R/psketto.R
In SBGalvin/psketti: Generating Investigatory Plots and Tables for Rasch Analysis
Defines functions
psketto
Documented in
psketto
#' @title Plot a Single ICC
#'
#' @name psketto
#'
#' @description psketto is singlular of psketti; (spaghetti <--> spaghetto).
#' This function plots the data for a single item from the output of
#' `pskettify()`. `psketto()` is also used in `psketti()` to create
#' multiple ICC plots. Based on Asril and Marais (2011).
#'
#' @param pskettified_data input data, generated using `pskettify()`.
#' @param item character name of the item to be plotted
#' @param style a character string for plotting style options are present for
#' coloured, or print for black and white. Defaults to "present".
#' @param item.label a character string of the item name to use.
#' @param IRFLocation logical, plots reference lines for Rasch IRF location.
#' Defaults to `TRUE`
#' @param empCI logical, plots confidence intervals for empirical points,
#' calculated using `pskettify()`. Defaults to `TRUE`
#' @param empICC logical, plots empirical ICC for item. Defaults to `TRUE`
#' @param empPoints logical, plots empirical points for based on class
#' intervals/score groups generated with `pskettify()`. Defaults to `TRUE`
#' @param facet_curves logical, should the plot be faceted by category curve?
#' Applies only to polytomous Rasch models. Defaults to `FALSE`,.
#'
#' @return psketto plot.
#'
#' @importFrom ggplot2 ggplot aes geom_segment geom_point geom_line scale_colour_manual scale_colour_grey facet_wrap theme_minimal theme ylab xlab ggtitle element_blank
#' @importFrom viridis scale_color_viridis viridis
#' @export
#'
#' @examples
#' library(eRm)
#' library(psketti)
#'
#' data("FakeData") # load data
#' # restructure fake data
#' Fake_Data_scores <- reshape(FakeData[, c("ID", "Item", "X")],
#' timevar = "Item",
#' idvar = "ID",
#' direction = "wide")
#' # for eRm col names and row names
#' names(Fake_Data_scores) <- c("ID",
#' paste0("i",
#' sprintf(fmt = "%02d", 1:23)))
#'
#' row.names(Fake_Data_scores) <- Fake_Data_scores$ID
#' Fake_Data_scores$ID <- NULL
#'
#' fake_rm <- RM(Fake_Data_scores) # Estimate Rasch model
#'
#' psk_data <- pskettify(fake_rm) # pskettify data
#'
#' # plot IRF in default colours
#' psk_1_present <- psketto(psk_data,
#' style = "present",
#' item = "i01",
#' item.label = "i01")
#' psk_1_present # plot output
#'
#' # plot IRF in default greyscale colours
#' psk_1_print <- psketto(psk_data,
#' style = "print",
#' item = "i01",
#' item.label = "i01")
#' psk_1_print # plot output
psketto <- function(pskettified_data, item, item.label, style = "present",
IRFLocation = TRUE, empCI = TRUE, empICC = TRUE, empPoints = TRUE, facet_curves = FALSE){
K <- Prop <- NULL # prevent error message
# set internal variables to NULL
Theta <- Probs <- Item <- Beta <- rel.freq <- lci <- uci <- NULL
# Set warning message about class pskettified !!!
if ("RM" %in% class(pskettified_data)){
# Set safety options so no confusion occurs, mention in doc
# DRM
# Data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
x <- pskettified_data
# warning messages for function
if(!"pskettified" %in% class(x)) stop("Object is not of class pskettified: Please ensure your input object is pskettified with psketti::pskettify()")
