File:Sliding Window Error Metrics Loglog Normal Data.png

Summary[edit]

# Reproducible R code

# Load necessary libraries
library(ggplot2)
library(patchwork)

# Set seed for reproducibility
set.seed(123)

# Generate data
n <- 10000
x <- sort(runif(n, min = 1, max = 100))
intercept <- 0.000001
slope <- 0.5
y_true_log <- intercept + slope * log10(x)
noise <- rnorm(n, mean = 0, sd = .1)
y_observed_log <- y_true_log + noise
y_observed <- 10^y_observed_log
y_true <- 10^y_true_log

# Create data frame
df <- data.frame(x = x, y_true = y_true, y_observed = y_observed)

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(zoo)

# Define window size
window_size <- 28

# Calculate error metrics over sliding window
df <- df %>%
  arrange(x) %>%
  mutate(MAE = rollapply(abs(y_true - y_observed), width = window_size, FUN = mean, align = "right", fill = NA),
         RMSE = sqrt(rollapply((y_true - y_observed)^2, width = window_size, FUN = mean, align = "right", fill = NA)),
         MALE = rollapply(abs(log10(y_true) - log10(y_observed)), width = window_size, FUN = mean, align = "right", fill = NA))

# Load necessary library
library(tidyr)

# Reshape data to long format
df_long <- df %>%
  gather(key = "error_type", value = "error_value", MAE, RMSE, MALE)

options(
  repr.plot.width  = 12,   # in inches (default = 7)
  repr.plot.height = 10   # in inches (default = 7)
)

# Plot error metrics
ggplot(df_long, aes(x = x, y = error_value, color = error_type)) +
  geom_line(alpha = .5) +
  geom_smooth() +
#   scale_x_log10() +
#   scale_y_log10() + 
  coord_cartesian(ylim = c(0, 2.5)) + # Set the limits of the plot without excluding obs
  theme_bw() + theme(text = element_text(size = 25)) +
  theme(legend.position="bottom") +

  labs(x = 'X', y = 'Error', title = 'Sliding Window Analysis of Error Metrics\nin Loglog Normal Data')

Licensing[edit]

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• to remix – to adapt the work

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https://creativecommons.org/licenses/by/4.0CC BY 4.0 Creative Commons Attribution 4.0 truetrue