#' Phi-plot of Phi based on its underlying drift matrix
#'
#' This function makes a Phi-plot of Phi(DeltaT) for a range of time intervals based on its underlying drift matrix. There is also an interactive web application on my website to create a Phi-plot: Phi-and-Psi-Plots and Find DeltaT (\url{https://www.uu.nl/staff/RMKuiper/Websites\%20\%2F\%20Shiny\%20apps}).
#'
#' @param DeltaT Optional. The time interval used. By default, DeltaT = 1.
#' @param Phi Matrix of size q times q of (un)standardized lagged effects. Note that the Phi (or Drift) matrix should be standardized to make a fair comparison between cross-lagged effects.
#' It also takes a fitted object from the classes "varest" (from the VAR() function in vars package) and "ctsemFit" (from the ctFit() function in the ctsem package); see example below. From such an object, the (standardized) Phi/Drift matrix is calculated/extracted.
#' @param Drift Optional (either Phi or Drift). Underling continuous-time lagged effects matrix (i.e., Drift matrix) of the discrete-time lagged effects matrix Phi(DeltaT). By default, input for Phi is used; only when Phi = NULL, Drift will be used.
#' @param Stand Optional. Indicator for whether Phi (or Drift) should be standardized (1) or not (0). In case Stand = 1, one of the following matrices should be input as well: SigmaVAR, Sigma, or Gamma (or it is subtracted from a varest or ctsemFit object). By default, Stand = 0.
#' @param SigmaVAR Optional (if Stand = 1, then either SigmaVAR, Sigma, or Gamma needed). Residual covariance matrix of the first-order discrete-time vector autoregressive (DT-VAR(1)) model.
#' @param Sigma Optional (if Stand = 1, then either SigmaVAR, Sigma, or Gamma needed). Residual covariance matrix of the first-order continuous-time (CT-VAR(1)) model, that is, the diffusion matrix.
#' @param Gamma Optional (either SigmaVAR, Sigma or Gamma). Stationary covariance matrix, that is, the contemporaneous covariance matrix of the data.
#' @param Min Optional. Minimum time interval used in the Phi-plot. By default, Min = 0.
#' @param Max Optional. Maximum time interval used in the Phi-plot. By default, Max = 10.
#' @param Step Optional. The step-size taken in the time intervals. By default, Step = 0.05. Hence, using the defaults, the Phi-plots is based on the values of Phi(DeltaT) for DeltaT = 0, 0.05, 0.10, ..., 10. Note: Especially in case of complex eigenvalues, this step size should be very small (then, the oscillating behavior can be seen best).
#' @param WhichElements Optional. Matrix of same size as Drift denoting which element/line should be plotted (1) or not (0). By default, WhichElements = NULL. Note that even though not all lines have to be plotted, the full Drift matrix is needed to determine the selected lines.
#' @param Labels Optional. Vector with (character) labels of the lines to be plotted. The length of this vector equals the number of 1s in WhichElements (or equals q*q). By default, Labels = NULL, which renders labels with Greek letter of Phi (as a function of the time-interval) together with the indices (of outcome and predictor variables).
#' @param Col Optional. Vector with color values (integers) of the lines to be plotted. The length of this vector equals the number of 1s in WhichElements (or equals q*q). By default, Col = NULL, which renders the same color for effects that belong to the same outcome variable (i.e. a row in the Drift matrix). See \url{https://www.statmethods.net/advgraphs/parameters.html} for more information about the values.
#' @param Lty Optional. Vector with line type values (integers) of the lines to be plotted. The length of this vector equals the number of 1s in WhichElements (or equals q*q). By default, Lty = NULL, which renders solid lines for the autoregressive effects and the same type of dashed line for reciprocal effects (i.e., same type for Phi_ij as for Phi_ji). See \url{https://www.statmethods.net/advgraphs/parameters.html} for more information about the values.
