R/plotall.R
In LiamDBailey/climwin: Climate Window Analysis
Defines functions
plotall
Documented in
plotall
#'Visualise climate window data
#'
#'Creates a panel of plots to help visualise climate window data.
#'@param dataset A dataframe containing information on all fitted climate
#' windows. Output from \code{\link{slidingwin}}.
#'@param datasetrand A dataframe containing information on all fitted climate
#' windows using randomised data. Output from \code{\link{randwin}}.
#'@param bestmodel A model object. The strongest climate window model. Returned
#' from \code{\link{singlewin}} or \code{\link{slidingwin}}.
#'@param bestmodeldata A dataframe containing the biological and climate data
#' used to fit the strongest climate window model. Output from
#' \code{\link{singlewin}} or \code{\link{slidingwin}}.
#'@param cw1,cw2,cw3 Cumulative weight levels used to visualise model weight
#' distribution. See \code{\link{plotweights}} for more detail.
#'@param title Title of the plot panel.
#'@param arrow TRUE or FALSE. Add arrows to plots to pinpoint best window.
#'@return Will return a panel of 6-8 plots:
#'
#' \itemize{
#' \item DeltaAICc: A colour plot of model deltaAICc values (larger
#' negative values indicate stronger models). DeltaAICc is the difference
#' between AICc of each climate window model and the baseline model containing
#' no climate data.
#'
#' \item Model weight: A plot showing the distribution of cumulative
#' model weights. Gradient levels determined by parameters cw1, cw2 and cw3.
#' Darker areas have a higher chance of containing the best climate window.
#' Also returns the percentage of models within the 95% confidence set (C).
#'
#' \item Model betas: A colour plot of model beta estimates. Where applicable,
#' 2nd order coefficients (quadratic) and 3rd order coefficients (cubic) will
#' be plotted separately.
#'
#' \item Histogram(s): If datasetrand is provided, plotall will return a
#' histogram showing the deltaAICc of randomised data.
#' This can help determine the likelihood of obtaining a deltaAICc value
#' for a fitted climate window model at random. plotall will also use
#' \code{\link{pvalue}} to return values of Pc and PdeltaAICc.
#'
#' \item Boxplots: Two boxplots showing the start and end time for a
#' subset of best climate windows. Best climate windows make up the
#' cumulative model weight equivalent to the largest value of cw1, cw2 and cw3.
#' Values above boxplots represent the median values.
#'
#' \item Best Model: If bestmodel and bestmodeldata are provided, plotall will
#' create a scatterplot to show the fit of the best model through the data. }
#'
#'@author Liam D. Bailey and Martijn van de Pol
#'@examples
#'
#'# Visualise a fixed climate window generated for dataframes Mass and MassClimate
#'
#'data(MassOutput)
#'data(Mass)
#'data(MassClimate)
#'
#'single <- singlewin(xvar = list(Temp = MassClimate$Temp),
#' cdate = MassClimate$Date, bdate = Mass$Date,
#' baseline = lm(Mass ~ 1, data = Mass),
#' range = c(72, 15),
#' stat = "mean", func = "lin",
#' type = "absolute", refday = c(20, 5),
#' cmissing = FALSE, cinterval = "day")
#'
#'plotall(dataset = MassOutput, bestmodel = single$BestModel,
#' bestmodeldata = single$BestModelData,
#' cw1 = 0.95, cw2 = 0.5, cw3 = 0.25, title = "Mass")
#'
#'
#'@import gridExtra
#'@import ggplot2
#'@export
plotall <- function(dataset, datasetrand = NULL,
bestmodel = NULL, bestmodeldata = NULL,
cw1 = 0.95, cw2 = 0.5, cw3 = 0.25,
title = NULL, arrow = FALSE){
if (class(bestmodel)[length(class(bestmodel))] == "coxph" | class(bestmodel)[1] == "lme") {
bestmodel = NULL
warning("plotall cannot yet plot the best model panel for a coxph or nlme models")
}
a <- c(cw1, cw2, cw3)
b <- a[order (-a)]
cwa <- b[1]
cwb <- b[2]
cwc <- b[3]
plotenv <- environment()
if(length(levels(dataset$Custom.mod)) == 1){
plotdelta(dataset = dataset, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
plotweights(dataset = dataset, cw1 = cwa, cw2 = cwb, cw3 = cwc, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
window <- plotwin(dataset = dataset, cw = cwa)
message("Plot of model coefficients and best model output is unavailable when using custom models.")