# Setup colours and labels for plot!!!
# set stuff for reference lines first
p_types <- c("Reference")
psk_colours_1 <- "grey40"
psk_colours_2 <- "grey40"
p_labels <- c("Rasch IRF")
if(empICC == TRUE){
p_types <- c(p_types, "EmpICC")
psk_colours_1 <- c(psk_colours_1, "steelblue")
psk_colours_2 <- c(psk_colours_2, "#020202")
p_labels <- c(p_labels, "Empirical ICC")
}else{
p_types <- p_types
psk_colours_1 <- psk_colours_1
psk_colours_2 <- psk_colours_2
p_labels <- p_labels
}
if(empCI == TRUE){
p_types <- c(p_types, "CI")
psk_colours_1 <- c(psk_colours_1, "#EE4000")
psk_colours_2 <- c(psk_colours_2, "#545454")
p_labels <- c(p_labels, "95% ci")
}else{
p_types <- p_types
psk_colours_1
psk_colours_2
p_labels <- p_labels
}
if(empPoints == TRUE){
p_types <- c(p_types, "EmpPoints")
psk_colours_1 <- c(psk_colours_1, "#AB1128")
psk_colours_2 <- c(psk_colours_2, "#303030")
p_labels <- c(p_labels, "Class Interval Proportions")
}else{
p_types <- p_types
psk_colours_1
psk_colours_2
p_labels <- p_labels
}
# labels and colours for plots
p_types <- factor(p_types, ordered = T, levels = p_types)
names(psk_colours_1) <- levels(p_types)
names(psk_colours_2) <- levels(p_types)
names(p_labels) <- levels(p_types)
# order vectors
psk_colours_1 <- psk_colours_1[order(match(psk_colours_1,levels(p_types)))]
psk_colours_2 <- psk_colours_2[order(match(psk_colours_2,levels(p_types)))]
p_labels <- p_labels[order(match(p_labels, levels(p_types)))]
# Data subset by item
tmp1 <- x$emp_ICC[x$emp_ICC$Item == item,]
tmp2 <- x$presp[x$presp$Item == item,]
tmp3 <- x$ItemDF[x$ItemDF$Item == item,]
# Item Difficulty/ Location
Beta_tmp <- round(unique(tmp3$Beta),2)
# Item label
if (nchar(item.label) == 0){
I_label <- ifelse(
grepl(x = item, "Item") | grepl(x = item, "item"),
item,
paste0("Item ", item)
)
} else {
I_label <- item.label
}
# Plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# base plot: Rasch ICC and Location reference lines
plt <- ggplot(data = tmp2, aes(x = Theta, y = Probs, group = Item))
if(IRFLocation == TRUE){
plt <- plt +
# Reference lines for P = .5 and item location (Beta)
## Pr =0.5 reference line
geom_segment(data = tmp3,
aes(y = .5, yend = .5, x = -Inf, xend = Beta, group = Item, colour = "Reference"),
linetype = "solid")+
## Item Difficulty/ Location reference line
geom_segment(data = tmp3,
aes(y = 0, yend = .5, x = Beta, xend = Beta, group = Item, colour = "Reference"),
linetype = "solid")+
# Theoretical Rasch ICC
geom_line(aes(colour = "Reference"))
} else{
plt <- plt +
# Theoretical Rasch ICC
geom_line(aes(x = Theta, y = Probs, group = Item, colour = "Reference"))
}
# add empirical ICC
if ( empICC == TRUE){
plt <- plt + ## Empirical ICC for data
geom_line(data = tmp1, aes(x = Theta, y = rel.freq, colour = "EmpICC"), linetype = 'dashed')
} else{
plt <- plt
}
# add empirical points
if (empPoints == TRUE){
plt <- plt +
# Theta and naive probs averages per score group
geom_point(data = tmp1, aes(x = Theta, y = rel.freq, group = Item, colour = 'EmpPoints'))
} else{
plt <- plt
}
# add empirical CI
if (empCI == TRUE){
plt <- plt+
## Lower Ci for score point
geom_segment(data = tmp1,
aes(y = rel.freq, yend = lci, x = Theta, xend = Theta, group = Item, colour = "CI"),
linetype = "dotted")+
## Upper Ci for score point
geom_segment(data = tmp1,
aes(y = rel.freq, yend = uci, x = Theta, xend = Theta, group = Item, colour = "CI"),
linetype = "dotted")
} else{
plt <- plt