#' @param Title Optional. A character or a list consisting of maximum 2 character-strings or 'expression' class objects that together represent the title of the Phi-plot. By default, Title = NULL, then the following code will be used for the title: as.list(expression(Phi(Delta[t])~plot), "How do the lagged parameters vary \n as a function of the time-interval")). Note that the default 2-items list will result in 3 lines because of the use of '\n'.
#' @param MaxMinPhi Work in progress, is available in PhiPlot(). Optional. An indicator (TRUE/FALSE) whether vertical lines for the optimum (maximum or minimum) should be added to the plot (TRUE) or not (FALSE). These values are obtained by the function MaxDeltaT(). By default, MaxMinPhi = FALSE; hence, by default, no vertical are added.
#'
#' @return This function returns a Phi-plot for a range of time intervals.
#' @importFrom expm expm
#' @importFrom purrr map
#' @importFrom ggplot2 ggplot
#' @importFrom ggpubr ggarrange
#' @import dplyr
#' @export
#' @examples
#'
#' # library(CTmeta)
#'
#' ### Make Phi-plot ###
#'
#' ## Example 1 ##
#'
#' # Phi(DeltaT)
#' DeltaT <- 1
#' Phi <- myPhi[1:2,1:2]
#' # or: Drift
#' Drift <- myDrift
#'
#' # Example 1.1: unstandardized Phi #
#' #
#' # Make plot of Phi
#' ggPhiPlot(DeltaT, Phi)
#' ggPhiPlot(DeltaT, Phi, Min = 0, Max = 10, Step = 0.01) # Specifying range x-axis and precision
#' ggPhiPlot(DeltaT, Drift = Drift, Min = 0, Max = 10, Step = 0.01) # Using Drift instead of Phi
#'
#'
#' # Example 1.2: standardized Phi #
#' q <- dim(Phi)[1]
#' SigmaVAR <- diag(q) # for ease
#' ggPhiPlot(DeltaT, Phi, Stand = 1, SigmaVAR = SigmaVAR)
#'
#'
#' ## Example 2: input from fitted object of class "varest" ##
#'
#' DeltaT <- 1
#' data <- myData
#' if (!require("vars")) install.packages("vars")
#' library(vars)
#' out_VAR <- VAR(data, p = 1)
#'
#' # Example 2.1: unstandardized Phi #
#' ggPhiPlot(DeltaT, out_VAR)
#'
#' # Example 2.2: standardized Phi #
#' ggPhiPlot(DeltaT, out_VAR, Stand = 1)
#'
#'
#' ## Example 3: Change plot options ##
#' DeltaT <- 1
#' Phi <- myPhi[1:2,1:2]
#' q <- dim(Phi)[1]
#' SigmaVAR <- diag(q) # for ease
#' #
#' WhichElements <- matrix(1, ncol = q, nrow = q) # Now, all elements are 1
#' diag(WhichElements) <- 0 # Now, the autoregressive parameters are excluded by setting the diagonals to 0.
#' Lab <- c("12", "21")
#' Labels <- NULL
#' for(i in 1:length(Lab)){
#' e <- bquote(expression(Phi(Delta[t])[.(Lab[i])]))
#' Labels <- c(Labels, eval(e))
#' }
#' Col <- c(1,2)
#' Lty <- c(1,2)
#' # Standardized Phi
#' ggPhiPlot(DeltaT = 1, Phi, Stand = 1, SigmaVAR = SigmaVAR, Min = 0, Max = 10, Step = 0.05, WhichElements = WhichElements, Labels = Labels, Col = Col, Lty = Lty)
#'
ggPhiPlot <- function(DeltaT = 1, Phi = NULL, Drift = NULL, Stand = 0, SigmaVAR = NULL, Sigma = NULL, Gamma = NULL, Min = 0, Max = 10, Step = 0.05, WhichElements = NULL, Labels = NULL, Col = NULL, Lty = NULL, Title = NULL, MaxMinPhi = FALSE) {
# DeltaT = 1; Drift = NULL; Stand = 0; SigmaVAR = NULL; Sigma = NULL; Gamma = NULL; Min = 0; Max = 10; Step = 0.05; WhichElements = NULL; Labels = NULL; Col = NULL; Lty = NULL; Title = NULL; MaxMinPhi = FALSE
# library(expm); library(purrr); library(ggplot2); library(dplyr); library(ggpubr) # library(tidyverse)
# library(CTmeta); Phi <- myPhi[1:2,1:2]
# Note needed:
#@import tidyverse
