Nothing
R/confint_methods.R
In forestinventory: Design-Based Global and Small-Area Estimations for Multiphase Forest Inventories
Defines functions
confint.threephase
confint.twophase
confint.onephase
Documented in
confint.onephase
confint.threephase
confint.twophase
#' Calculates Confidence Intervals for Global and Small-Area Estimations
#'
#' @param object object of class \code{onephase}, \code{twophase} or \code{threephase},
#' containing estimation results of the respective estimation method.
#' @param parm ignored.
#' @param level the confidence level required.
#' @param ... additional arguments, so far ignored.
#' @param adjust.method correction method to obtain simultaneous confidence intervals
#' for a set of estimates (thus restricted to objects of class \code{"onephase"},
#' \code{c("smallarea", "twophase")} and \code{c("smallarea", "threephase")}).
#' Available correction methods are \code{c("none","bonferroni")}. Defaults to \code{"none"}.
#'
#' @details
#'
#' Depending on the estimation method specified, \code{confint()} computes confidence intervals as follows:
#'
#' \code{\link{onephase}}:
#'
#' Two-sided confidence intervals are computed based on the t-distribution with \code{n2 - 1} \emph{degrees of freedom},
#' where \code{n2} is the number of terrestrial data in the respective inventory domain.
#'
#' \code{\link{twophase}}:
#'
#' The calculation of the two-sided confidence intervals for \emph{global} twophase estimates
#' (objects of class \code{global}) are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2 - p} \emph{degrees of freedom}, where \code{p} is the number of parameters used in
#' the regression model, and \code{n2} is the number of terrestrial observations (i.e. \emph{local densities})
#' in the inventory domain.
#'
#' The calculation of the two-sided confidence intervals for \emph{smallarea} twophase estimates
#' (objects of class \code{smallarea}) are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2G - 1} \emph{degrees of freedom}, where \code{n2G} is the number of
#' terrestrial observations (i.e. \emph{local densities}) in the smallarea.
#'
#' \code{\link{threephase}}:
#'
#' The calculation of the two-sided confidence intervals for \emph{global} threephase estimates
#' (objects of class \code{global}) are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2 - p} \emph{degrees of freedom}, where \code{p} is the number of parameters used in
#' the \strong{full} regression model, and \code{n2} is the number of terrestrial observations
#' (i.e. \emph{local densities}) in the inventory domain (note: in notation used here n0, n1 and n2
#' correspond to the zero, first and second phase sample sizes respectively).
#'
#' The calculation of the two-sided confidence intervals for \emph{smallarea} theephase estimates
#' (objects of class \code{smallarea}) are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2G - 1} \emph{degrees of freedom}, where \code{n2G} is the number of
#' terrestrial observations (i.e. \emph{local densities}) in the smallarea.
#'
#'@return \code{confint} returns a list of the following 3 components:
#'
#' \item{ci}{a \code{data.frame} containing the columns:
#' \itemize{
#' \item \code{area} the domain, i.e. small area
#' \item \code{ci_lower_ext} the lower confidence limit based on the external variance
#' \item \code{ci_upper_ext} the upper confidence limit based on the external variance
#' \item \code{ci_lower_g} the lower confidence limit based on the g-weight variance
#' \item \code{ci_upper_g} the upper confidence limit based on the g-weight variance
#' }}
#' \item{level}{the applied confidence level}
#' \item{adjust.method}{the adjustment method applied to retrieve simultaneous confidence intervals}
#'
#'
#'
#' @note
#' In the special case of \emph{synthetic} smallarea estimations, the two-sided confidence intervals
#' are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2 - p} \emph{degrees of freedom}, i.e. based on the global sample size.
#'
#' The confidence intervals for \emph{synthetic} smallarea estimations do not account for the potential
#' bias of a linear model that was fit in a large forest area and applied to a small area. Thus,
#' the coverage rates for confidence intervals produced by synthetic estimators may be less than
#' the nominal level of confidence.
#'
#' In case of cluster-sampling, \code{n2G} is the number of terrestrial clusters
#' (a cluster constitutes the sample unit). This is automatically considered by \code{confint}.
