R/conventional.b.R
In WLenhard/cNORM_JAMOVI: Continuous Norming with cNORM for JAMOVI
# This file is a generated template, your changes will not be overwritten
conventionalClass <- if (requireNamespace('jmvcore')) R6::R6Class(
"conventionalClass",
inherit = conventionalBase,
private = list(
.manifestNorms = NA,
.manifestPerc = NA,
.predictedNorms = NA,
.predictedPerc = NA,
.rowNames = NA,
.rowMapping = NA,
.init = function() {
private$.manifestNorms <- NULL
private$.manifestPerc <- NULL
private$.predictedNorms <- NULL
private$.predictedPerc <- NULL
private$.rowNames <- NULL
private$.rowMapping <- NULL
self$results$instructions$setContent(
"<html>
<head>
</head>
<body>
<div class='instructions'>
<p>The module estimates norm scores (= location, L) for raw scores within single groups and
compiles norm tables. In order to smooth
the norm score data and to close the missings, the functional relationship between raw score
and norm score is modeled via polynomial regression up to power 5, using the cNORM package
(A. Lenhard, Lenhard & Gary, 2024; v.3.4.0).</p>
<p>Please specify the raw score variable, the ranking order, and the type of norm score scale.</p>
<p>Further information on the procedure is available via the <a href=\"https://www.psychometrica.de/cNorm_jamovi_en.html\" target=\"_blank\">cNORMj tutorial</a>.</p>
</div>
</body>
</html>")
},
.safeModelFit = function(data, terms = NULL, k = 5, weights = NULL) {
tryCatch({
data <- computePowers(data, k=k)
if (!is.null(terms)) {
if(terms <= 0 || terms > k) {
return(list(
success = FALSE,
message = paste0("Number of terms (", terms, ") is out of valid range (1 to ", k, ")."),
model = NULL
))
}
if (!is.null(weights)) {
model <- bestModel(data, terms = terms, weights = weights, plot = FALSE)
} else {
model <- bestModel(data, terms = terms, plot = FALSE)
}
} else {
if (!is.null(weights)) {
model <- bestModel(data, weights = weights, plot = FALSE)
} else {
model <- bestModel(data, plot = FALSE)
}
}
if (is.null(model)) {
return(list(
success = FALSE,
message = "Model fitting failed. Try different parameters or check your data.",
model = NULL
))
}
return(list(
success = TRUE,
message = NULL,
model = model
))
}, error = function(e) {
return(list(
success = FALSE,
message = paste0("Error fitting model: ", e$message),
model = NULL
))
})
},
.run = function() {
# Initial state - show computing message
self$results$instructions$setContent("<html><head></head><body><div class='instructions'><p>Computing results, please wait...</p></div></body></html>")
self$results$instructions$setVisible(TRUE)
if (is.null(self$options$raw)){
self$results$instructions$setContent(
"<html>
<head>
</head>
<body>
<div class='instructions'>
<p>The module estimates norm scores (= location, L) for raw scores within single groups and
compiles norm tables. It is based on the Rankit procedure and inverse normal transformation. In order to smooth
the norm score data and to close the missings, the functional relationship between raw score
and norm score is modeled via polynomial regression up to power 5, using the cNORM package
(W. Lenhard, Lenhard & Gary, 2018).</p>
<p>Please specify the raw score variable, the ranking order, the method and the type of norm score scale.</p>
<p>Further information on the procedure is available via <a href=\"https://www.psychometrica.de/cNorm_jamovi_en.html\" target=\"_blank\">Psychometrica</a>.</p>
</div>
</body>
</html>")
return()
} else {
self$results$instructions$setContent("<html/>")
self$results$instructions$setVisible(visible = FALSE)
}
regression <- self$options$k
terms <- 4
if(self$options$terms == '1')
terms <- 1
else if(self$options$terms == '2')
terms <- 2
else if(self$options$terms == '3')
terms <- 3
else if(self$options$terms == '4')
terms <- 4
else if(self$options$terms == '5')
terms <- 5