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# add theme and colour depending on style
if (style == "present"){
plt <- plt +
# Manual legend
scale_color_manual(name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "),
values = psk_colours_1,
guide = "legend",
labels = p_labels)+
# Theme settings
theme_minimal() +
theme(legend.position = "bottom", # legend bottom-right
legend.direction = "horizontal",
legend.background = element_blank())+
xlab(expression("Latent Dimension" ~theta))+
ylab(expression(Pr(X[ij]~"="~1~"|"~theta)))
}else if(style == "print"){
plt <- plt +
# Manual legend
scale_colour_manual(name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "),
values = psk_colours_2,
guide = "legend",
labels = p_labels)+
# Theme settings
theme_minimal() +
theme(legend.position = "bottom", # legend bottom-right
legend.direction = "horizontal",
legend.background = element_blank())+
xlab(expression("Latent Dimension" ~theta))+
ylab(expression(Pr(X[ij]~"="~1~"|"~theta)))
}
} else if("PCM" %in% class(pskettified_data)){
# PCM MODEL ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
x <- pskettified_data
# warning messages for function
if(!"pskettified" %in% class(x)) stop("Object is not of class pskettified: Please ensure your input object is pskettified with psketti::pskettify()")
# Data subset by item ~~~~~~~~~~~~~
tmp1 <- x$emp_ICC[x$emp_ICC$Item == item,]
tmp2 <- x$presp[x$presp$Item == item,]
tmp3 <- x$ItemDF[x$ItemDF$Item == item,]
# Item Difficulty/ Location ~~~~~~~
Beta_tmp <- round(unique(tmp3$Beta),2)
# set colours for plot ~~~~~~~~~~
K_unique <- length(unique(tmp1$K))
K_cols <- viridis(K_unique)
# Replace viridis light yellow: "#fde725ff"
# "#FF4500" a red orange colour
# "#F7B900" a gold colour
K_cols[K_unique] <- "#F7B900"
# Item label ~~~~~~~~~~~~~~~~~~~~
if (nchar(item.label) == 0){
I_label <- ifelse(
grepl(x = item, "Item") | grepl(x = item, "item"),
item,
paste0("Item ", item)
)
} else {
I_label <- item.label
}
# Plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Intentionally a simpler plot
plt <- ggplot(data = tmp2,
aes(x = Theta, y = Probs, colour = K))+
geom_line(aes(alpha = .5), show.legend = FALSE)+ # addded alpha
xlab(expression("Latent Dimension" ~theta))+
ylab(expression(Pr(X[ni]~"="~x~"|"~theta)))
if (empICC == TRUE){
# Empirical ICC
plt <- plt +
geom_line(data = tmp1,
aes(x = Theta, y = Prop, colour = K))
} else if(empICC == FALSE){
plt <- plt
}
if (empPoints == TRUE){
# empirical points
plt <- plt +
geom_point(data = tmp1,
aes(x = Theta,
y = Prop,
colour = K))#
#shape = 21, # Dropped shape aes; using default
#fill = 'white') # Dropped fill aes
} else if (empPoints == FALSE){
plt <- plt
}
if (style == "present"){
# colour style viridis
plt <- plt +
scale_color_manual(values = K_cols, name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "))+
#scale_color_viridis(discrete = T, name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "))+
theme_minimal()+
theme(legend.position = "bottom")
} else if(style == "print"){
# colour style black and white
plt <- plt +
scale_colour_grey(name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "))+
theme_minimal()+
theme(legend.position = "bottom")
}
if (facet_curves == TRUE){
# Facet the plot
# this will be useful for Confidence intervals
plt <- plt + facet_wrap(~K)
}else if(facet_curves == FALSE){
plt<-plt
}
}
return(plt)
}
SBGalvin/psketti documentation built on March 13, 2021, 1:47 p.m.