#'
#' The adjustment methods passed to \code{adjust.method} are designed to achieve
#' \emph{simultaneous} confidence intervals by correcting the confidence level given by \code{level}.
#' The use of this option is recommended if a set of estimates contained in a \code{onephase}- or
#' \code{smallarea}-object should be compared by their confidence intervals. It ensures that the
#' percentage of confidence intervals containing the true value will correspond to
#' the nominal confidence level.
#'
#'
#' @references
#'
#' Hill, A., Massey, A. F. (2021). \emph{The R Package forestinventory: Design-Based Global and Small
#' Area Estimations for Multiphase Forest Inventories.} Journal of Statistical Software, 97(4), 1-40.
#'
#' Mandallaz, D. (2013). \emph{Design-based properties of some small-area estimators in forest inventory
#' with two-phase sampling.} Canadian Journal of Forest Research, 43(5), 441-449.
#'
#' Mandallaz, D., Breschan, J., & Hill, A. (2013). \emph{New regression estimators in forest inventories
#' with two-phase sampling and partially exhaustive information: a design-based monte carlo approach
#' with applications to small-area estimation.} Canadian Journal of Forest Research,
#' 43(11), 1023-1031.
#'
#' Mandallaz, D. (2013). \emph{A three-phase sampling extension of the generalized regression estimator
#' with partially exhaustive information.} Canadian Journal of Forest Research,
#' 44(4), 383-388.
#'
#' Benjamini, Y., and Hochberg, Y. (1995). \emph{Controlling the false discovery rate:
#' a practical and powerful approach to multiple testing.}
#' Journal of the Royal Statistical Society Series B 57, 289-300.
#'
#' @example examples/example_confint.R
#' @import methods
#' @name confint
NULL
#>
# -----------------------------------------------------------------------------#
# -----------------------------------------------------------------------------#
#' @rdname confint
#' @export
confint.onephase<- function(object, parm, level = 0.95, adjust.method="none",...){
# confint-method for one-phase outputs:
orig.level<- level
# ----------#
# adjust confidence level if required:
if(adjust.method %in% c("none", "bonferroni")){
# bonferroni-correction:
if (adjust.method == "bonferroni"){
if(nrow(object$estimation)==1){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
} else {
level<- 1 - ((1 - level) / nrow(object$estimation))
}
}
} else {
stop("invalid argument for 'adjust.method'")
}
# ----------#
# calculating the quantile of the t-distribution with n2 - 1 df:
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = object$estimation$n2 - 1))
# calculating the lower and upper confidence intervals based on the design-based variance:
ci_lower_op<- object$estimation$estimate - (t.val * sqrt(object$estimation$variance))
ci_upper_op<- object$estimation$estimate + (t.val * sqrt(object$estimation$variance))
if ("area" %in% names(object$estimation)){
ci<- list(ci=data.frame(area=object$estimation$area, estimate=object$estimation$estimate, ci_lower_op, ci_upper_op), level=orig.level, adjust.method=adjust.method)
} else {
ci<- list(ci=data.frame(estimate=object$estimation$estimate, ci_lower_op, ci_upper_op), level=orig.level, adjust.method=adjust.method)
}
class(ci)<- c("confint.global", "onephase")
return(ci)
} # end of confint.onephase
# -----------------------------------------------------------------------------#
# -----------------------------------------------------------------------------#
#' @rdname confint
#' @export
confint.twophase<- function(object, parm, level = 0.95, adjust.method="none",...){
# confint-method for for twophase estimations:
orig.level<- level
# -------------------------------------------#
# confidence-intervals for twophase-smallarea:
if(is(object,"smallarea") & inherits(object, "twophase")){ # if class(object) is c("smallarea", "twophase")
# ----------#
# adjust confidence level if required:
if(adjust.method %in% c("none", "bonferroni")){
# bonferroni-correction:
if (adjust.method == "bonferroni"){
if(nrow(object$estimation)==1){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
} else {
level<- 1 - ((1 - level) / nrow(object$estimation))
}
}
} else {
stop("invalid argument for 'adjust.method'")
}