x <- jmvcore::toNumeric(self$data[[self$options$raw]])
w <- rep(1, length(x))
if(!is.null(self$options$weights)){
w <- jmvcore::toNumeric(self$data[[self$options$weights]])
}
data <- data.frame(raw = x, weights = w)
# Store original row indices before filtering
originalIndices <- rownames(self$data)
rownames(data) <- originalIndices
# Save complete data rows
dataComplete <- data[complete.cases(data),]
private$.rowNames <- rownames(dataComplete)
# Create mapping from filtered rows to original positions
private$.rowMapping <- match(private$.rowNames, originalIndices)
# Check for valid weights
if(!is.null(self$options$weights) && min(dataComplete$weights, na.rm = TRUE) < 1) {
self$results$instructions$setVisible(visible = TRUE)
self$results$instructions$setContent("<html><head></head><body><div class='instructions'><p>The weighting variable must only contain values >= 1.</p></div></body></html>")
return()
}
scale <- self$options$scale
sd <- self$options$range
minNorm <- 20
maxNorm <- 80
sd1 <- 1
m1 <- 0
scale <- self$options$scale
if(scale=="Wechsler"){
m1 <- 10
sd1 <- 3
scale <- c(10, 3)
minNorm <- 10 - (sd*3)
maxNorm <- 10 + (sd*3)
}
else if(scale=="PISA"){
m1 <- 500
sd1 <- 100
scale <- c(500, 100)
minNorm <- 500 - (sd*100)
maxNorm <- 500 + (sd*100)
}
else if(scale=="T"){
m1 <- 50
sd1 <- 10
minNorm <- 50 - (sd*10)
maxNorm <- 50 + (sd*10)
}
else if(scale=="IQ"){
m1 <- 100
sd1 <- 15
minNorm <- 100 - (sd*15)
maxNorm <- 100 + (sd*15)
}
else if(scale=="z"){
minNorm <- -sd
maxNorm <- sd
}
descend <- self$options$descend
if(!is.null(self$options$weights)){
dataComplete <- rankByGroup(dataComplete, raw=dataComplete$raw, group=FALSE, scale = scale, descend = descend, weights = dataComplete$weights)
}else{
dataComplete <- rankByGroup(dataComplete, raw=dataComplete$raw, group=FALSE, scale = scale, descend = descend)
}
tab1 <- data.frame(raw = dataComplete$raw, norm=dataComplete$normValue, percentile=dataComplete$percentile*100)
tab1 <- unique(tab1)
tab1 <- tab1[order(tab1$raw),]
tab1$type <- rep(0)
if(regression){
minRaw <- self$options$minRaw
maxRaw <- self$options$maxRaw
if(maxRaw <= minRaw){
minRaw <- NULL
maxRaw <- NULL
}
if(!is.null(minRaw))
foot <- paste0(" The range of raw scores was manually set from ", minRaw, " to ", maxRaw, ".")
else
foot <- paste0(" The range of raw scores was automatically retrieved from the dataset.")
dataComplete <- computePowers(dataComplete, k=5)
# Use safe model fitting function
modelResult <- private$.safeModelFit(
data = dataComplete,
terms = terms,
k = 5,
weights = if(!is.null(self$options$weights)) dataComplete$weights else NULL
)
if (!modelResult$success) {
self$results$instructions$setVisible(visible = TRUE)
self$results$instructions$setContent(paste0("<html><head></head><body><div class='instructions'>",
"<b>Error during model fitting:</b> ", modelResult$message,
"</div></body></html>"))
return()
}
model <- modelResult$model
tab <- rawTable(0, model, minNorm = minNorm, maxNorm = maxNorm, minRaw = minRaw, maxRaw = maxRaw, step = as.numeric(self$options$stepping))
tab$type <- rep(1)
if(is.null(model)){
self$results$instructions$setVisible(visible = TRUE)
self$results$instructions$setContent(paste0("<html><head></head><body><div class='instructions'>",
"<b>Ups! Something went wrong!</b>",
"</div></body></html>"))
return()
}else{
if(self$options$model){
self$results$modelTab$setRow(rowNo=1, values=list(Variable="Model summary", Weight="", Terms=model$ideal.model, RMSE=model$rmse, R2adj=model$subset$adjr2[model$ideal.model], BIC=model$subset$bic[model$ideal.model]))
for(i in 1:length(model$coefficients)){
self$results$modelTab$addRow(rowKey=(i+1), values=list(Variable=names(model$coefficients)[i], Weight=model$coefficients[[i]], Terms="", RMSE="", R2adj="", BIC=""))
}
}
}
}else{
tab <- tab1
foot <- "The norm score table is based on the manifest frequency distribution."