R Package Documentation
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Defines functions psketto
Documented in psketto
#' @title Plot a Single ICC
#'
#' @name psketto
#'
#' @description psketto is singlular of psketti; (spaghetti <--> spaghetto).
#' This function plots the data for a single item from the output of
#' `pskettify()`. `psketto()` is also used in `psketti()` to create
#' multiple ICC plots. Based on Asril and Marais (2011).
#'
#' @param pskettified_data input data, generated using `pskettify()`.
#' @param item character name of the item to be plotted
#' @param style a character string for plotting style options are present for
#' coloured, or print for black and white. Defaults to "present".
#' @param item.label a character string of the item name to use.
#' @param IRFLocation logical, plots reference lines for Rasch IRF location.
#' Defaults to `TRUE`
#' @param empCI logical, plots confidence intervals for empirical points,
#' calculated using `pskettify()`. Defaults to `TRUE`
#' @param empICC logical, plots empirical ICC for item. Defaults to `TRUE`
#' @param empPoints logical, plots empirical points for based on class
#' intervals/score groups generated with `pskettify()`. Defaults to `TRUE`
#' @param facet_curves logical, should the plot be faceted by category curve?
#' Applies only to polytomous Rasch models. Defaults to `FALSE`,.
#'
#' @return psketto plot.
#'
#' @importFrom ggplot2 ggplot aes geom_segment geom_point geom_line scale_colour_manual scale_colour_grey facet_wrap theme_minimal theme ylab xlab ggtitle element_blank
#' @importFrom viridis scale_color_viridis viridis
#' @export
#'
#' @examples
#' library(eRm)
#' library(psketti)
#'
#' data("FakeData") # load data
#' # restructure fake data
#' Fake_Data_scores <- reshape(FakeData[, c("ID", "Item", "X")],
#' timevar = "Item",
#' idvar = "ID",
#' direction = "wide")
#' # for eRm col names and row names
#' names(Fake_Data_scores) <- c("ID",
#' paste0("i",
#' sprintf(fmt = "%02d", 1:23)))
#'
#' row.names(Fake_Data_scores) <- Fake_Data_scores$ID
#' Fake_Data_scores$ID <- NULL
#'
#' fake_rm <- RM(Fake_Data_scores) # Estimate Rasch model
#'
#' psk_data <- pskettify(fake_rm) # pskettify data
#'
#' # plot IRF in default colours
#' psk_1_present <- psketto(psk_data,
#' style = "present",
#' item = "i01",
#' item.label = "i01")
#' psk_1_present # plot output
#'
#' # plot IRF in default greyscale colours
#' psk_1_print <- psketto(psk_data,
#' style = "print",
#' item = "i01",
#' item.label = "i01")
#' psk_1_print # plot output
psketto <- function(pskettified_data, item, item.label, style = "present",
IRFLocation = TRUE, empCI = TRUE, empICC = TRUE, empPoints = TRUE, facet_curves = FALSE){
K <- Prop <- NULL # prevent error message
# set internal variables to NULL
Theta <- Probs <- Item <- Beta <- rel.freq <- lci <- uci <- NULL
# Set warning message about class pskettified !!!
if ("RM" %in% class(pskettified_data)){
# Set safety options so no confusion occurs, mention in doc
# DRM
# Data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
x <- pskettified_data
# warning messages for function
if(!"pskettified" %in% class(x)) stop("Object is not of class pskettified: Please ensure your input object is pskettified with psketti::pskettify()")
