I have a dataset that contains let’s say some mesurements for position, speed and acciliration. All come from the same “run”. I could construct a linear system and fit a polynome to all of those measurements.
But can I do the same with splines? What is an ‘R’ way of doing this?
Here is some simulated data I would like to fit:
f <- function(x) 2+x-0.5*x^2+rnorm(length(x), mean=0, sd=0.1) df <- function(x) 1-x+rnorm(length(x), mean=0, sd=0.3) ddf <- function(x) -1+rnorm(length(x), mean=0, sd=0.6) x_f <- runif(5, 0, 5) x_df <- runif(8, 3, 8) x_ddf <- runif(10, 4, 9) data <- data.frame(type=rep('f'), x=x_f, y=f(x_f)) data <- rbind(data, data.frame(type=rep('df'), x=x_df, y=df(x_df))) data <- rbind(data, data.frame(type=rep('ddf'), x=x_ddf, y=ddf(x_ddf))) library(ggplot2) ggplot(data, aes(x, y, color=type)) + geom_point() library(splines) m <- lm(data$y ~ bs(data$x, degree=6)) # but I want to fit on f, df, ddf. possible?