}
table <- self$results$norms
table$setRow(rowNo=1, values=list(Raw=tab$raw[[1]], Norm=tab$norm[[1]], Percentile=tab$percentile[[1]]))
for(i in 2:nrow(tab)){
if(tab$norm[[i]]<tab$norm[[i-1]]){
tab$norm[[i]] <- tab$norm[[i-1]]
tab$percentile[[i]] <- tab$percentile[[i-1]]
}
table$addRow(rowKey=i, values=list(Raw=tab$raw[[i]], Norm=tab$norm[[i]], Percentile=tab$percentile[[i]]))
}
self$results$norms$setNote("empty", foot, init=FALSE)
image <- self$results$plot
if(regression){
image$setState(rbind(tab1, tab))
}else{
image$setState(tab1)
}
private$.manifestNorms <- dataComplete$normValue
private$.manifestPerc <- dataComplete$percentile * 100
if(regression){
dataComplete$predictedNorm <- predictNorm(dataComplete$raw, 0, model, minNorm=minNorm, maxNorm=maxNorm)
dataComplete$predictedPercentile <- pnorm((dataComplete$predictedNorm-m1)/sd1) * 100
private$.predictedNorms <- dataComplete$predictedNorm
private$.predictedPerc <- dataComplete$predictedPercentile
}
private$.populateOutputs()
# Reset instructions
self$results$instructions$setVisible(FALSE)
},
.populateOutputs=function() {
if (self$options$saveManifest && self$results$saveManifest$isNotFilled()) {
self$results$saveManifest$setRowNums(private$.rowNames)
self$results$saveManifest$setValues(private$.manifestNorms)
}
if (self$options$saveManifestPerc && self$results$saveManifestPerc$isNotFilled()) {
self$results$saveManifestPerc$setRowNums(private$.rowNames)
self$results$saveManifestPerc$setValues(private$.manifestPerc)
}
if(self$options$k){
if (self$options$savePredicted && self$results$savePredicted$isNotFilled()) {
self$results$savePredicted$setRowNums(private$.rowNames)
self$results$savePredicted$setValues(private$.predictedNorms)
}
if (self$options$savePredictedPerc && self$results$savePredictedPerc$isNotFilled()) {
self$results$savePredictedPerc$setRowNums(private$.rowNames)
self$results$savePredictedPerc$setValues(private$.predictedPerc)
}
}
},
.plot=function(image, ...) { # <-- the plot function
if (is.null(self$options$raw) || is.null(image$state))
return(FALSE)
data <- image$state
plotting <- self$options$plotting == "Norm Score"
# Create the base plot with proper theme
p <- ggplot2::ggplot() +
ggplot2::theme_bw() +
ggplot2::theme(
legend.position = "top",
legend.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(hjust = 0.5)
)
# Add points based on the plotting condition
if (plotting) {
p <- p +
ggplot2::geom_point(data = data[data$type == 0, ],
ggplot2::aes(x = raw, y = norm),
color = "black", alpha = 0.7) +
ggplot2::labs(x = "Raw Score", y = "Norm Score", title = "Norm Score Conversion")
} else {
p <- p +
ggplot2::geom_point(data = data[data$type == 0, ],
ggplot2::aes(x = raw, y = percentile),
color = "black", alpha = 0.7) +
ggplot2::labs(x = "Raw Score", y = "Percentile", title = "Percentile Conversion")
}
# Add regression line if required
if (self$options$k && sum(data$type == 1) > 0) {
regressionData <- data[data$type == 1, ]
if (plotting) {
p <- p +
ggplot2::geom_line(data = regressionData,
ggplot2::aes(x = raw, y = norm, color = "Regression"),
size = 1)
} else {
p <- p +
ggplot2::geom_line(data = regressionData,
ggplot2::aes(x = raw, y = percentile, color = "Regression"),
size = 1)
}
# Add legend
p <- p +
ggplot2::scale_color_manual(values = c("Regression" = "blue"),
labels = c("Regression" = "Regression")) +
ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(linetype = 1, shape = NA)))
# Add annotation for observed data
p <- p +
ggplot2::annotate("text", x = -Inf, y = -Inf, label = "● Observed",
hjust = -0.1, vjust = -1, size = 3.5)
}
print(p)
return(TRUE)
})
)
WLenhard/cNORM_JAMOVI documentation built on July 4, 2025, 5:21 p.m.
