[R] predict.lm(...,type="terms") question

[Rui Barradas]

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 <johnthaden at 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 <ruipbarradas at 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?
>>>>
>>>> ______________________________________________
>>>> R-help at 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.
>>>