# Setup colours and labels for plot!!!
# set stuff for reference lines first
p_types <- c("Reference")
psk_colours_1 <- "grey40"
psk_colours_2 <- "grey40"
p_labels <- c("Rasch IRF")
if(empICC == TRUE){
p_types <- c(p_types, "EmpICC")
psk_colours_1 <- c(psk_colours_1, "steelblue")
psk_colours_2 <- c(psk_colours_2, "#020202")
p_labels <- c(p_labels, "Empirical ICC")
}else{
p_types <- p_types
psk_colours_1 <- psk_colours_1
psk_colours_2 <- psk_colours_2
p_labels <- p_labels
}
if(empCI == TRUE){
p_types <- c(p_types, "CI")
psk_colours_1 <- c(psk_colours_1, "#EE4000")
psk_colours_2 <- c(psk_colours_2, "#545454")
p_labels <- c(p_labels, "95% ci")
}else{
p_types <- p_types
psk_colours_1
psk_colours_2
p_labels <- p_labels
}
if(empPoints == TRUE){
p_types <- c(p_types, "EmpPoints")
psk_colours_1 <- c(psk_colours_1, "#AB1128")
psk_colours_2 <- c(psk_colours_2, "#303030")
p_labels <- c(p_labels, "Class Interval Proportions")
}else{
p_types <- p_types
psk_colours_1
psk_colours_2
p_labels <- p_labels
}
# labels and colours for plots
p_types <- factor(p_types, ordered = T, levels = p_types)
names(psk_colours_1) <- levels(p_types)
names(psk_colours_2) <- levels(p_types)
names(p_labels) <- levels(p_types)
# order vectors
psk_colours_1 <- psk_colours_1[order(match(psk_colours_1,levels(p_types)))]
psk_colours_2 <- psk_colours_2[order(match(psk_colours_2,levels(p_types)))]
p_labels <- p_labels[order(match(p_labels, levels(p_types)))]
# Data subset by item
tmp1 <- x$emp_ICC[x$emp_ICC$Item == item,]
tmp2 <- x$presp[x$presp$Item == item,]
tmp3 <- x$ItemDF[x$ItemDF$Item == item,]
# Item Difficulty/ Location
Beta_tmp <- round(unique(tmp3$Beta),2)
# Item label
if (nchar(item.label) == 0){
I_label <- ifelse(
grepl(x = item, "Item") | grepl(x = item, "item"),
item,
paste0("Item ", item)
)
} else {
I_label <- item.label
}
# Plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# base plot: Rasch ICC and Location reference lines
plt <- ggplot(data = tmp2, aes(x = Theta, y = Probs, group = Item))
if(IRFLocation == TRUE){
plt <- plt +
# Reference lines for P = .5 and item location (Beta)
## Pr =0.5 reference line
geom_segment(data = tmp3,
aes(y = .5, yend = .5, x = -Inf, xend = Beta, group = Item, colour = "Reference"),
linetype = "solid")+
## Item Difficulty/ Location reference line
geom_segment(data = tmp3,
aes(y = 0, yend = .5, x = Beta, xend = Beta, group = Item, colour = "Reference"),
linetype = "solid")+
# Theoretical Rasch ICC
geom_line(aes(colour = "Reference"))
} else{
plt <- plt +
# Theoretical Rasch ICC
geom_line(aes(x = Theta, y = Probs, group = Item, colour = "Reference"))
}
# add empirical ICC
if ( empICC == TRUE){
plt <- plt + ## Empirical ICC for data
geom_line(data = tmp1, aes(x = Theta, y = rel.freq, colour = "EmpICC"), linetype = 'dashed')
} else{
plt <- plt
}
# add empirical points
if (empPoints == TRUE){
plt <- plt +
# Theta and naive probs averages per score group
geom_point(data = tmp1, aes(x = Theta, y = rel.freq, group = Item, colour = 'EmpPoints'))
} else{
plt <- plt
}
# add empirical CI
if (empCI == TRUE){
plt <- plt+
## Lower Ci for score point
geom_segment(data = tmp1,
aes(y = rel.freq, yend = lci, x = Theta, xend = Theta, group = Item, colour = "CI"),
linetype = "dotted")+
## Upper Ci for score point
geom_segment(data = tmp1,
aes(y = rel.freq, yend = uci, x = Theta, xend = Theta, group = Item, colour = "CI"),
linetype = "dotted")
} else{
plt <- plt