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# This file is a generated template, your changes will not be overwritten
conventionalClass <- if (requireNamespace('jmvcore')) R6::R6Class(
"conventionalClass",
inherit = conventionalBase,
private = list(
.manifestNorms = NA,
.manifestPerc = NA,
.predictedNorms = NA,
.predictedPerc = NA,
.rowNames = NA,
.rowMapping = NA,
.init = function() {
private$.manifestNorms <- NULL
private$.manifestPerc <- NULL
private$.predictedNorms <- NULL
private$.predictedPerc <- NULL
private$.rowNames <- NULL
private$.rowMapping <- NULL
self$results$instructions$setContent(
"<html>
<head>
</head>
<body>
<div class='instructions'>
<p>The module estimates norm scores (= location, L) for raw scores within single groups and
compiles norm tables. In order to smooth
the norm score data and to close the missings, the functional relationship between raw score
and norm score is modeled via polynomial regression up to power 5, using the cNORM package
(A. Lenhard, Lenhard & Gary, 2024; v.3.4.0).</p>
<p>Please specify the raw score variable, the ranking order, and the type of norm score scale.</p>
<p>Further information on the procedure is available via the <a href=\"https://www.psychometrica.de/cNorm_jamovi_en.html\" target=\"_blank\">cNORMj tutorial</a>.</p>
</div>
</body>
</html>")
},
.safeModelFit = function(data, terms = NULL, k = 5, weights = NULL) {
tryCatch({
data <- computePowers(data, k=k)
if (!is.null(terms)) {
if(terms <= 0 || terms > k) {
return(list(
success = FALSE,
message = paste0("Number of terms (", terms, ") is out of valid range (1 to ", k, ")."),
model = NULL
))
}
if (!is.null(weights)) {
model <- bestModel(data, terms = terms, weights = weights, plot = FALSE)
} else {
model <- bestModel(data, terms = terms, plot = FALSE)
}
} else {
if (!is.null(weights)) {
model <- bestModel(data, weights = weights, plot = FALSE)
} else {
model <- bestModel(data, plot = FALSE)
}
}
if (is.null(model)) {
return(list(
success = FALSE,
message = "Model fitting failed. Try different parameters or check your data.",
model = NULL
))
}
return(list(
success = TRUE,
message = NULL,
model = model
))
}, error = function(e) {
return(list(
success = FALSE,
message = paste0("Error fitting model: ", e$message),
model = NULL
))
})
},
.run = function() {
# Initial state - show computing message
self$results$instructions$setContent("<html><head></head><body><div class='instructions'><p>Computing results, please wait...</p></div></body></html>")
self$results$instructions$setVisible(TRUE)
if (is.null(self$options$raw)){
self$results$instructions$setContent(
"<html>
<head>
</head>
<body>
<div class='instructions'>
<p>The module estimates norm scores (= location, L) for raw scores within single groups and
compiles norm tables. It is based on the Rankit procedure and inverse normal transformation. In order to smooth
the norm score data and to close the missings, the functional relationship between raw score
and norm score is modeled via polynomial regression up to power 5, using the cNORM package
(W. Lenhard, Lenhard & Gary, 2018).</p>
<p>Please specify the raw score variable, the ranking order, the method and the type of norm score scale.</p>
<p>Further information on the procedure is available via <a href=\"https://www.psychometrica.de/cNorm_jamovi_en.html\" target=\"_blank\">Psychometrica</a>.</p>
</div>
</body>
</html>")
return()
} else {
self$results$instructions$setContent("<html/>")
self$results$instructions$setVisible(visible = FALSE)
}
regression <- self$options$k
terms <- 4
if(self$options$terms == '1')
terms <- 1
else if(self$options$terms == '2')
terms <- 2
else if(self$options$terms == '3')
terms <- 3
else if(self$options$terms == '4')
terms <- 4
else if(self$options$terms == '5')
terms <- 5
x <- jmvcore::toNumeric(self$data[[self$options$raw]])
