On Aug 29, 2012, at 8:06 AM, John Thaden wrote:
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?
The new data argument MUST contain a column with the name "area". If
it does not hten the original data is used.
If so, what numbers would I fill it with?
lm.predict is not set up to do the task you are requesting (as Peter
Ehlers points out.)
Or should my newdata object include the original data, too? Is there
another mailing list I can ask?
This question has a good chance of having been asked on r-help before.
Why not do a search with MarkMail (using this as the first term:
list:org.r-project.r-help) or at the Newcastle search site? Ehler's
suggestion of using the term "inverse prediction" or "calibration
curve" might be useful in that task.
You can also search the accumulated package documantation with:
installpackages("sos")
require(sos)
findFn("calibration curve")
findFn("inverse prediction")
Doing a search in StackOverflow with [r] calibration curve" also
brings up some items that look possibly helpful.
--
David.
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?
--
David Winsemius, MD
Alameda, CA, USA
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