#@import ggpubr
# #######################################################################################################################
#
# #if (!require("expm")) install.packages("expm")
# library(expm)
#
# #######################################################################################################################
# Checks:
if(length(DeltaT) != 1){
ErrorMessage <- (paste0("The argument DeltaT should be a scalar, that is, one number, that is, a vector with one element. Currently, DeltaT = ", DeltaT))
return(ErrorMessage)
stop(ErrorMessage)
}
if(Stand != 0 & Stand != 1){
ErrorMessage <- (paste0("The argument Stand should be a 0 or a 1, not ", Stand))
return(ErrorMessage)
stop(ErrorMessage)
}
if(length(Min) != 1){
ErrorMessage <- (paste0("The argument Min should be a scalar, that is, one number, that is, a vector with one element. Currently, Min = ", Min))
return(ErrorMessage)
stop(ErrorMessage)
}
if(length(Max) != 1){
ErrorMessage <- (paste0("The argument Max should be a scalar, that is, one number, that is, a vector with one element. Currently, Max = ", Max))
return(ErrorMessage)
stop(ErrorMessage)
}
if(length(Step) != 1){
ErrorMessage <- (paste0("The argument Step should be a scalar, that is, one number, that is, a vector with one element. Currently, Step = ", Step))
return(ErrorMessage)
stop(ErrorMessage)
}
if(!is.logical(MaxMinPhi) & MaxMinPhi != FALSE & MaxMinPhi != TRUE){
ErrorMessage <- (paste0("The argument 'MaxMinPhi' should be T(RUE) or F(ALSE); or 1 or 0; not ", MaxMinPhi))
return(ErrorMessage)
stop(ErrorMessage)
}
#
# Check on Phi
if(any(class(Phi) == "varest")){
Phi_VARest <- Acoef(Phi)[[1]]
CTMp <- CTMparam(DeltaT, Phi_VARest)
if(is.null(CTMp$ErrorMessage)){
Drift <- CTMp$Drift # Drift <- logm(Phi)/DeltaT # Phi <- expm(Drift * DeltaT)
}else{
ErrorMessage <- CTMp$ErrorMessage
return(ErrorMessage)
stop(ErrorMessage)
}
} else if(any(class(Phi) == "ctsemFit")){
Drift <- summary(Phi)$DRIFT
} else{
if(is.null(Drift)){
if(!is.null(Phi)){
CTMp <- CTMparam(DeltaT, Phi)
if(is.null(CTMp$ErrorMessage)){
Drift <- CTMp$Drift # Drift <- logm(Phi)/DeltaT # Phi <- expm(Drift * DeltaT)
}else{
ErrorMessage <- CTMp$ErrorMessage
return(ErrorMessage)
stop(ErrorMessage)
}
}else{ # is.null(Phi)
ErrorMessage <- ("Either the drift matrix Drift or the autoregressive matrix Phi should be input to the function.")
#("Note that Phi(DeltaT) = expm(Drift*DeltaT).")
return(ErrorMessage)
stop(ErrorMessage)
}
}
#
# Check on B
if(length(Drift) > 1){
Check_B_or_Phi(B=-Drift)
if(all(Re(eigen(Drift)$val) > 0)){
cat("All (the real parts of) the eigenvalues of the drift matrix Drift are positive. Therefore. I assume the input for Drift was B = -A instead of A (or -Phi instead of Phi). I will use Drift = -B = A.")
("Note that Phi(DeltaT) = expm(-B*DeltaT) = expm(A*DeltaT) = expm(Drift*DeltaT).")
catDrift = -Drift
}
if(any(Re(eigen(Drift)$val) > 0)){
#ErrorMessage <- ("The function stopped, since some of (the real parts of) the eigenvalues of the drift matrix Drift are positive.")