# ----------#
# calulating the quantile of the t-distribution:
# if synthetic-estimation was applied, the df's are calculated as n2 - # parameters of regression model:
if(object$input$method %in% c("synth", "psynth")){
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = unique(object$estimation[["n2"]]) - length(all.vars(object$input$formula)[-1])))
# if non-synthetic-estimation was applied, the df's are calculated as n2G - 1:
} else {
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = object$estimation$n2G - 1))
}
#-----------#
# calculating the lower and upper confidence intervals based on the external variance:
ci_lower_g<- object$estimation$estimate - (t.val * sqrt(object$estimation$g_variance))
ci_upper_g<- object$estimation$estimate + (t.val * sqrt(object$estimation$g_variance))
# calculating the lower and upper confidence intervals based on the design-based variance:
ci_lower_ext<- object$estimation$estimate - (t.val * sqrt(object$estimation$ext_variance))
ci_upper_ext<- object$estimation$estimate + (t.val * sqrt(object$estimation$ext_variance))
ci<- list(ci=data.frame(area=object$estimation$area, estimate=object$estimation$estimate, ci_lower_ext, ci_upper_ext, ci_lower_g, ci_upper_g),
level=orig.level, adjust.method=adjust.method)
class(ci)<- c("confint.smallarea", "twophase")
return(ci)
} # end of smallarea-CI
# -------------------------------------------#
# confidence-intervals for twophase-global:
if(is(object, "global") & inherits(object, "twophase")){ # if class(object) is c("global", "twophase")
if(adjust.method != "none"){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
}
n2<- ifelse(object$input$cluster, object$samplesizes$n2_clust, object$samplesizes$n2)
t.val<- qt(p = 1-( (1-level)/2 ), df = n2 - length(all.vars(object$input$formula)[-1]))
ci_lower_g<- object$estimation[["estimate"]] - (t.val * sqrt(object$estimation[["g_variance"]]))
ci_upper_g<- object$estimation[["estimate"]] + (t.val * sqrt(object$estimation[["g_variance"]]))
ci_lower_ext<- object$estimation[["estimate"]] - (t.val * sqrt(object$estimation[["ext_variance"]]))
ci_upper_ext<- object$estimation[["estimate"]] + (t.val * sqrt(object$estimation[["ext_variance"]]))
ci<- list(ci=data.frame(estimate=object$estimation[["estimate"]], ci_lower_ext, ci_upper_ext, ci_lower_g, ci_upper_g),
level=orig.level, adjust.method=adjust.method)
class(ci)<- c("confint.global", "twophase")
return(ci)
}# end of global-CI
} ## end of confint.twophase
# -----------------------------------------------------------------------------#
# -----------------------------------------------------------------------------#
#' @rdname confint
#' @export
confint.threephase<- function(object, parm, level = 0.95, adjust.method="none", ...){
# confint-method for for threephase estimations:
orig.level<- level
# --------------------------------#
# confint for threephase-smallarea:
if(is(object, "smallarea") & inherits(object, "threephase")){ # if class(object) is c("smallarea", "threephase")
#-----------#
# adjust confidence level if required:
if(adjust.method %in% c("none", "bonferroni")){
# bonferroni-correction:
if (adjust.method == "bonferroni"){
if(nrow(object$estimation)==1){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
} else {
level<- 1 - ((1 - level) / nrow(object$estimation))
}
}
} else {
stop("invalid argument for 'adjust.method'")
}
#-----------#
# calulating the quantile of the t-distribution:
# if synthetic-estimation was applied, the df's are calculated as n2 - # parameters of regression model:
if(object$input$method %in% c("synth", "psynth")){
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = unique(object$estimation[["n2"]]) - length(all.vars(object$input$formula.s1)[-1])))
# if non-synthetic-estimation was applied, the df's are calculated as n2G - 1:
} else {
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = object$estimation$n2G - 1))
}
#-----------#
# calculating the lower and upper confidence intervals based on the external variance:
ci_lower_g<- object$estimation$estimate - (t.val * sqrt(object$estimation$g_variance))
ci_upper_g<- object$estimation$estimate + (t.val * sqrt(object$estimation$g_variance))
# calculating the lower and upper confidence intervals based on the design-based variance:
ci_lower_ext<- object$estimation$estimate - (t.val * sqrt(object$estimation$ext_variance))
