Simulation of Autocorrelation in Linear Regression Model

autocorrelation_simulation_visualization.R

# Install package
install.packages('ggplot2')
# Load the package
library(ggplot2)

# Without autocorrelation - intercept
df_InterceptNonAutocorrelation = long %>%
  filter(Coefficient == 'interceptNonAutocorrelation')
unique(df_InterceptNonAutocorrelation$Rho)
ggplot(data = df_InterceptNonAutocorrelation)+
  geom_boxplot(aes(x = as.factor(Rho),
                   y = Value),
               outlier.shape = NA)+
  labs(title = 'Intercept without Autocorrelation',
       caption = 'Statistics simulation')+
  xlab('Rho')+
  ylab('Coefficient')+
  theme_minimal()

# Without autocorrelation - slope
df_SlopeNonAutocorrelation = long %>%
  filter(Coefficient == 'slopeNonAutocorrelation')
unique(df_SlopeNonAutocorrelation$Rho)
ggplot(data = df_SlopeNonAutocorrelation)+
  geom_boxplot(aes(x = as.factor(Rho),
                   y = Value),
               outlier.shape = NA)+
  labs(title = 'Slope without Autocorrelation',
       caption = 'Statistics simulation')+
  xlab('Rho')+
  ylab('Coefficient')+
  theme_minimal()

# With autocorrelation - intercept
df_InterceptAutocorrelation = long %>%
  filter(Coefficient == 'interceptAutocorrelation')
unique(df_InterceptAutocorrelation$Rho)
ggplot(data = df_InterceptAutocorrelation)+
  geom_boxplot(aes(x = as.factor(Rho),
                   y = Value),
               outlier.shape = NA)+
  labs(title = 'Intercept with Autocorrelation',
       caption = 'Statistics simulation')+
  xlab('Rho')+
  ylab('Coefficient')+
  theme_minimal()

# With autocorrelation - slope
df_SlopeNonAutocorrelation = long %>%
  filter(Coefficient == 'slopeAutocorrelation')
unique(df_SlopeNonAutocorrelation$Rho)
ggplot(data = df_SlopeNonAutocorrelation)+
  geom_boxplot(aes(x = as.factor(Rho),
                   y = Value),
               outlier.shape = NA)+
  labs(title = 'Slope with Autocorrelation',
       caption = 'Statistics simulation')+
  xlab('Rho')+
  ylab('Coefficient')+
  theme_minimal()