R/plot-interval-estimates.R

Defines functions lenDenByMethod lenDenByType lenBoxByMethod lenBoxByType sdsByMethod sdsByType sdmByMethod sdmByType sctVersusC sctVersusH boxByMethod boxByType boxByConfidence plotCoefficientInterval plotHeterogeneityInterval turn2perc

Documented in boxByConfidence boxByMethod boxByType lenBoxByMethod lenBoxByType lenDenByMethod lenDenByType plotCoefficientInterval plotHeterogeneityInterval sctVersusC sctVersusH sdmByMethod sdmByType sdsByMethod sdsByType

# Copyright (C) 2014 Thomas W. D. Möbius ([email protected])
#
#     This program is free software: you can redistribute it and/or
#     modify it under the terms of the GNU General Public License as
#     published by the Free Software Foundation, either version 3 of the
#     License, or (at your option) any later version.
#
#     This program is distributed in the hope that it will be useful,
#     but WITHOUT ANY WARRANTY; without even the implied warranty of
#     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
#     General Public License for more details.
#
#     You should have received a copy of the GNU General Public License
#     along with this program. If not, see
#     <http://www.gnu.org/licenses/>.

turn2perc <- function(confidence) {
     return(factor(  paste(confidence*100, "%")
                   , ordered=TRUE))
}

#' Plot pivots: Interval estimates of the heterogeneity
#'
#' @param cnfh interval estimates of the heterogeneity.
#' @export
plotHeterogeneityInterval <- function(cnfh) {
    cnfh$confidence <- turn2perc(cnfh$confidence)
    return(ggplot(cnfh, aes(type, 0, ymin = lower, ymax = upper))
           + geom_linerange(size = 3.42)
           + scale_y_continuous(expression(tau))
           + scale_x_discrete(name = "")
           + coord_flip()
           + facet_wrap(~confidence, ncol=1)
           + theme(axis.text = element_text(colour = "black"))
           )
}

#' Plot pivots: Interval estimates of the heterogeneity
#'
#' @param cnfr interval estimates of the heterogeneity.
#' @export
plotCoefficientInterval <- function(cnfr) {
    cnfr$confidence <- turn2perc(cnfr$confidence)
    return( ggplot(cnfr, aes(method, 0, ymin = lower, ymax = upper))
           + geom_linerange(size = 3.42)
           + scale_y_continuous(expression(beta[2]))
           + scale_x_discrete(name = "")
           + geom_hline(yintercept = 0, size = 0.3, linetype=4)
           + coord_flip()
           + facet_wrap(~confidence, ncol=1)
           + theme(axis.text = element_text(colour = "black"))
           )
}

################
### Boxplots ###
################

#' Plotting performance: Box plots for target value confidence-coverage
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
boxByConfidence <- function(res) {
    boxp <- (  ggplot(res, aes(factor(confidence, ordered=T), confidence-coverage))
             + geom_boxplot(aes(fill=type))
             + geom_hline(yintercept=0, size=.42, linetype=4)
             + coord_flip()
             + scale_fill_discrete(expression("Type of method\nused for\ninterval estimation"))
             + scale_x_discrete(expression(paste("Aspired confidence level")))
             + ylab(expression("Confidence level - Coverage probability"))
             + theme(  legend.position="bottom"
                     , axis.text = element_text(colour = "black"))
             )
   return(boxp)
}

#' Plotting performance: Box plots for target value confidence-coverage
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
boxByType <- function(res) {
    boxp <- (  ggplot(res, aes(type, confidence-coverage))
             + geom_boxplot(aes(fill=factor(confidence, ordered=T)))
             + geom_hline(yintercept=0, size=.42, linetype=4)
             + coord_flip()
             + scale_x_discrete(expression(paste("Method used for interval estimation")))
             + scale_fill_discrete(expression(paste("Aspired\nconfidence\nlevel")))
             + ylab(expression("Confidence level - Coverage probability"))
             + theme(  legend.position="bottom"
                     , axis.text = element_text(colour = "black"))
             )
   return(boxp)
}

#' Plotting performance: Box plots for target value confidence-coverage
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
boxByMethod <- function(res) {
    boxp <- (  ggplot(res, aes(type, confidence-coverage))
             + geom_boxplot(aes(fill=method))
             + geom_hline(yintercept=0, size=.42, linetype=4)
             + coord_flip()
             + scale_x_discrete(expression("Underlying type used for interval estimation"))
             + scale_fill_discrete(expression("Type of interval estimate"))
             + ylab(expression("Confidence level - Coverage probability"))
             + theme(  legend.position="bottom"
                     , axis.text = element_text(colour = "black"))
             )
   return(boxp)
}

##############################################################
### Scatter plots against h and propagated confidence level ###
##############################################################

#' Plotting performance: Scatter plot against heterogeneity
#'
#' @param res The collected interval results from a computer experiment.
#' @return A plot object.
#' @export
sctVersusH <- function(res) {
    res$colour <- turn2perc(res$confidence)
    p <- (  ggplot(res, aes(h, confidence-coverage))
          + geom_point(aes(colour=colour), alpha=.729)
          + scale_colour_discrete(expression("Aspired confidence level"))
          + geom_hline(yintercept=0, size=.42, linetype=3)
          + scale_x_continuous(expression(tau))
          + ylab(expression("Confidence level - Coverage probability"))
          + stat_smooth(method="lm", colour="black")
          + theme(  legend.position="bottom"
                  , axis.text = element_text(colour = "black"))
          )
    return(p)
}