ci_upper_ext<- object$estimation$estimate + (t.val * sqrt(object$estimation$ext_variance))
ci<- list(ci=data.frame(area=object$estimation$area, estimate=object$estimation$estimate, ci_lower_ext, ci_upper_ext, ci_lower_g, ci_upper_g),
level=orig.level, adjust.method=adjust.method)
class(ci)<- c("confint.smallarea", "threephase")
return(ci)
} # end of smallarea-CI
# --------------------------------#
# summary for threephase-global:
if(is(object, "global") & inherits(object, "threephase")){ # if class(object) is c("global", "threephase")
if(adjust.method != "none"){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
}
n2<- ifelse(object$input$cluster, object$samplesizes$n2_clust, object$samplesizes$n2)
t.val<- qt(p = 1-( (1-level)/2 ), df = n2 - length(all.vars(object$input$formula.s1)[-1])) # that's however not recommended by Daniel,
# see Article Crfr, Threephase, page 387
ci_lower_g<- object$estimation[["estimate"]] - (t.val * sqrt(object$estimation[["g_variance"]]))
ci_upper_g<- object$estimation[["estimate"]] + (t.val * sqrt(object$estimation[["g_variance"]]))
ci_lower_ext<- object$estimation[["estimate"]] - (t.val * sqrt(object$estimation[["ext_variance"]]))
ci_upper_ext<- object$estimation[["estimate"]] + (t.val * sqrt(object$estimation[["ext_variance"]]))
ci<- list(ci=data.frame(estimate=object$estimation[["estimate"]], ci_lower_ext, ci_upper_ext, ci_lower_g, ci_upper_g),
level=orig.level,adjust.method=adjust.method)
class(ci)<- c("confint.global", "threepase")
return(ci)
}# end of global-CI
}## end of confint.threephase
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Defines functions confint.threephase confint.twophase confint.onephase
Documented in confint.onephase confint.threephase confint.twophase
#' Calculates Confidence Intervals for Global and Small-Area Estimations
#'
#' @param object object of class \code{onephase}, \code{twophase} or \code{threephase},
#' containing estimation results of the respective estimation method.
#' @param parm ignored.
#' @param level the confidence level required.
#' @param ... additional arguments, so far ignored.
#' @param adjust.method correction method to obtain simultaneous confidence intervals
#' for a set of estimates (thus restricted to objects of class \code{"onephase"},
#' \code{c("smallarea", "twophase")} and \code{c("smallarea", "threephase")}).
#' Available correction methods are \code{c("none","bonferroni")}. Defaults to \code{"none"}.
#'
#' @details
#'
#' Depending on the estimation method specified, \code{confint()} computes confidence intervals as follows:
#'
#' \code{\link{onephase}}:
#'
#' Two-sided confidence intervals are computed based on the t-distribution with \code{n2 - 1} \emph{degrees of freedom},
#' where \code{n2} is the number of terrestrial data in the respective inventory domain.
#'
#' \code{\link{twophase}}:
#'
#' The calculation of the two-sided confidence intervals for \emph{global} twophase estimates
#' (objects of class \code{global}) are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2 - p} \emph{degrees of freedom}, where \code{p} is the number of parameters used in
#' the regression model, and \code{n2} is the number of terrestrial observations (i.e. \emph{local densities})
#' in the inventory domain.
#'
#' The calculation of the two-sided confidence intervals for \emph{smallarea} twophase estimates
#' (objects of class \code{smallarea}) are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2G - 1} \emph{degrees of freedom}, where \code{n2G} is the number of
#' terrestrial observations (i.e. \emph{local densities}) in the smallarea.
#'
#' \code{\link{threephase}}:
#'
#' The calculation of the two-sided confidence intervals for \emph{global} threephase estimates
#' (objects of class \code{global}) are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2 - p} \emph{degrees of freedom}, where \code{p} is the number of parameters used in
#' the \strong{full} regression model, and \code{n2} is the number of terrestrial observations
#' (i.e. \emph{local densities}) in the inventory domain (note: in notation used here n0, n1 and n2
#' correspond to the zero, first and second phase sample sizes respectively).