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, window, nrow = 1, ncol = 3, top = paste(title))
} else {
plotbetas(dataset = dataset, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
plotdelta(dataset = dataset, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
plotweights(dataset = dataset, cw1 = cwa, cw2 = cwb, cw3 = cwc, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
window <- plotwin(dataset = dataset, cw = cwa)
if(!is.null(datasetrand)){
hist <- plothist(dataset = dataset, datasetrand = datasetrand)
if(is.null(bestmodel) == FALSE && is.null(bestmodeldata) == FALSE){
best <- plotbest(dataset = dataset, bestmodel = bestmodel, bestmodeldata = bestmodeldata)
if (dataset$Function[1] == "lin"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, hist, window, best, nrow = 2, ncol = 3, top = paste(title))
} else if (dataset$Function[1] == "quad"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, hist, window, best, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "cub"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, hist, window, best, plotenv$beta3, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "centre"){
if(is.null(dataset$WithinGrpDev) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, hist, window, best, nrow = 2, ncol = 4, top = paste(title))
} else if(is.null(dataset$WithinGrpMean) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgdev, hist, window, best, nrow = 2, ncol = 4, top = paste(title))
} else {
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, plotenv$wgdev, hist, window, best, nrow = 2, ncol = 4, top = paste(title))
}
} else {
gridExtra::grid.arrange(plotenv$beta, plotenv$delta, plotenv$cw, hist, window, best, nrow = 2, top = paste(title))
}
} else {
if (dataset$Function[1] == "lin"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, hist, window, nrow = 2, ncol = 3, top = paste(title))
} else if (dataset$Function[1] == "quad"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, hist, window, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "cub"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, hist, window, plotenv$beta3, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "centre"){
if(is.null(dataset$WithinGrpDev) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, hist, window, nrow = 2, ncol = 4, top = paste(title))
} else if(is.null(dataset$WithinGrpMean) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgdev, hist, window, nrow = 2, ncol = 4, top = paste(title))
} else {
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, plotenv$wgdev, hist, window, nrow = 2, ncol = 4, top = paste(title))
}
} else {
gridExtra::grid.arrange(plotenv$beta, plotenv$delta, plotenv$cw, hist, window, nrow = 2, top = paste(title))
}
}
} else {
if(!is.null(bestmodel) && !is.null(bestmodeldata)){
best <- plotbest(dataset = dataset, bestmodel = bestmodel, bestmodeldata = bestmodeldata)
if (dataset$Function[1] == "lin"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, window, best, nrow = 2, ncol = 3, top = paste(title))
} else if (dataset$Function[1] == "quad"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, window, best, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "cub"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, window, best, plotenv$beta3, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "centre"){
if(is.null(dataset$WithinGrpDev) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, window, best, nrow = 2, ncol = 4, top = paste(title))
} else if(is.null(dataset$WithinGrpMean) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgdev, window, best, nrow = 2, ncol = 4, top = paste(title))
} else {
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, plotenv$wgdev, window, best, nrow = 2, ncol = 4, top = paste(title))
}
} else {
gridExtra::grid.arrange(plotenv$beta, plotenv$delta, plotenv$cw, window, best, nrow = 2, top = paste(title))
}
} else {
if (dataset$Function[1] == "lin"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, window, nrow = 2, ncol = 3, top = paste(title))
} else if (dataset$Function[1] == "quad"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, window, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "cub"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, window, plotenv$beta3, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "centre"){
if(is.null(dataset$WithinGrpDev) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, window, nrow = 2, ncol = 4, top = paste(title))
} else if(is.null(dataset$WithinGrpMean) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgdev, window, nrow = 2, ncol = 4, top = paste(title))
} else {
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, plotenv$wgdev, window, nrow = 2, ncol = 4, top = paste(title))
}
} else {
gridExtra::grid.arrange(plotenv$beta, plotenv$delta, plotenv$cw, window, nrow = 2, top = paste(title))
}
}
}
}
}
LiamDBailey/climwin documentation built on July 8, 2022, 8:26 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions plotall
Documented in plotall
#'Visualise climate window data
#'
#'Creates a panel of plots to help visualise climate window data.