#return(ErrorMessage)
#stop(ErrorMessage)
cat("If the function stopped, this is because some of (the real parts of) the eigenvalues of the drift matrix Drift are positive.")
}
}
}
#
if(length(Drift) == 1){
q <- 1
}else{
q <- dim(Drift)[1]
}
#
#
if(Stand == 1){
# Check on SigmaVAR, Sigma, and Gamma
if(any(class(Phi) == "varest")){
SigmaVAR <- cov(resid(Phi))
Phi <- Phi_VARest
Gamma <- Gamma.fromVAR(Phi, SigmaVAR)
}else if(any(class(Phi) == "ctsemFit")){
Sigma <- summary(Phi)$DIFFUSION
Gamma <- Gamma.fromCTM(Drift, Sigma)
}else if(is.null(SigmaVAR) & is.null(Gamma) & is.null(Sigma)){ # All three unknown
ErrorMessage <- (paste0("The arguments SigmaVAR, Sigma, or Gamma are not found: one should be part of the input (when Stand = 1). Notably, in case of the first matrix, specify 'SigmaVAR = SigmaVAR'."))
return(ErrorMessage)
stop(ErrorMessage)
}else if(is.null(Gamma)){ # Gamma unknown, calculate Gamma from Phi & SigmaVAR or Drift & Sigma
if(!is.null(SigmaVAR)){ # SigmaVAR known, calculate Gamma from Phi & SigmaVAR
# Check on SigmaVAR
Check_SigmaVAR(SigmaVAR, q)
# Calculate Gamma
if(is.null(Phi)){
if(q == 1){
Phi <- exp(-B*DeltaT)
}else{
Phi <- expm(-B*DeltaT)
}
}
Gamma <- Gamma.fromVAR(Phi, SigmaVAR)
}else if(!is.null(Sigma)){ # Sigma known, calculate Gamma from Drift & Sigma
# Check on Sigma
Check_Sigma(Sigma, q)
# Calculate Gamma
if(is.null(Drift)){
CTMp <- CTMparam(DeltaT, Phi)
if(is.null(CTMp$ErrorMessage)){
Drift <- CTMp$Drift # Drift <- logm(Phi)/DeltaT # Phi <- expm(Drift * DeltaT)
}else{
ErrorMessage <- CTMp$ErrorMessage
return(ErrorMessage)
stop(ErrorMessage)
}
}
Gamma <- Gamma.fromCTM(Drift, Sigma)
}
}else if(!is.null(Gamma)){ # Gamma known, only check on Gamma needed
# Checks on Gamma
Check_Gamma(Gamma, q)
}
# Standardize Drift and Gamma
Sxy <- sqrt(diag(diag(Gamma)))
Gamma <- solve(Sxy) %*% Gamma %*% solve(Sxy)
Drift <- solve(Sxy) %*% Drift %*% Sxy
#Sigma_s <- solve(Sxy) %*% Sigma %*% solve(Sxy)
}
#
#
if(!is.null(WhichElements)){
# Check on WhichElements
Check_WhichElts(WhichElements, q)
nrLines <- sum(WhichElements)
} else{
WhichElements <- matrix(1, ncol = q, nrow = q)
nrLines <- q*q #<- sum(WhichElements)
}
#
if(!is.null(Labels)){
if(length(Labels) != nrLines){
ErrorMessage <- (paste0("The argument Labels should contain ", nrLines, " elements, that is, q*q or the number of 1s in WhichElements (or WhichElements is incorrectly specified); not ", length(Labels)))
return(ErrorMessage)
stop(ErrorMessage)
}
#if(any(!is.character(Labels))){ # Note: This does not suffice, since it could also be an expression
# ErrorMessage <- (paste0("The argument Labels should consist of solely characters."))
# stop(ErrorMessage)
#}
}
if(!is.null(Col)){
if(length(Col) != nrLines){
ErrorMessage <- (paste0("The argument Col should contain ", nrLines, " elements, that is, q*q or the number of 1s in WhichElements (or WhichElements is incorrectly specified); not ", length(Col)))
return(ErrorMessage)
stop(ErrorMessage)
}
if(any(Col %% 1 != 0)){
ErrorMessage <- (paste0("The argument Col should consist of solely integers."))
