Hello,
Inline.
Em 29-08-2012 16:06, John Thaden escreveu:
Could it be that my newdata object needs to include a column for the
concn term even though I'm asking for concn to be predicted? If so,
what numbers would I fill it with? Or should my newdata object include
the original data, too? Is there another mailing list I can ask?
stackoverflow.com
There's an R tag.
Rui Barradas
Thanks,
-John
On Wed, Aug 29, 2012 at 9:16 AM, John Thaden <johntha...@gmail.com> wrote:
I think I may be misreading the help pages, too, but misreading how?
I agree that inverting the fitted model is simpler, but I worry that I'm
misusing ordinary least squares regression by treating my response, with its
error distribution, as a predictor with no such error. In practice, with my
real data that includes about six independent peak area measurements per
known concentration level, the diagnostic plots from plot.lm(inv.model) look
very strange and worrisome.
Certainly predict.lm(..., type = "terms") must be able to do what I need.
-John
On Wed, Aug 29, 2012 at 6:50 AM, Rui Barradas <ruipbarra...@sapo.pt> wrote:
Hello,
You seem to be misreading the help pages for lm and predict.lm, argument
'terms'.
A much simpler way of solving your problem should be to invert the fitted
model using lm():
model <- lm(area ~ concn, data) # Your original model
inv.model <- lm(concn ~ area, data = data) # Your problem's model.
# predicts from original data
pred1 <- predict(inv.model)
# predict from new data
pred2 <- predict(inv.model, newdata = new)
# Let's see it.
plot(concn ~ area, data = data)
abline(inv.model)
points(data$area, pred1, col="blue", pch="+")
points(new$area, pred2, col="red", pch=16)
Also, 'data' is a really bad variable name, it's already an R function.
Hope this helps,
Rui Barradas
Em 28-08-2012 23:30, John Thaden escreveu:
Hello all,
How do I actually use the output of predict.lm(..., type="terms") to
predict new term values from new response values?
I'm a chromatographer trying to use R (2.15.1) for one of the most
common calculations in that business:
- Given several chromatographic peak areas measured for control
samples containing a molecule at known (increasing) concentrations,
first derive a linear regression model relating the known
concentration (predictor) to the observed peak area (response)
- Then, given peak areas from new (real) samples containing
unknown amounts of the molecule, use the model to predict
concentrations of the
molecule in the unknowns.
In other words, given y = mx +b, I need to solve x' = (y'-b)/m for new
data y'
and in R, I'm trying something like this
require(stats)
data <- data.frame(area = c(4875, 8172, 18065, 34555), concn = c(25,
50, 125, 250))
new <- data.frame(area = c(8172, 10220, 11570, 24150))
model <- lm(area ~ concn, data)
pred <- predict(model, type = "terms")
#predicts from original data
pred <- predict(model, type = "terms", newdata = new)
#error
pred <- predict(model, type = "terms", newdata = new, se.fit = TRUE)
#error
pred <- predict(model, type = "terms", newdata = new, interval =
"prediction") #error
new2 <- data.frame(area = c(8172, 10220, 11570, 24150), concn = 0)
new2
pred <- predict(model, type = "terms", newdata = new2)
#wrong results
Can someone please show me what I'm doing wrong?
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