#'
#' The calculation of the two-sided confidence intervals for \emph{smallarea} theephase estimates
#' (objects of class \code{smallarea}) are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2G - 1} \emph{degrees of freedom}, where \code{n2G} is the number of
#' terrestrial observations (i.e. \emph{local densities}) in the smallarea.
#'
#'@return \code{confint} returns a list of the following 3 components:
#'
#' \item{ci}{a \code{data.frame} containing the columns:
#' \itemize{
#' \item \code{area} the domain, i.e. small area
#' \item \code{ci_lower_ext} the lower confidence limit based on the external variance
#' \item \code{ci_upper_ext} the upper confidence limit based on the external variance
#' \item \code{ci_lower_g} the lower confidence limit based on the g-weight variance
#' \item \code{ci_upper_g} the upper confidence limit based on the g-weight variance
#' }}
#' \item{level}{the applied confidence level}
#' \item{adjust.method}{the adjustment method applied to retrieve simultaneous confidence intervals}
#'
#'
#'
#' @note
#' In the special case of \emph{synthetic} smallarea estimations, the two-sided confidence intervals
#' are calculated based on the quantiles of the \emph{t}-distribution
#' with \code{n2 - p} \emph{degrees of freedom}, i.e. based on the global sample size.
#'
#' The confidence intervals for \emph{synthetic} smallarea estimations do not account for the potential
#' bias of a linear model that was fit in a large forest area and applied to a small area. Thus,
#' the coverage rates for confidence intervals produced by synthetic estimators may be less than
#' the nominal level of confidence.
#'
#' In case of cluster-sampling, \code{n2G} is the number of terrestrial clusters
#' (a cluster constitutes the sample unit). This is automatically considered by \code{confint}.
#'
#' The adjustment methods passed to \code{adjust.method} are designed to achieve
#' \emph{simultaneous} confidence intervals by correcting the confidence level given by \code{level}.
#' The use of this option is recommended if a set of estimates contained in a \code{onephase}- or
#' \code{smallarea}-object should be compared by their confidence intervals. It ensures that the
#' percentage of confidence intervals containing the true value will correspond to
#' the nominal confidence level.
#'
#'
#' @references
#'
#' Hill, A., Massey, A. F. (2021). \emph{The R Package forestinventory: Design-Based Global and Small
#' Area Estimations for Multiphase Forest Inventories.} Journal of Statistical Software, 97(4), 1-40.
#'
#' Mandallaz, D. (2013). \emph{Design-based properties of some small-area estimators in forest inventory
#' with two-phase sampling.} Canadian Journal of Forest Research, 43(5), 441-449.
#'
#' Mandallaz, D., Breschan, J., & Hill, A. (2013). \emph{New regression estimators in forest inventories
#' with two-phase sampling and partially exhaustive information: a design-based monte carlo approach
#' with applications to small-area estimation.} Canadian Journal of Forest Research,
#' 43(11), 1023-1031.
#'
#' Mandallaz, D. (2013). \emph{A three-phase sampling extension of the generalized regression estimator
#' with partially exhaustive information.} Canadian Journal of Forest Research,
#' 44(4), 383-388.
#'
#' Benjamini, Y., and Hochberg, Y. (1995). \emph{Controlling the false discovery rate:
#' a practical and powerful approach to multiple testing.}
#' Journal of the Royal Statistical Society Series B 57, 289-300.