return(ErrorMessage)
stop(ErrorMessage)
}
}
if(!is.null(Lty)){
if(length(Lty) != nrLines){
ErrorMessage <- (paste0("The argument Lty should contain ", nrLines, " elements, that is, q*q or the number of 1s in WhichElements (or WhichElements is incorrectly specified); not ", length(Lty)))
return(ErrorMessage)
stop(ErrorMessage)
}
if(any(Lty %% 1 != 0)){
ErrorMessage <- (paste0("The argument Lty should consist of solely integers."))
return(ErrorMessage)
stop(ErrorMessage)
}
}
if(!is.null(Title)){
if(length(Title) != 1 & !is.list(Title)){
ErrorMessage <- (paste0("The argument Title should be a character or a list (containing at max 2 items)."))
return(ErrorMessage)
stop(ErrorMessage)
}
if(length(Title) > 2){
ErrorMessage <- (paste0("The list Title should at max contain 2 items. Currently, it consists of ", length(Title), " items."))
return(ErrorMessage)
stop(ErrorMessage)
}
# Option: Also check whether each element in list either a "call" or a 'character' is...
}
#def.par <- par(no.readonly = TRUE) # save default, for resetting...
#par(def.par) #- reset to default
if(is.null(Labels)){
subscripts = NULL
for(i in 1:q){
subscripts = c(subscripts, paste0(i, 1:q, sep=""))
}
legendT = NULL
for(i in 1:(q*q)){
e <- bquote(expression(Phi(Delta[t])[.(subscripts[i])]))
legendT <- c(legendT, eval(e))
}
} else{
legendT <- as.vector(Labels)
}
if(is.null(Col)){
Col <- matrix(NA, ncol = q, nrow = q)
for(i in 1:q){
Col[i, 1:q] <- i
}
Col <- as.vector(t(Col))
}
if(is.null(Lty)){
Lty <- matrix(NA, ncol = q, nrow = q)
diag(Lty) <- 1
Lty[upper.tri(Lty, diag = FALSE)] <- 2:(1+length(Lty[upper.tri(Lty, diag = FALSE)]))
Lty[lower.tri(Lty, diag = FALSE)] <- Lty[upper.tri(Lty, diag = FALSE)]
Lty <- as.vector(t(Lty))
}
if(is.null(Title)){
Title_1 <- expression(Phi(Delta[t])~plot)
Title_2 <- "How do the lagged parameters vary \n as a function of the time-interval"
}else{
if(length(Title) == 1){
if(is.list(Title)){
Title_1 <- Title[[1]]
}else{
Title_1 <- Title
}
Title_2 <- NULL
}else if(length(Title) == 2){
Title_1 <- Title[[1]]
Title_2 <- Title[[2]]
}
}
###############################################################################################
while (!is.null(dev.list())) dev.off() # to reset the graphics pars to defaults
if(any(is.complex(eigen(Drift)$val))){
complex <- TRUE
} else{
complex <- FALSE
}
DeltaTs<-seq(Min,Max,by=Step)
PhiDeltaTsDF <- map(DeltaTs, function(x) {
if(length(Drift) == 1) {exp(Drift * x)}
else {expm(Drift * x)}
}) %>%
map(function(x) data.frame(Values = as.vector(t(x)))) %>%
bind_rows %>%
bind_cols(WhichElements = rep(as.vector(WhichElements), length(DeltaTs))) %>%
filter(WhichElements == 1) %>%
bind_cols(DeltaTs = rep(DeltaTs, each = sum(WhichElements)),
Color = rep(as.character(Col), length(DeltaTs)),
LineType = rep(as.character(Lty), length(DeltaTs)),
Labels = rep(as.character(legendT), length(DeltaTs)))
Xlab <- expression(Time-interval (Delta[t]))
Ylab <- expression(Phi(Delta[t])~values)
#
phi_plot <- ggplot(PhiDeltaTsDF, aes(DeltaTs, Values, color = Labels, linetype = Labels)) +
