d <- log(Gravity_no_zeros$gdp_d)
#'
#' GPML(y="flow", dist="distw", x=c("rta", "lgdp_o", "lgdp_d"),
#' vce_robust=TRUE, data=Gravity_no_zeros)
#'
#' GPML(y="flow", dist="distw", x=c("rta", "iso_o", "iso_d"),
#' vce_robust=TRUE, data=Gravity_no_zeros)
#' }
#'
#' \dontshow{
#' # examples for CRAN checks:
#' # executable in < 5 sec together with the examples above
#' # not shown to users
#'
#' data(Gravity_no_zeros)
#' # choose exemplarily 10 biggest countries for check data
#' countries_chosen <- names(sort(table(Gravity_no_zeros$iso_o), decreasing = TRUE)[1:10])
#' grav_small <- Gravity_no_zeros[Gravity_no_zeros$iso_o %in% countries_chosen,]
#' GPML(y="flow", dist="distw", x=c("rta", "iso_o", "iso_d"), vce_robust=TRUE, data=grav_small)
#' }
#'
#' @return
#' The function returns the summary of the estimated gravity model similar to a
#' \code{\link[stats]{glm}}-object.
#'
#' @seealso \code{\link[glm2]{glm2}}, \code{\link[lmtest]{coeftest}},
#' \code{\link[sandwich]{vcovHC}}
#'
#'
#' @export
#'
GPML <- function(y, dist, x, vce_robust=TRUE, data, ...){
if(!is.data.frame(data)) stop("'data' must be a 'data.frame'")
if((vce_robust %in% c(TRUE, FALSE)) == FALSE) stop("'vce_robust' has to be either 'TRUE' or 'FALSE'")
if(!is.character(y) | !y%in%colnames(data) | length(y)!=1) stop("'y' must be a character of length 1 and a colname of 'data'")
if(!is.character(dist) | !dist%in%colnames(data) | length(dist)!=1) stop("'dist' must be a character of length 1 and a colname of 'data'")
if(!is.character(x) | !all(x%in%colnames(data))) stop("'x' must be a character vector and all x's have to be colnames of 'data'")
# Transforming data, logging flows, distances --------------------------------
d <- data
d$dist_log <- (log(d[dist][,1]))
d$y <- d[y][,1]
# Model ----------------------------------------------------------------------
vars <- paste(c("dist_log", x), collapse=" + ")
form <- paste("y", "~",vars)
form2 <- stats::as.formula(form)
model.GPML <- glm2::glm2(form2, data = d, family = stats::Gamma(link = "log"), control = list(maxit = 200, trace = FALSE)) # maxit default = 25
model.GPML.robust <- lmtest::coeftest(model.GPML, vcov=sandwich::vcovHC(model.GPML, "HC1"))
# Return ---------------------------------------------------------------------
if(vce_robust == TRUE){
summary.GPML.1 <- .robustsummary.lm(model.GPML, robust=TRUE)
summary.GPML.1$coefficients <- model.GPML.robust[1:length(rownames(model.GPML.robust)),]
return.object.1 <- summary.GPML.1
return.object.1$call <- form2
return.object.1$r.squared <- NULL
return.object.1$adj.r.squared <- NULL
return.object.1$fstatistic <- NULL
return(return.object.1)
}
if(vce_robust == FALSE){
return.object.1 <- summary(model.GPML)
return.object.1$call <- form2
return(return.object.1)}
}
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