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# add theme and colour depending on style
if (style == "present"){
plt <- plt +
# Manual legend
scale_color_manual(name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "),
values = psk_colours_1,
guide = "legend",
labels = p_labels)+
# Theme settings
theme_minimal() +
theme(legend.position = "bottom", # legend bottom-right
legend.direction = "horizontal",
legend.background = element_blank())+
xlab(expression("Latent Dimension" ~theta))+
ylab(expression(Pr(X[ij]~"="~1~"|"~theta)))
}else if(style == "print"){
plt <- plt +
# Manual legend
scale_colour_manual(name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "),
values = psk_colours_2,
guide = "legend",
labels = p_labels)+
# Theme settings
theme_minimal() +
theme(legend.position = "bottom", # legend bottom-right
legend.direction = "horizontal",
legend.background = element_blank())+
xlab(expression("Latent Dimension" ~theta))+
ylab(expression(Pr(X[ij]~"="~1~"|"~theta)))
}
} else if("PCM" %in% class(pskettified_data)){
# PCM MODEL ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
x <- pskettified_data
# warning messages for function
if(!"pskettified" %in% class(x)) stop("Object is not of class pskettified: Please ensure your input object is pskettified with psketti::pskettify()")
# Data subset by item ~~~~~~~~~~~~~
tmp1 <- x$emp_ICC[x$emp_ICC$Item == item,]
tmp2 <- x$presp[x$presp$Item == item,]
tmp3 <- x$ItemDF[x$ItemDF$Item == item,]
# Item Difficulty/ Location ~~~~~~~
Beta_tmp <- round(unique(tmp3$Beta),2)
# set colours for plot ~~~~~~~~~~
K_unique <- length(unique(tmp1$K))
K_cols <- viridis(K_unique)
# Replace viridis light yellow: "#fde725ff"
# "#FF4500" a red orange colour
# "#F7B900" a gold colour
K_cols[K_unique] <- "#F7B900"
# Item label ~~~~~~~~~~~~~~~~~~~~
if (nchar(item.label) == 0){
I_label <- ifelse(
grepl(x = item, "Item") | grepl(x = item, "item"),
item,
paste0("Item ", item)
)
} else {
I_label <- item.label
}
# Plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Intentionally a simpler plot
plt <- ggplot(data = tmp2,
aes(x = Theta, y = Probs, colour = K))+
geom_line(aes(alpha = .5), show.legend = FALSE)+ # addded alpha
xlab(expression("Latent Dimension" ~theta))+
ylab(expression(Pr(X[ni]~"="~x~"|"~theta)))
if (empICC == TRUE){
# Empirical ICC
plt <- plt +
geom_line(data = tmp1,
aes(x = Theta, y = Prop, colour = K))
} else if(empICC == FALSE){
plt <- plt
}
if (empPoints == TRUE){
# empirical points
plt <- plt +
geom_point(data = tmp1,
aes(x = Theta,
y = Prop,
colour = K))#
#shape = 21, # Dropped shape aes; using default
#fill = 'white') # Dropped fill aes
} else if (empPoints == FALSE){
plt <- plt
}
if (style == "present"){
# colour style viridis
plt <- plt +
scale_color_manual(values = K_cols, name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "))+
#scale_color_viridis(discrete = T, name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "))+
theme_minimal()+
theme(legend.position = "bottom")
} else if(style == "print"){
# colour style black and white
plt <- plt +
scale_colour_grey(name = bquote(.(I_label)~"Location "~theta~"="~.(Beta_tmp)~": "))+
theme_minimal()+
theme(legend.position = "bottom")
}
if (facet_curves == TRUE){
# Facet the plot
# this will be useful for Confidence intervals
plt <- plt + facet_wrap(~K)
}else if(facet_curves == FALSE){
plt<-plt
}
}
return(plt)
}
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