geom_line(lwd = 0.75) +
geom_abline(intercept = 0, slope = 0, alpha = .5) +
scale_linetype_manual(name = " ", values = Lty, labels = legendT) +
scale_color_manual(name = " ", values = Col, labels = legendT) +
ylab(Ylab) +
xlab(Xlab) +
#labs(title = as.expression(Title_1),
# subtitle = as.expression(Title_2)) +
ggtitle(as.expression(Title_1), subtitle = Title_2) +
theme_classic() +
theme(plot.title = element_text(margin = margin(t = 20))) +
ylim(0,1) +
theme(
legend.key.width = unit(2, "lines"),
legend.spacing.x = unit(1.5, "lines"),
legend.text = element_text(size = 12)
) #; phi_plot
# TO DO
#Add lines for max or min of Phi
#if(MaxMinPhi == TRUE){
# MaxD <- MaxDeltaT(Phi = Phi)
# if(is.null(MaxD$ErrorMessage)){
# Max_DeltaT <- as.vector(MaxD$DeltaT_MinOrMaxPhi[WhichElements])
# Phi_MinMax <- as.vector(MaxD$MinOrMaxPhi[WhichElements])
# }else{
# ErrorMessage <- MaxD$ErrorMessage
# return(ErrorMessage)
# stop(ErrorMessage)
# }
# #
# phi_plot <- phi_plot +
# geom_vline(xintercept = Max_DeltaT, linetype=Lty, color = Col, size=0.5)
# #geom_vline(xintercept = Max_DeltaT, linetype=Lty, color = "white", size=0.5) +
# #geom_segment(aes(x = Max_DeltaT, y = 0, xend = Max_DeltaT, yend = Phi_MinMax), linetype=Lty,
# # color = Col, size=0.5)
#}
if(complex){
# Multiple solutions and add 3 plots (2 for 2 different solutions and one scatter plot)
Title_2_N <- "using an 'aliasing' matrix \n (i.e., another solution for A)"
Title_1_N2 <- expression(Phi(Delta[t])~scatter~plot~'for'~multiples~of~Delta[t])
Title_2_N2 <- expression(Note~that~'for'~multiples~of~Delta[t]~Phi(Delta[t])~is~unique)
#Title_2_N2 <- expression(paste("Note that for multiples of ", Delta[t], "\n", Phi(Delta[t]), "is unique"))
EigenDrift <- eigen(Drift)
V <- EigenDrift$vector
PhiDeltaTsDF_L <- list(NULL)
for(N in 1:2){ # Note: last plot is scatter plot
im <- complex(real = 0, imaginary = 1)
diagN <- matrix(0, ncol = q, nrow = q)
# Note: ordering eigenvalues is based on Mod(eigenvalues): so, if find one complex then next is its conjugate.
W_complex <- which(Im(EigenDrift$val) != 0)
NrComplexPairs <- length(W_complex)/2
tellerComplex = -1
for(i in 1:NrComplexPairs){
tellerComplex = tellerComplex + 2
index <- W_complex[tellerComplex]
diagN[index,index] <- 1
diagN[index+1,index+1] <- -diagN[index,index]
# Note if nr of complex pairs > 1: 'diagN' should always be x and -x within a conjugate pair, but over the complex pairs x does not have to be the same...
}
diagN <- N * diagN
A_N = Drift + (2 * base::pi * im / DeltaT) * V %*% diagN %*% solve(V)
#A_N
#print(A_N)
Drift_N <- Re(A_N)
#
PhiDeltaTsDF_N <- map(DeltaTs, function(x) {
expm(Drift_N * x)
}) %>%
map(function(x) data.frame(Values = as.vector(t(x)))) %>%
bind_rows %>%
bind_cols(WhichElements = rep(as.vector(WhichElements), length(DeltaTs))) %>%
filter(WhichElements == 1) %>%
bind_cols(DeltaTs = rep(DeltaTs, each = sum(WhichElements)),
Color = rep(as.character(Col), length(DeltaTs)),
LineType = rep(as.character(Lty), length(DeltaTs)),
Labels = rep(as.character(legendT), length(DeltaTs)))
