How to predict x values from a linear model (lm)
lm, predict, r
Solution
Since this is a typical problem in chemistry (predict values from a calibration), package `chemCal` provides `inverse.predict`. However, this function is limited to "univariate model object[s] of class lm or rlm with model formula y ~ x or y ~ x - 1."
x <- c(0, 40, 80, 120, 160, 200)
y <- c(6.52, 5.10, 4.43, 3.99, 3.75, 3.60)
plot(x,y)
model <- lm(y ~ x)
abline(model)
require(chemCal)
ynew <- c(5.5, 4.5, 3.5)
xpred<-t(sapply(ynew,function(y) inverse.predict(model,y)[1:2]))
# Prediction Standard Error
#[1,] 31.43007 -38.97289
#[2,] 104.7669 -36.45131
#[3,] 178.1037 -39.69539
points(xpred[,1],ynew,col="red")
Warning: This function is quite slow and not suitable, if you need to inverse.predict a large number of values.
If I remember correctly, the neg. SEs occur because the function expects the slope to be always positive. Absolute values of SE should still be correct.
Problem
I have this data set: ``` x <- c(0, 40, 80, 120, 160, 200) y <- c(6.52, 5.10, 4.43, 3.99, 3.75, 3.60) ``` I calculated a linear model using `lm()`: ``` model <- lm(y ~ x) ``` I want know the predicted values of `x` if I have new `y` values, e.g. `ynew <- c(5.5, 4.5, 3.5)`, but if I use the `predict()` function, it calculates only new `y` values. How can I predict new `x` values if I have new `y` values?