w <- rep(1, length(x))
if(!is.null(self$options$weights)){
w <- jmvcore::toNumeric(self$data[[self$options$weights]])
}
data <- data.frame(raw = x, weights = w)
# Store original row indices before filtering
originalIndices <- rownames(self$data)
rownames(data) <- originalIndices
# Save complete data rows
dataComplete <- data[complete.cases(data),]
private$.rowNames <- rownames(dataComplete)
# Create mapping from filtered rows to original positions
private$.rowMapping <- match(private$.rowNames, originalIndices)
# Check for valid weights
if(!is.null(self$options$weights) && min(dataComplete$weights, na.rm = TRUE) < 1) {
self$results$instructions$setVisible(visible = TRUE)
self$results$instructions$setContent("<html><head></head><body><div class='instructions'><p>The weighting variable must only contain values >= 1.</p></div></body></html>")
return()
}
scale <- self$options$scale
sd <- self$options$range
minNorm <- 20
maxNorm <- 80
sd1 <- 1
m1 <- 0
scale <- self$options$scale
if(scale=="Wechsler"){
m1 <- 10
sd1 <- 3
scale <- c(10, 3)
minNorm <- 10 - (sd*3)
maxNorm <- 10 + (sd*3)
}
else if(scale=="PISA"){
m1 <- 500
sd1 <- 100
scale <- c(500, 100)
minNorm <- 500 - (sd*100)
maxNorm <- 500 + (sd*100)
}
else if(scale=="T"){
m1 <- 50
sd1 <- 10
minNorm <- 50 - (sd*10)
maxNorm <- 50 + (sd*10)
}
else if(scale=="IQ"){
m1 <- 100
sd1 <- 15
minNorm <- 100 - (sd*15)
maxNorm <- 100 + (sd*15)
}
else if(scale=="z"){
minNorm <- -sd
maxNorm <- sd
}
descend <- self$options$descend
if(!is.null(self$options$weights)){
dataComplete <- rankByGroup(dataComplete, raw=dataComplete$raw, group=FALSE, scale = scale, descend = descend, weights = dataComplete$weights)
}else{
dataComplete <- rankByGroup(dataComplete, raw=dataComplete$raw, group=FALSE, scale = scale, descend = descend)
}
tab1 <- data.frame(raw = dataComplete$raw, norm=dataComplete$normValue, percentile=dataComplete$percentile*100)
tab1 <- unique(tab1)
tab1 <- tab1[order(tab1$raw),]
tab1$type <- rep(0)
if(regression){
minRaw <- self$options$minRaw
maxRaw <- self$options$maxRaw
if(maxRaw <= minRaw){
minRaw <- NULL
maxRaw <- NULL
}
if(!is.null(minRaw))
foot <- paste0(" The range of raw scores was manually set from ", minRaw, " to ", maxRaw, ".")
else
foot <- paste0(" The range of raw scores was automatically retrieved from the dataset.")
dataComplete <- computePowers(dataComplete, k=5)
# Use safe model fitting function
modelResult <- private$.safeModelFit(
data = dataComplete,
terms = terms,
k = 5,
weights = if(!is.null(self$options$weights)) dataComplete$weights else NULL
)
if (!modelResult$success) {
self$results$instructions$setVisible(visible = TRUE)
self$results$instructions$setContent(paste0("<html><head></head><body><div class='instructions'>",
"<b>Error during model fitting:</b> ", modelResult$message,
"</div></body></html>"))
return()
}
model <- modelResult$model
tab <- rawTable(0, model, minNorm = minNorm, maxNorm = maxNorm, minRaw = minRaw, maxRaw = maxRaw, step = as.numeric(self$options$stepping))
tab$type <- rep(1)
if(is.null(model)){
self$results$instructions$setVisible(visible = TRUE)
self$results$instructions$setContent(paste0("<html><head></head><body><div class='instructions'>",
"<b>Ups! Something went wrong!</b>",
"</div></body></html>"))
return()
}else{
if(self$options$model){
self$results$modelTab$setRow(rowNo=1, values=list(Variable="Model summary", Weight="", Terms=model$ideal.model, RMSE=model$rmse, R2adj=model$subset$adjr2[model$ideal.model], BIC=model$subset$bic[model$ideal.model]))
for(i in 1:length(model$coefficients)){
self$results$modelTab$addRow(rowKey=(i+1), values=list(Variable=names(model$coefficients)[i], Weight=model$coefficients[[i]], Terms="", RMSE="", R2adj="", BIC=""))
}
}
}
}else{
tab <- tab1
foot <- "The norm score table is based on the manifest frequency distribution."