#'
#' @example examples/example_confint.R
#' @import methods
#' @name confint
NULL
#>
# -----------------------------------------------------------------------------#
# -----------------------------------------------------------------------------#
#' @rdname confint
#' @export
confint.onephase<- function(object, parm, level = 0.95, adjust.method="none",...){
# confint-method for one-phase outputs:
orig.level<- level
# ----------#
# adjust confidence level if required:
if(adjust.method %in% c("none", "bonferroni")){
# bonferroni-correction:
if (adjust.method == "bonferroni"){
if(nrow(object$estimation)==1){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
} else {
level<- 1 - ((1 - level) / nrow(object$estimation))
}
}
} else {
stop("invalid argument for 'adjust.method'")
}
# ----------#
# calculating the quantile of the t-distribution with n2 - 1 df:
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = object$estimation$n2 - 1))
# calculating the lower and upper confidence intervals based on the design-based variance:
ci_lower_op<- object$estimation$estimate - (t.val * sqrt(object$estimation$variance))
ci_upper_op<- object$estimation$estimate + (t.val * sqrt(object$estimation$variance))
if ("area" %in% names(object$estimation)){
ci<- list(ci=data.frame(area=object$estimation$area, estimate=object$estimation$estimate, ci_lower_op, ci_upper_op), level=orig.level, adjust.method=adjust.method)
} else {
ci<- list(ci=data.frame(estimate=object$estimation$estimate, ci_lower_op, ci_upper_op), level=orig.level, adjust.method=adjust.method)
}
class(ci)<- c("confint.global", "onephase")
return(ci)
} # end of confint.onephase
# -----------------------------------------------------------------------------#
# -----------------------------------------------------------------------------#
#' @rdname confint
#' @export
confint.twophase<- function(object, parm, level = 0.95, adjust.method="none",...){
# confint-method for for twophase estimations:
orig.level<- level
# -------------------------------------------#
# confidence-intervals for twophase-smallarea:
if(is(object,"smallarea") & inherits(object, "twophase")){ # if class(object) is c("smallarea", "twophase")
# ----------#
# adjust confidence level if required:
if(adjust.method %in% c("none", "bonferroni")){
# bonferroni-correction:
if (adjust.method == "bonferroni"){
if(nrow(object$estimation)==1){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
} else {
level<- 1 - ((1 - level) / nrow(object$estimation))
}
}
} else {
stop("invalid argument for 'adjust.method'")
}
# ----------#
# calulating the quantile of the t-distribution:
# if synthetic-estimation was applied, the df's are calculated as n2 - # parameters of regression model:
if(object$input$method %in% c("synth", "psynth")){
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = unique(object$estimation[["n2"]]) - length(all.vars(object$input$formula)[-1])))
# if non-synthetic-estimation was applied, the df's are calculated as n2G - 1:
} else {
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = object$estimation$n2G - 1))
}
#-----------#
# calculating the lower and upper confidence intervals based on the external variance:
ci_lower_g<- object$estimation$estimate - (t.val * sqrt(object$estimation$g_variance))
ci_upper_g<- object$estimation$estimate + (t.val * sqrt(object$estimation$g_variance))
# calculating the lower and upper confidence intervals based on the design-based variance:
ci_lower_ext<- object$estimation$estimate - (t.val * sqrt(object$estimation$ext_variance))
ci_upper_ext<- object$estimation$estimate + (t.val * sqrt(object$estimation$ext_variance))
ci<- list(ci=data.frame(area=object$estimation$area, estimate=object$estimation$estimate, ci_lower_ext, ci_upper_ext, ci_lower_g, ci_upper_g),
level=orig.level, adjust.method=adjust.method)
class(ci)<- c("confint.smallarea", "twophase")
return(ci)
} # end of smallarea-CI
# -------------------------------------------#
# confidence-intervals for twophase-global:
if(is(object, "global") & inherits(object, "twophase")){ # if class(object) is c("global", "twophase")
if(adjust.method != "none"){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
}
n2<- ifelse(object$input$cluster, object$samplesizes$n2_clust, object$samplesizes$n2)
