Linear interpolation in R

Question

I have a dataset of real data, for example looking like this:

# Dataset 1 with known data
known <- data.frame(
    x = c(0:6),
    y = c(0, 10, 20, 23, 41, 39         


        

Answer 1

To follow up on DWin's answer, here's how you'd get the predicted values using a linear model.

model.lm <- lm(y ~ x, data = known)

# Use predict to estimate the values for aim.
# Note that predict expects a data.frame and the col 
# names need to match
newY <- predict(model.lm, newdata = data.frame(x = aim))

#Add the predicted points to the original plot
points(aim, newY, col = "red")

And of course you can retrieve those predicted values directly:

> cbind(aim, newY)
  aim       newY
1 0.3  2.4500000
2 0.7  6.1928571
3 2.3 21.1642857
....

Answer 2

You could be looking at approx() and approxfun() ... or I suppose you could fit with lm for linear or lowess for non-parametric fits.