Here is an example code to plot a seven-day bandwidth local linear regression using ggplot2:
# Load necessary libraries library(ggplot2) library(dplyr) library(zoo) # Create some dummy data set.seed(123) data <- data.frame(date = seq(as.Date("2021-01-01"), as.Date("2021-01-31"), by = "day"), value = rnorm(31)) # Define the bandwidth for local linear regression bw <- 7 # Calculate the local linear regression using the zooreg function from the zoo package data_llr <- data %>% select(date, value) %>% mutate(date = as.Date(date)) %>% mutate(value_llr = zoo::rollapplyr(value, bw, function(x) lm(value ~ as.numeric(date), data = data.frame(date = x))$fitted)) # Plot the data and the local linear regression using ggplot2 ggplot(data_llr, aes(x = date)) + geom_line(aes(y = value), color = "blue") + geom_line(aes(y = value_llr), color = "red") + ggtitle("Seven-day bandwidth local linear regression") + xlab("Date") + ylab("Value")
This code assumes that the data is in a data.frame
with columns date
and value
. The bw
variable defines the bandwidth for the local linear regression. The zoo::rollapplyr
function is used to apply the local linear regression over a rolling window of width bw
. The resulting data frame data_llr
contains the original data and the calculated local linear regression values. The ggplot2
functions are used to plot the original data in blue and the local linear regression in red. The resulting plot shows how the local linear regression changes over time, highlighting trends and patterns in the data.
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Asked: 2023-01-27 11:00:00 +0000
Seen: 6 times
Last updated: Jul 23 '22
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