t.val<- qt(p = 1-( (1-level)/2 ), df = n2 - length(all.vars(object$input$formula)[-1]))
ci_lower_g<- object$estimation[["estimate"]] - (t.val * sqrt(object$estimation[["g_variance"]]))
ci_upper_g<- object$estimation[["estimate"]] + (t.val * sqrt(object$estimation[["g_variance"]]))
ci_lower_ext<- object$estimation[["estimate"]] - (t.val * sqrt(object$estimation[["ext_variance"]]))
ci_upper_ext<- object$estimation[["estimate"]] + (t.val * sqrt(object$estimation[["ext_variance"]]))
ci<- list(ci=data.frame(estimate=object$estimation[["estimate"]], ci_lower_ext, ci_upper_ext, ci_lower_g, ci_upper_g),
level=orig.level, adjust.method=adjust.method)
class(ci)<- c("confint.global", "twophase")
return(ci)
}# end of global-CI
} ## end of confint.twophase
# -----------------------------------------------------------------------------#
# -----------------------------------------------------------------------------#
#' @rdname confint
#' @export
confint.threephase<- function(object, parm, level = 0.95, adjust.method="none", ...){
# confint-method for for threephase estimations:
orig.level<- level
# --------------------------------#
# confint for threephase-smallarea:
if(is(object, "smallarea") & inherits(object, "threephase")){ # if class(object) is c("smallarea", "threephase")
#-----------#
# adjust confidence level if required:
if(adjust.method %in% c("none", "bonferroni")){
# bonferroni-correction:
if (adjust.method == "bonferroni"){
if(nrow(object$estimation)==1){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
} else {
level<- 1 - ((1 - level) / nrow(object$estimation))
}
}
} else {
stop("invalid argument for 'adjust.method'")
}
#-----------#
# calulating the quantile of the t-distribution:
# if synthetic-estimation was applied, the df's are calculated as n2 - # parameters of regression model:
if(object$input$method %in% c("synth", "psynth")){
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = unique(object$estimation[["n2"]]) - length(all.vars(object$input$formula.s1)[-1])))
# if non-synthetic-estimation was applied, the df's are calculated as n2G - 1:
} else {
t.val<- suppressWarnings(qt(p = 1-( (1-level)/2 ), df = object$estimation$n2G - 1))
}
#-----------#
# calculating the lower and upper confidence intervals based on the external variance:
ci_lower_g<- object$estimation$estimate - (t.val * sqrt(object$estimation$g_variance))
ci_upper_g<- object$estimation$estimate + (t.val * sqrt(object$estimation$g_variance))
# calculating the lower and upper confidence intervals based on the design-based variance:
ci_lower_ext<- object$estimation$estimate - (t.val * sqrt(object$estimation$ext_variance))
ci_upper_ext<- object$estimation$estimate + (t.val * sqrt(object$estimation$ext_variance))
ci<- list(ci=data.frame(area=object$estimation$area, estimate=object$estimation$estimate, ci_lower_ext, ci_upper_ext, ci_lower_g, ci_upper_g),
level=orig.level, adjust.method=adjust.method)
class(ci)<- c("confint.smallarea", "threephase")
return(ci)
} # end of smallarea-CI
# --------------------------------#
# summary for threephase-global:
if(is(object, "global") & inherits(object, "threephase")){ # if class(object) is c("global", "threephase")
if(adjust.method != "none"){
stop("'bonferroni-correction only reasonable for objects that contain a set of estimates'")
}
n2<- ifelse(object$input$cluster, object$samplesizes$n2_clust, object$samplesizes$n2)
t.val<- qt(p = 1-( (1-level)/2 ), df = n2 - length(all.vars(object$input$formula.s1)[-1])) # that's however not recommended by Daniel,
# see Article Crfr, Threephase, page 387
ci_lower_g<- object$estimation[["estimate"]] - (t.val * sqrt(object$estimation[["g_variance"]]))
ci_upper_g<- object$estimation[["estimate"]] + (t.val * sqrt(object$estimation[["g_variance"]]))
ci_lower_ext<- object$estimation[["estimate"]] - (t.val * sqrt(object$estimation[["ext_variance"]]))
ci_upper_ext<- object$estimation[["estimate"]] + (t.val * sqrt(object$estimation[["ext_variance"]]))
ci<- list(ci=data.frame(estimate=object$estimation[["estimate"]], ci_lower_ext, ci_upper_ext, ci_lower_g, ci_upper_g),
level=orig.level,adjust.method=adjust.method)
class(ci)<- c("confint.global", "threepase")
return(ci)
}# end of global-CI
}## end of confint.threephase
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