PhiDeltaTsDF_L[[N]] <- PhiDeltaTsDF_N
#
}
# In case last plot is scatter plot
# In last plot a scatter plot, for multiples of DeltaT, from Min to Max.
Min_ <- Min + Min%%DeltaT # last part is remainder after integer division
Max_ <- Max - Max%%DeltaT # last part is remainder after integer division
DeltaTs <- seq(Min_, Max_, by=DeltaT)
#
PhiDeltaTsDF_4 <- map(DeltaTs, function(x) {
expm(Drift_N * x)
}) %>%
map(function(x) data.frame(Values = as.vector(t(x)))) %>%
bind_rows %>%
bind_cols(WhichElements = rep(as.vector(WhichElements), length(DeltaTs))) %>%
filter(WhichElements == 1) %>%
bind_cols(DeltaTs = rep(DeltaTs, each = sum(WhichElements)),
Color = rep(as.character(Col), length(DeltaTs)),
LineType = rep(as.character(Lty), length(DeltaTs)),
Labels = rep(as.character(legendT), length(DeltaTs)))
for (i in 1:2) {
p.plot <- ggplot(PhiDeltaTsDF_L[[i]], aes(DeltaTs, Values, color = Labels, linetype = Labels)) +
geom_line(lwd = 0.75) +
geom_abline(intercept = 0, slope = 0, alpha = .5) +
scale_linetype_manual(name = " ", values = Lty, labels = legendT) +
scale_color_manual(name = " ", values = Col, labels = legendT) +
ylab(Ylab) +
xlab(Xlab) +
#labs(title = as.expression(Title_1),
# subtitle = Title_2_N) +
ggtitle(as.expression(Title_1), subtitle = Title_2_N) +
theme_classic() +
theme(plot.title = element_text(margin = margin(t = 20))) +
ylim(0,1) +
theme(
legend.key.width = unit(2, "lines"),
legend.spacing.x = unit(1.5, "lines"),
legend.text = element_text(size = 12)
)
PlotName <- paste0("Plot_", i)
assign(PlotName, p.plot)
#Plot_1
#Plot_2
}
Plot_3 <- ggplot(PhiDeltaTsDF_4, aes(DeltaTs, Values, color = Labels, shape = Labels)) +
geom_point(show.legend = ) +
geom_abline(intercept = 0, slope = 0, alpha = .5) +
scale_shape_manual(name = " ", values = Lty, labels = legendT) +
scale_color_manual(name = " ", values = Col, labels = legendT) +
ylab(Ylab) +
xlab(Xlab) +
#labs(title = as.expression(Title_1_N2),
# subtitle = as.expression(Title_2_N2)) +
ggtitle(as.expression(Title_1_N2), subtitle = Title_2_N2) +
theme_classic() +
theme(plot.title = element_text(margin = margin(t = 20))) +
ylim(0,1) +
theme(
legend.key.width = unit(2, "lines"),
legend.spacing.x = unit(1, "lines"),
legend.text = element_text(size = 12)
)
# Plot_3
Plot <- phi_plot + theme(legend.position = "none")
Plot_2_ <- Plot_2 + theme(legend.position = "none")
Plot_complex <- ggarrange(plotlist = list(Plot, Plot_1, Plot_2_, Plot_3), ncol = 2, nrow = 2,
widths = c(3,4)) %>% show
final <- list(PhiPlot = phi_plot,
complex = complex,
PhiPlot_aliasing_1 = Plot_1,
PhiPlot_aliasing_2 = Plot_2,
PhiPlot_scatter = Plot_3,
PhiPlot_all = Plot_complex)
}else{ # if not complex, then only one plot
final <- list(PhiPlot = phi_plot,
complex = complex)
print(phi_plot)
}
############################################################################################################
#final <- list(.. = ...)
return(invisible(final))
}
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