#'@param dataset A dataframe containing information on all fitted climate
#' windows. Output from \code{\link{slidingwin}}.
#'@param datasetrand A dataframe containing information on all fitted climate
#' windows using randomised data. Output from \code{\link{randwin}}.
#'@param bestmodel A model object. The strongest climate window model. Returned
#' from \code{\link{singlewin}} or \code{\link{slidingwin}}.
#'@param bestmodeldata A dataframe containing the biological and climate data
#' used to fit the strongest climate window model. Output from
#' \code{\link{singlewin}} or \code{\link{slidingwin}}.
#'@param cw1,cw2,cw3 Cumulative weight levels used to visualise model weight
#' distribution. See \code{\link{plotweights}} for more detail.
#'@param title Title of the plot panel.
#'@param arrow TRUE or FALSE. Add arrows to plots to pinpoint best window.
#'@return Will return a panel of 6-8 plots:
#'
#' \itemize{
#' \item DeltaAICc: A colour plot of model deltaAICc values (larger
#' negative values indicate stronger models). DeltaAICc is the difference
#' between AICc of each climate window model and the baseline model containing
#' no climate data.
#'
#' \item Model weight: A plot showing the distribution of cumulative
#' model weights. Gradient levels determined by parameters cw1, cw2 and cw3.
#' Darker areas have a higher chance of containing the best climate window.
#' Also returns the percentage of models within the 95% confidence set (C).
#'
#' \item Model betas: A colour plot of model beta estimates. Where applicable,
#' 2nd order coefficients (quadratic) and 3rd order coefficients (cubic) will
#' be plotted separately.
#'
#' \item Histogram(s): If datasetrand is provided, plotall will return a
#' histogram showing the deltaAICc of randomised data.
#' This can help determine the likelihood of obtaining a deltaAICc value
#' for a fitted climate window model at random. plotall will also use
#' \code{\link{pvalue}} to return values of Pc and PdeltaAICc.
#'
#' \item Boxplots: Two boxplots showing the start and end time for a
#' subset of best climate windows. Best climate windows make up the
#' cumulative model weight equivalent to the largest value of cw1, cw2 and cw3.
#' Values above boxplots represent the median values.
#'
#' \item Best Model: If bestmodel and bestmodeldata are provided, plotall will
#' create a scatterplot to show the fit of the best model through the data. }
#'
#'@author Liam D. Bailey and Martijn van de Pol
#'@examples
#'
#'# Visualise a fixed climate window generated for dataframes Mass and MassClimate
#'
#'data(MassOutput)
#'data(Mass)
#'data(MassClimate)
#'
#'single <- singlewin(xvar = list(Temp = MassClimate$Temp),
#' cdate = MassClimate$Date, bdate = Mass$Date,
#' baseline = lm(Mass ~ 1, data = Mass),
#' range = c(72, 15),
#' stat = "mean", func = "lin",
#' type = "absolute", refday = c(20, 5),
#' cmissing = FALSE, cinterval = "day")
#'
#'plotall(dataset = MassOutput, bestmodel = single$BestModel,
#' bestmodeldata = single$BestModelData,
#' cw1 = 0.95, cw2 = 0.5, cw3 = 0.25, title = "Mass")
#'
#'
#'@import gridExtra
#'@import ggplot2
#'@export
plotall <- function(dataset, datasetrand = NULL,
bestmodel = NULL, bestmodeldata = NULL,
cw1 = 0.95, cw2 = 0.5, cw3 = 0.25,
title = NULL, arrow = FALSE){
if (class(bestmodel)[length(class(bestmodel))] == "coxph" | class(bestmodel)[1] == "lme") {
bestmodel = NULL
warning("plotall cannot yet plot the best model panel for a coxph or nlme models")
}
a <- c(cw1, cw2, cw3)
b <- a[order (-a)]
cwa <- b[1]
cwb <- b[2]
cwc <- b[3]
plotenv <- environment()
if(length(levels(dataset$Custom.mod)) == 1){
plotdelta(dataset = dataset, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
plotweights(dataset = dataset, cw1 = cwa, cw2 = cwb, cw3 = cwc, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
window <- plotwin(dataset = dataset, cw = cwa)
message("Plot of model coefficients and best model output is unavailable when using custom models.")