}
table <- self$results$norms
table$setRow(rowNo=1, values=list(Raw=tab$raw[[1]], Norm=tab$norm[[1]], Percentile=tab$percentile[[1]]))
for(i in 2:nrow(tab)){
if(tab$norm[[i]]<tab$norm[[i-1]]){
tab$norm[[i]] <- tab$norm[[i-1]]
tab$percentile[[i]] <- tab$percentile[[i-1]]
}
table$addRow(rowKey=i, values=list(Raw=tab$raw[[i]], Norm=tab$norm[[i]], Percentile=tab$percentile[[i]]))
}
self$results$norms$setNote("empty", foot, init=FALSE)
image <- self$results$plot
if(regression){
image$setState(rbind(tab1, tab))
}else{
image$setState(tab1)
}
private$.manifestNorms <- dataComplete$normValue
private$.manifestPerc <- dataComplete$percentile * 100
if(regression){
dataComplete$predictedNorm <- predictNorm(dataComplete$raw, 0, model, minNorm=minNorm, maxNorm=maxNorm)
dataComplete$predictedPercentile <- pnorm((dataComplete$predictedNorm-m1)/sd1) * 100
private$.predictedNorms <- dataComplete$predictedNorm
private$.predictedPerc <- dataComplete$predictedPercentile
}
private$.populateOutputs()
# Reset instructions
self$results$instructions$setVisible(FALSE)
},
.populateOutputs=function() {
if (self$options$saveManifest && self$results$saveManifest$isNotFilled()) {
self$results$saveManifest$setRowNums(private$.rowNames)
self$results$saveManifest$setValues(private$.manifestNorms)
}
if (self$options$saveManifestPerc && self$results$saveManifestPerc$isNotFilled()) {
self$results$saveManifestPerc$setRowNums(private$.rowNames)
self$results$saveManifestPerc$setValues(private$.manifestPerc)
}
if(self$options$k){
if (self$options$savePredicted && self$results$savePredicted$isNotFilled()) {
self$results$savePredicted$setRowNums(private$.rowNames)
self$results$savePredicted$setValues(private$.predictedNorms)
}
if (self$options$savePredictedPerc && self$results$savePredictedPerc$isNotFilled()) {
self$results$savePredictedPerc$setRowNums(private$.rowNames)
self$results$savePredictedPerc$setValues(private$.predictedPerc)
}
}
},
.plot=function(image, ...) { # <-- the plot function
if (is.null(self$options$raw) || is.null(image$state))
return(FALSE)
data <- image$state
plotting <- self$options$plotting == "Norm Score"
# Create the base plot with proper theme
p <- ggplot2::ggplot() +
ggplot2::theme_bw() +
ggplot2::theme(
legend.position = "top",
legend.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(hjust = 0.5)
)
# Add points based on the plotting condition
if (plotting) {
p <- p +
ggplot2::geom_point(data = data[data$type == 0, ],
ggplot2::aes(x = raw, y = norm),
color = "black", alpha = 0.7) +
ggplot2::labs(x = "Raw Score", y = "Norm Score", title = "Norm Score Conversion")
} else {
p <- p +
ggplot2::geom_point(data = data[data$type == 0, ],
ggplot2::aes(x = raw, y = percentile),
color = "black", alpha = 0.7) +
ggplot2::labs(x = "Raw Score", y = "Percentile", title = "Percentile Conversion")
}
# Add regression line if required
if (self$options$k && sum(data$type == 1) > 0) {
regressionData <- data[data$type == 1, ]
if (plotting) {
p <- p +
ggplot2::geom_line(data = regressionData,
ggplot2::aes(x = raw, y = norm, color = "Regression"),
size = 1)
} else {
p <- p +
ggplot2::geom_line(data = regressionData,
ggplot2::aes(x = raw, y = percentile, color = "Regression"),
size = 1)
}
# Add legend
p <- p +
ggplot2::scale_color_manual(values = c("Regression" = "blue"),
labels = c("Regression" = "Regression")) +
ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(linetype = 1, shape = NA)))
# Add annotation for observed data
p <- p +
ggplot2::annotate("text", x = -Inf, y = -Inf, label = "● Observed",
hjust = -0.1, vjust = -1, size = 3.5)
}
print(p)
return(TRUE)
})
)
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