I have time series data that is well modeled using a sinusoidal curve. I'd like to visualize the uncertainty in the fitted model using bootstrapping.
I adapted the approach from here. I am also interested in this approach too, using nlsBoot
. I can get the first approach to run, but the resulting plot contains curves that are not continuous, but jagged.
library(dplyr)
library(broom)
library(ggplot2)
xdata <- c(-35.98, -34.74, -33.46, -32.04, -30.86, -29.64, -28.50, -27.29, -26.00,
-24.77, -23.57, -22.21, -21.19, -20.16, -18.77, -17.57, -16.47, -15.35,
-14.40, -13.09, -11.90, -10.47, -9.95,-8.90,-7.77,-6.80, -5.99,
-5.17, -4.21, -3.06, -2.29, -1.04)
ydata <- c(-4.425, -4.134, -5.145, -5.411, -6.711, -7.725, -8.087, -9.059, -10.657,
-11.734, NA, -12.803, -12.906, -12.460, -12.128, -11.667, -10.947, -10.294,
-9.185, -8.620, -8.025, -7.493, -6.713, -6.503, -6.316, -5.662, -5.734, -4.984,
-4.723, -4.753, -4.503, -4.200)
data <- data.frame(xdata,ydata)
bootnls_aug <- data %>% bootstrap(100) %>%
do(augment(nls(ydata ~ A*cos(2*pi*((xdata-x_0)/z))+M, ., start=list(A=4,M=-7,x_0=-10,z=30),.)))
ggplot(bootnls_aug, aes(xdata, ydata)) +
geom_line(aes(y=.fitted, group=replicate), alpha=.1, color="blue") +
geom_point(size=3) +
theme_bw()
ggplot output
Can anyone offer help? Why are the displayed curves not smooth? Is there a better way to implement?