# Histogram of Residuals.
# Creates a histogram of the residuals from a model
plot_hist <- function(model, type, bins, theme, axis.text.size, title.text.size, title.opt){
## Creation of Values to Plot -----------------------------------------------------
# If bins = NA, use default value of 30
if(is.na(bins)){
bins <- 30
}
# Create a data frame with the residuals
if(is.na(type)){
model_values <- data.frame(Residual = helper_resid(type = NA, model = model))
} else{
model_values <- data.frame(Residual = helper_resid(type = type, model = model))
}
# # Steps to make sure any huge outliers are not cut off
# if (min(model_values$Residual) < -4 * sd(model_values$Residual)){
# min_x <- NA
# } else{
# min_x <- -4*sd(model_values$Residual)
# }
# if (max(model_values$Residual) > 4 * sd(model_values$Residual)){
# max_x <- NA
# } else{
# max_x <- 4 * sd(model_values$Residual)
# }
grid_r <- seq(-4 * sd(model_values$Residual),4 * sd(model_values$Residual), .01)
y <- dnorm(grid_r, mean = 0, sd = sd(model_values$Residual))
d_data <- data.frame(grid_r, y)
## Creation of Labels -------------------------------------------------------------
# Call function to return appropriate residual label
r_label <- helper_label(type, model)
# Create a title for the plot based on r_label
# title <- paste("Histogram of", r_label)
sd_resid <- sd(model_values$Residual)
#Call data for labels
Data <- helper_plotly_label(model)
model_values <- cbind(model_values, Data)
model_values$y <- rep(0, nrow(model_values))
model_values$Resid <- model_values$Residual
## Creation of Plot ---------------------------------------------------------------
#Create the histogram of residuals
# if (is.na(min_x) & is.na(max_x)){
model_values$Residual_Density <- model_values$Residual
plot <- ggplot(model_values, aes_string(x = "Residual")) +
geom_point(aes_string(x = "Resid", y = "y", group = "Data"), alpha = 0)+
labs(x = r_label, y = "Density")+
geom_histogram(aes_string(y = "..density..", fill = "..count.."),
color = "black", fill = "grey82", bins = bins) +
stat_function(fun = dnorm, color = "blue",
args = list(mean = 0, sd = sd_resid)) +
geom_point(data = d_data, aes_string(x = "grid_r", y = "y"), alpha = 0)
# } else{
#
# # Data is not outside of 4*sd, so xlim is used
# plot <- ggplot(model_values, aes(x = Residual)) +
# geom_histogram(aes(y = ..density.., fill = ..count..),
# color = "black", fill = "grey82", bins = bins) +
# stat_function(fun = dnorm, color = "blue",
# args = list(mean = 0, sd = sd_resid)) +
# labs(x = r_label, y = "Density") +
# xlim(c(min_x, max_x))
#
# }
# plot <- ggplot(model_values, aes(x = Residual)) +
# geom_histogram(aes(y = ..density.., fill = ..count..),
# color = "black", fill = "grey82", bins = bins) +
# geom_line(data=d_data, aes(Residual,y),color = "blue") +
# labs(x = r_label, y = "Density")
# Add theme to plot
if (theme == "bw"){
plot <- plot + theme_bw()
} else if (theme == "classic"){
plot <- plot + theme_classic()
} else if (theme == "gray" | theme == "grey"){
plot <- plot + theme_grey()
}
# Set text size of title and axis lables, determine whether to include a title,
# and return plot
if(title.opt == TRUE){
plot +
labs(title = "Histogram") +
theme(plot.title = element_text(size = title.text.size, face = "bold"),
axis.title = element_text(size = axis.text.size))
} else if (title.opt == FALSE){
plot + theme(axis.title = element_text(size = axis.text.size))
}
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.