#' Plotting performance: Scatter plot against heterogeneity
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
sctVersusC <- function(res) {
    p <- (  ggplot(res, aes(confidence, confidence-coverage))
          + geom_point(aes(colour=h), alpha=.729)
          + scale_colour_gradient(expression("True heterogeneity")
                                  , low=muted("blue"), high=muted("red")
                                  , space="Lab"
                                  )
          + geom_hline(yintercept=0, size=.42, linetype=3)
          + scale_x_continuous(expression(tau))
          + ylab(expression("Confidence level - Coverage probability"))
          + stat_smooth(method="lm", colour="black") # aes(colour=type))
          + theme(  legend.position="bottom"
                  , axis.text = element_text(colour = "black"))
          )
    return(p)
}

###################################
### Scatterplots against d_mean ###
###################################

#' Plotting performance: Scatter plot against heterogeneity
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
sdmByType <- function(res) {
    sctp <- (  ggplot(res, aes(d_mean, confidence-coverage))
             + geom_point(alpha=5/8, aes(colour=type))
             + geom_hline(yintercept=0, size=.3)
             + scale_x_continuous(expression(bar(delta)))
             + ylab(expression("Confidence level - Coverage probability"))
             + estColourPalette
             + facet_grid(confidence ~ method))
    return(sctp)
}

#' Plotting performance: Scatter plot against heterogeneity
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
sdmByMethod <- function(res) {
    sdmp <- (  ggplot(res, aes(d_mean, confidence-coverage))
             + geom_point(alpha=5/8, aes(colour=factor(confidence)))
             + geom_hline(yintercept=0, size=.3)
             + scale_x_continuous(expression(bar(delta)))
             + ylab(expression("Confidence level - Coverage probability"))
             + facet_grid(. ~ method)
             + cnfColourPalette)
    return(sdmp)
}

#################################
### Scatterplots against d_sd ###
#################################

#' Plotting performance: Scatter plot against heteroscedasticity
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
sdsByType <- function(res) {
    sctp <- (  ggplot(res, aes(d_sd, confidence-coverage))
             + geom_point(alpha=5/8, aes(colour=type))
             + geom_hline(yintercept=0, size=.3)
             + scale_x_continuous(expression(sd(delta)))
             + ylab(expression("Confidence level - Coverage probability"))
             + estColourPalette
             + facet_grid(confidence ~ method))
    return(sctp)
}

#' Plotting performance: Scatter plot against heteroscedasticity
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
sdsByMethod <- function(res) {
    sdsp <- (  ggplot(res, aes(d_sd, confidence-coverage))
             + geom_point(alpha=5/8, aes(colour=factor(confidence)))
             + geom_hline(yintercept=0, size=.3)
             + scale_x_continuous(expression(sd(delta)))
             + ylab(expression("Confidence level - Coverage probability"))
             + facet_grid(. ~ method)
             + cnfColourPalette)
    return(sdsp)
}

##########################################
### Plotting boxplot (for mean length) ###
##########################################

#' Plotting performance: Box plot of mean width
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
lenBoxByType <- function(res) {
    lenp <- (  ggplot(res, aes(factor(confidence), width))
             + geom_boxplot(aes(fill=type))
             + coord_flip()
             + scale_x_discrete(name=expression("Aspired confidence level"))
             + scale_y_continuous(name=expression("Estimated mean interval width"))
             + scale_fill_discrete(expression("Type of method\nused for\ninterval estimation"))
             + theme(  legend.position="bottom"
                     , axis.text = element_text(colour = "black"))
             )
    return(lenp)
}

#' Plotting performance: Box plot of mean width
#'
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
lenBoxByMethod <- function(res) {
    lenp <- (  ggplot(res, aes(factor(confidence), width))
             + geom_boxplot(aes(fill=method))
             + coord_flip()
             + scale_x_discrete(name=expression("Aspired confidence level"))
             + scale_y_continuous(name=expression("Estimated mean interval width"))
             + scale_fill_discrete(expression("Type of method\nused for\ninterval estimation"))
             + theme(  legend.position="bottom"
                     , axis.text = element_text(colour = "black"))
             )
    return(lenp)
}

############################################
### Plotting densities (for mean length) ###
############################################

#' Plotting performance: Density estimate of mean width
#'
#' By type.
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
lenDenByType <- function(res) {
    lenp <- (  ggplot(res, aes(x=width))
             + geom_density(aes(fill=type), alpha=.3)
             + scale_x_continuous(name=expression("Mean interval width"))
             + scale_y_continuous(name=expression("Density"))
             + scale_fill_discrete(expression(paste("Type of ", tau, "-estimator")))
             + theme(  legend.position="bottom"
                     , axis.text = element_text(colour = "black"))
             )
    return(lenp)
}

#' Plotting performance: Density estimate of mean width
#'
#' By method.
#' @param res The collected results from a computer experiment.
#' @return A plot object.
#' @export
lenDenByMethod <- function(res) {
    lenp <- (  ggplot(res, aes(x=width))
             + geom_density(aes(fill=method), alpha=.3)
             + scale_x_continuous(name=expression("Mean interval width"))
             + scale_y_continuous(name=expression("Density"))
             + scale_fill_discrete(expression(paste("Method used for\ninterval estimation")))
             + theme(  legend.position="bottom"
                     , axis.text = element_text(colour = "black"))
             )
    return(lenp)
}

Try the metagen package in your browser

Any scripts or data that you put into this service are public.

metagen documentation built on May 29, 2017, 7:13 p.m.