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, window, nrow = 1, ncol = 3, top = paste(title))
} else {
plotbetas(dataset = dataset, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
plotdelta(dataset = dataset, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
plotweights(dataset = dataset, cw1 = cwa, cw2 = cwb, cw3 = cwc, arrow = arrow, plotall = TRUE, plotallenv = plotenv)
window <- plotwin(dataset = dataset, cw = cwa)
if(!is.null(datasetrand)){
hist <- plothist(dataset = dataset, datasetrand = datasetrand)
if(is.null(bestmodel) == FALSE && is.null(bestmodeldata) == FALSE){
best <- plotbest(dataset = dataset, bestmodel = bestmodel, bestmodeldata = bestmodeldata)
if (dataset$Function[1] == "lin"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, hist, window, best, nrow = 2, ncol = 3, top = paste(title))
} else if (dataset$Function[1] == "quad"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, hist, window, best, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "cub"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, hist, window, best, plotenv$beta3, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "centre"){
if(is.null(dataset$WithinGrpDev) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, hist, window, best, nrow = 2, ncol = 4, top = paste(title))
} else if(is.null(dataset$WithinGrpMean) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgdev, hist, window, best, nrow = 2, ncol = 4, top = paste(title))
} else {
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, plotenv$wgdev, hist, window, best, nrow = 2, ncol = 4, top = paste(title))
}
} else {
gridExtra::grid.arrange(plotenv$beta, plotenv$delta, plotenv$cw, hist, window, best, nrow = 2, top = paste(title))
}
} else {
if (dataset$Function[1] == "lin"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, hist, window, nrow = 2, ncol = 3, top = paste(title))
} else if (dataset$Function[1] == "quad"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, hist, window, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "cub"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, hist, window, plotenv$beta3, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "centre"){
if(is.null(dataset$WithinGrpDev) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, hist, window, nrow = 2, ncol = 4, top = paste(title))
} else if(is.null(dataset$WithinGrpMean) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgdev, hist, window, nrow = 2, ncol = 4, top = paste(title))
} else {
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, plotenv$wgdev, hist, window, nrow = 2, ncol = 4, top = paste(title))
}
} else {
gridExtra::grid.arrange(plotenv$beta, plotenv$delta, plotenv$cw, hist, window, nrow = 2, top = paste(title))
}
}
} else {
if(!is.null(bestmodel) && !is.null(bestmodeldata)){
best <- plotbest(dataset = dataset, bestmodel = bestmodel, bestmodeldata = bestmodeldata)
if (dataset$Function[1] == "lin"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, window, best, nrow = 2, ncol = 3, top = paste(title))
} else if (dataset$Function[1] == "quad"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, window, best, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "cub"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, window, best, plotenv$beta3, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "centre"){
if(is.null(dataset$WithinGrpDev) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, window, best, nrow = 2, ncol = 4, top = paste(title))
} else if(is.null(dataset$WithinGrpMean) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgdev, window, best, nrow = 2, ncol = 4, top = paste(title))
} else {
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, plotenv$wgdev, window, best, nrow = 2, ncol = 4, top = paste(title))
}
} else {
gridExtra::grid.arrange(plotenv$beta, plotenv$delta, plotenv$cw, window, best, nrow = 2, top = paste(title))
}
} else {
if (dataset$Function[1] == "lin"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, window, nrow = 2, ncol = 3, top = paste(title))
} else if (dataset$Function[1] == "quad"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, window, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "cub"){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$beta, plotenv$beta2, window, plotenv$beta3, nrow = 2, ncol = 4, top = paste(title))
} else if(dataset$Function[1] == "centre"){
if(is.null(dataset$WithinGrpDev) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, window, nrow = 2, ncol = 4, top = paste(title))
} else if(is.null(dataset$WithinGrpMean) == TRUE){
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgdev, window, nrow = 2, ncol = 4, top = paste(title))
} else {
gridExtra::grid.arrange(plotenv$delta, plotenv$cw, plotenv$wgmean, plotenv$wgdev, window, nrow = 2, ncol = 4, top = paste(title))
}
} else {
gridExtra::grid.arrange(plotenv$beta, plotenv$delta, plotenv$cw, window, nrow = 2, top = paste(title))
}
}
}
}
}
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